Package ‘ORFik’
August 22, 2024
Type Package
Title Open Reading Frames in Genomics
Version 1.24.0
Encoding UTF-8
Description
R package for analysis of transcript and translation features through manipulation of sequence data
and NGS data like Ribo-Seq, RNA-Seq, TCP-
Seq and CAGE. It is generalized in the sense that any transcript region
can be analysed, as the name hints to it was made with investigation of ribosomal patterns over
Open Reading Frames (ORFs) as it's primary use case.
ORFik is extremely fast through use of C++, data.table and GenomicRanges.
Package allows to reassign starts of the transcripts with the use of CAGE-Seq data,
automatic shifting of RiboSeq reads, finding of Open Reading Frames for
whole genomes and much more.
biocViews ImmunoOncology, Software, Sequencing, RiboSeq, RNASeq,
FunctionalGenomics, Coverage, Alignment, DataImport
License MIT + file LICENSE
LazyData TRUE
BugReports https://github.com/Roleren/ORFik/issues
URL https://github.com/Roleren/ORFik
Depends R (>= 4.4.0), IRanges (>= 2.17.1), GenomicRanges (>= 1.35.1),
GenomicAlignments (>= 1.19.0)
Imports AnnotationDbi (>= 1.45.0), Biostrings (>= 2.51.1), biomaRt,
biomartr (>= 1.0.7), BiocFileCache, BiocGenerics (>= 0.29.1),
BiocParallel (>= 1.19.0), BSgenome, cowplot (>= 1.0.0), curl,
RCurl, data.table (>= 1.11.8), DESeq2 (>= 1.24.0), downloader,
fst (>= 0.9.2), GenomeInfoDb (>= 1.15.5), GenomicFeatures (>=
1.31.10), ggplot2 (>= 2.2.1), gridExtra (>= 2.3), httr (>=
1.3.0), jsonlite, methods (>= 3.6.0), R.utils, Rcpp (>= 1.0.0),
Rsamtools (>= 1.35.0), rtracklayer (>= 1.43.0), stats,
SummarizedExperiment (>= 1.14.0), S4Vectors (>= 0.21.3), tools,
txdbmaker, utils, XML, xml2 (>= 1.2.0), withr
1
2 Contents
RoxygenNote 7.3.1
Suggests testthat, rmarkdown, knitr, BiocStyle,
BSgenome.Hsapiens.UCSC.hg19
LinkingTo Rcpp
VignetteBuilder knitr
git_url https://git.bioconductor.org/packages/ORFik
git_branch RELEASE_3_19
git_last_commit d2ff2c0
git_last_commit_date 2024-04-30
Repository Bioconductor 3.19
Date/Publication 2024-08-21
Author Haakon Tjeldnes [aut, cre, dtc],
Kornel Labun [aut, cph],
Michal Swirski [ctb],
Katarzyna Chyzynska [ctb, dtc],
Yamila Torres Cleuren [ctb, ths],
Eivind Valen [ths, fnd]
Maintainer Haakon Tjeldnes <[email protected]>
Contents
ORFik-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
addCdsOnLeaderEnds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
addNewTSSOnLeaders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
alignmentFeatureStatistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
allFeaturesHelper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
appendZeroes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
artificial.orfs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
assignAnnotations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
assignFirstExonsStartSite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
assignLastExonsStopSite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
assignTSSByCage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
asTX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
bamVarName . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
bamVarNamePicker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
batchNames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
bedToGR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
browseSRA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
cellLineNames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
cellTypeNames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
changePointAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
checkRFP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
checkRNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
codonSumsPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Contents 3
codon_usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
codon_usage_exp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
codon_usage_plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
collapse.by.scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
collapse.fastq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
collapseDuplicatedReads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
collapseDuplicatedReads,data.table-method . . . . . . . . . . . . . . . . . . . . . . . . 38
collapseDuplicatedReads,GAlignmentPairs-method . . . . . . . . . . . . . . . . . . . . 39
collapseDuplicatedReads,GAlignments-method . . . . . . . . . . . . . . . . . . . . . . 39
collapseDuplicatedReads,GRanges-method . . . . . . . . . . . . . . . . . . . . . . . . 40
combn.pairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
computeFeatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
computeFeaturesCage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
conditionNames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
config . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
config.exper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
config.save . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
config_file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
convertLibs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
convertToOneBasedRanges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
convert_bam_to_ofst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
convert_to_bigWig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
convert_to_covRle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
convert_to_covRleList . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
convert_to_fstWig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
correlation.plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
cor_plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
cor_table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
countOverlapsW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
countTable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
countTable_regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
coverageByTranscriptC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
coverageByTranscriptW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
coverageGroupings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
coverageHeatMap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
coveragePerTiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
coverageScorings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
coverage_to_dt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
covRle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
covRle-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
covRleFromGR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
covRleList . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
covRleList-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
create.experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
defineIsoform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
defineTrailer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
DEG.analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
DEG.plot.static . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4 Contents
DEG_model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
DEG_model_results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
DEG_model_simple . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
design,experiment-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
detectRibosomeShifts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
detect_ribo_orfs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
disengagementScore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
distToCds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
distToTSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
download.ebi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
download.SRA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
download.SRA.metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
downstreamFromPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
downstreamN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
downstreamOfPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
DTEG.analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
DTEG.plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
envExp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
envExp,experiment-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
envExp<- . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
envExp<-,experiment-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
exists.ftp.dir.fast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
exists.ftp.file.fast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
experiment-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
experiment.colors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
export.bed12 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
export.bedo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
export.bedoc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
export.bigWig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
export.fstwig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
export.ofst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
export.ofst,GAlignmentPairs-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
export.ofst,GAlignments-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
export.ofst,GRanges-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
export.wiggle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
extendLeaders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
extendsTSSexons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
extendTrailers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
extract_run_id . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
f . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
f,covRle-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
filepath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
filterCage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
filterExtremePeakGenes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
filterTranscripts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
filterUORFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
fimport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
Contents 5
findFa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
findFromPath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
findLibrariesInFolder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
findMapORFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
findMaxPeaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
findNewTSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
findNGSPairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
findORFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
findORFsFasta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
findPeaksPerGene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
findUORFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
findUORFs_exp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
find_url_ebi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
find_url_ebi_safe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
firstEndPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
firstExonPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
firstStartPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
fix_malformed_gff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
flankPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
floss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
footprints.analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
fpkm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
fpkm_calc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
fractionLength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
fractionNames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
fread.bed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
gcContent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
geneToSymbol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
getGAlignments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
getGAlignmentsPairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
getGenomeAndAnnotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
getGRanges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
getGtfPathFromTxdb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
getNGenesCoverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
getWeights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
get_bioproject_candidates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
get_genome_fasta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
get_genome_gtf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
get_noncoding_rna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186
get_phix_genome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
get_silva_rRNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
groupGRangesBy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
groupings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
gSort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
hasHits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
heatMapL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
heatMapRegion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
heatMap_single . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
6 Contents
import.bedo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
import.bedoc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
import.fstwig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200
import.ofst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
importGtfFromTxdb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
inhibitorNames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
initiationScore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
insideOutsideORF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
install.fastp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
install.sratoolkit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
is.grl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
is.gr_or_grl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
is.ORF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
is.range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
isInFrame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210
isOverlapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
isPeriodic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
kozakHeatmap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
kozakSequenceScore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
kozak_IR_ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
lastExonEndPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
lastExonPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
lastExonStartPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
length,covRle-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
length,covRleList-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
lengths,covRle-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
lengths,covRleList-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
libFolder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
libFolder,experiment-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
libNames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
libraryTypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
list.experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
list.genomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
loadRegion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224
loadRegions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225
loadTranscriptType . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
loadTxdb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
longestORFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228
mainNames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
makeExonRanks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230
makeORFNames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230
makeSummarizedExperimentFromBam . . . . . . . . . . . . . . . . . . . . . . . . . . 231
makeTxdbFromGenome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
mapToGRanges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234
matchColors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
matchNaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
matchSeqStyle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236
mergeFastq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236
Contents 7
mergeLibs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
metadata.autnaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
metaWindow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
model.matrix,experiment-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
name . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242
name,experiment-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242
nrow,experiment-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243
numCodons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243
numExonsPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244
ofst_merge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244
optimizedTranscriptLengths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
optimized_txdb_path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246
optimizeReads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
orfFrameDistributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
orfID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248
ORFik.template.experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249
ORFik.template.experiment.zf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249
ORFikQC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250
orfScore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252
organism,experiment-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
outputLibs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
pasteDir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257
pcaExperiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258
percentage_to_ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
plotHelper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
pmapFromTranscriptF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260
pmapToTranscriptF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
prettyScoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263
pseudo.transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263
pSitePlot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264
QCfolder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
QCfolder,experiment-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266
QCplots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266
QCreport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
QCstats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269
QCstats.plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270
QC_count_tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270
r . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
r,covRle-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272
rankOrder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272
read.experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
readBam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274
readBigWig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276
readLengthTable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277
readWidths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277
readWig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278
reassignTSSbyCage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
reassignTxDbByCage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281
8 Contents
reduceKeepAttr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282
regionPerReadLength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284
remakeTxdbExonIds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285
remove.experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286
remove.file_ext . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287
removeMetaCols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287
removeORFsWithinCDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288
removeORFsWithSameStartAsCDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288
removeORFsWithSameStopAsCDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289
removeORFsWithStartInsideCDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289
removeTxdbExons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290
removeTxdbTranscripts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290
rename.SRA.files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291
repNames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291
resFolder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292
resFolder,experiment-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292
restrictTSSByUpstreamLeader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293
revElementsF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293
reverseMinusStrandPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294
riboORFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294
riboORFsFolder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295
RiboQC.plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295
ribosomeReleaseScore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297
ribosomeStallingScore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298
ribo_fft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299
ribo_fft_plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300
rnaNormalize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301
runIDs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301
runIDs,experiment-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302
save.experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302
savePlot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303
scaledWindowPositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304
scoreSummarizedExperiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305
seqinfo,covRle-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306
seqinfo,covRleList-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306
seqinfo,experiment-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
seqlevels,covRle-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
seqlevels,covRleList-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308
seqlevels,experiment-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308
seqnamesPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
shiftFootprints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
shiftFootprintsByExperiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311
shiftPlots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314
shifts.load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315
show,covRle-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316
show,covRleList-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316
show,experiment-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317
simpleLibs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317
Contents 9
sortPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319
splitIn3Tx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320
stageNames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321
STAR.align.folder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322
STAR.align.single . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326
STAR.allsteps.multiQC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330
STAR.index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331
STAR.install . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333
STAR.multiQC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334
STAR.remove.crashed.genome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334
startCodons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335
startDefinition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336
startRegion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337
startRegionCoverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338
startRegionString . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339
startSites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340
stopCodons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341
stopDefinition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342
stopRegion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342
stopSites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343
strandBool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344
strandMode,covRle-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345
strandMode,covRleList-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345
strandPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346
subsetCoverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346
subsetToFrame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347
symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348
symbols,experiment-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348
te.plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349
te.table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350
te_rna.plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351
tile1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352
tissueNames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353
TOP.Motif.ecdf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354
topMotif . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355
transcriptWindow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356
transcriptWindow1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358
transcriptWindowPer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360
translationalEff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361
trimming.table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363
trim_detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363
txNames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364
txNamesToGeneNames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365
txSeqsFromFa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366
uniqueGroups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367
uniqueOrder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367
unlistGrl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368
uORFSearchSpace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369
10 ORFik-package
updateTxdbRanks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370
updateTxdbStartSites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371
upstreamFromPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371
upstreamOfPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372
validateExperiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373
validGRL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373
validSeqlevels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374
widthPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375
windowCoveragePlot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375
windowPerGroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377
windowPerReadLength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378
windowPerTranscript . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380
xAxisScaler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381
yAxisScaler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382
Index 383
ORFik-package ORFik for analysis of open reading frames.
Description
Main goals:
1. Finding Open Reading Frames (very fast) in the genome of interest or on the set of tran-
scripts/sequences.
2. Utilities for metaplots of RiboSeq coverage over gene START and STOP codons allowing to
spot the shift.
3. Shifting functions for the RiboSeq data.
4. Finding new Transcription Start Sites with the use of CageSeq data.
5. Various measurements of gene identity e.g. FLOSS, coverage, ORFscore, entropy that are
recreated based on many scientific publications.
6. Utility functions to extend GenomicRanges for faster grouping, splitting, tiling etc.
Author(s)
Maintainer: Haakon Tjeldnes <[email protected]> [data contributor]
Authors:
• Kornel Labun <[email protected]> [copyright holder]
Other contributors:
• Michal Swirski <[email protected]> [contributor]
• Katarzyna Chyzynska <[email protected]> [contributor, data contributor]
• Yamila Torres Cleuren <[email protected]> [contributor, thesis advisor]
• Eivind Valen <[email protected]> [thesis advisor, funder]
addCdsOnLeaderEnds 11
See Also
Useful links:
• https://github.com/Roleren/ORFik
• Report bugs at https://github.com/Roleren/ORFik/issues
addCdsOnLeaderEnds Extends leaders downstream
Description
When finding uORFs, often you want to allow them to end inside the cds.
Usage
addCdsOnLeaderEnds(fiveUTRs, cds, onlyFirstExon = FALSE)
Arguments
fiveUTRs The 5’ leader sequences as GRangesList
cds If you want to extend 5’ leaders downstream, to catch uorfs going into cds,
include it.
onlyFirstExon logical (F), include whole cds or only first exons.
Details
This is a simple way to do that
Value
a GRangesList of cds exons added to ends
See Also
Other uorfs: filterUORFs(), removeORFsWithSameStartAsCDS(), removeORFsWithSameStopAsCDS(),
removeORFsWithStartInsideCDS(), removeORFsWithinCDS(), uORFSearchSpace()
12 alignmentFeatureStatistics
addNewTSSOnLeaders Add cage max peaks as new transcript start sites for each 5’ leader (*)
strands are not supported, since direction must be known.
Description
Add cage max peaks as new transcript start sites for each 5’ leader (*) strands are not supported,
since direction must be known.
Usage
addNewTSSOnLeaders(fiveUTRs, maxPeakPosition, removeUnused, cageMcol)
Arguments
fiveUTRs (GRangesList) The 5’ leaders or full transcript sequences
maxPeakPosition
The max peak for each 5’ leader found by cage
removeUnused logical (FALSE), if False: (standard is to set them to original annotation), If
TRUE: remove leaders that did not have any cage support.
cageMcol a logical (FALSE), if TRUE, add a meta column to the returned object with the
raw CAGE counts in support for new TSS.
Value
a GRanges object of first exons
alignmentFeatureStatistics
Create alignment feature statistcs
Description
Among others how much reads are in mRNA, introns, intergenic, and check of reads from rRNA
and other ncRNAs. The better the annotation / gtf used, the more results you get.
Usage
alignmentFeatureStatistics(df, type = "ofst", BPPARAM = bpparam())
allFeaturesHelper 13
Arguments
df an ORFik experiment
type a character(default: "default"), load files in experiment or some precomputed
variant, like "ofst" or "pshifted". These are made with ORFik:::convertLibs(),
shiftFootprintsByExperiment(), etc. Can also be custom user made folders in-
side the experiments bam folder. It acts in a recursive manner with priority: If
you state "pshifted", but it does not exist, it checks "ofst". If no .ofst files, it uses
"default", which always must exists.
Presets are (folder is relative to default lib folder, some types fall back to other
formats if folder does not exist):
- "default": load the original files for experiment, usually bam.
- "ofst": loads ofst files from the ofst folder, relative to lib folder (falls back to
default)
- "pshifted": loads ofst, wig or bigwig from pshifted folder (falls back to ofst,
then default)
- "cov": Load covRle objects from cov_RLE folder (fail if not found)
- "covl": Load covRleList objects, from cov_RLE_List folder (fail if not found)
- "bed": Load bed files, from bed folder (falls back to default)
- Other formats must be loaded directly with fimport
BPPARAM how many cores/threads to use? default: bpparam(). To see number of threads
used, do bpparam()$workers. You can also add a time remaining bar, for a
more detailed pipeline.
Value
a data.table of the statistcs
allFeaturesHelper Calculate the features in computeFeatures function
Description
Not used directly, calculates all features internally for computeFeatures.
Usage
allFeaturesHelper(
grl,
RFP,
RNA,
tx,
fiveUTRs,
cds,
threeUTRs,
faFile,
riboStart,
14 allFeaturesHelper
riboStop,
sequenceFeatures,
uorfFeatures,
grl.is.sorted,
weight.RFP = 1L,
weight.RNA = 1L,
st = NULL
)
Arguments
grl a GRangesList object with usually ORFs, but can also be either leaders, cds’,
3’ utrs, etc. This is the regions you want to score.
RFP RiboSeq reads as GAlignments , GRanges or GRangesList object
RNA RnaSeq reads as GAlignments , GRanges or GRangesList object
tx a GRangesList of transcripts, normally called from: exonsBy(Gtf, by = "tx",
use.names = T) only add this if you are not including Gtf file If you are using
CAGE, you do not need to reassign these to the cage peaks, it will do it for you.
fiveUTRs fiveUTRs as GRangesList, if you used cage-data to extend 5’ utrs, remember to
input CAGE assigned version and not original!
cds a GRangesList of coding sequences
threeUTRs a GRangesList of transcript 3’ utrs, normally called from: threeUTRsByTran-
script(Gtf, use.names = T)
faFile a path to fasta indexed genome, an open FaFile, a BSgenome, or path to ORFik
experiment with valid genome.
riboStart usually 26, the start of the floss interval, see ?floss
riboStop usually 34, the end of the floss interval
sequenceFeatures
a logical, default TRUE, include all sequence features, that is: Kozak, fraction-
Lengths, distORFCDS, isInFrame, isOverlapping and rankInTx. uorfFeatures =
FALSE will remove the 4 last.
uorfFeatures a logical, default TRUE, include all uORF sequence features, that is: distOR-
FCDS, isInFrame, isOverlapping and rankInTx
grl.is.sorted logical (F), a speed up if you know argument grl is sorted, set this to TRUE.
weight.RFP a vector (default: 1L). Can also be character name of column in RFP. As in trans-
lationalEff(weight = "score") for: GRanges("chr1", 1, "+", score = 5), would
mean score column tells that this alignment region was found 5 times.
weight.RNA Same as weightRFP but for RNA weights. (default: 1L)
st (NULL), if defined must be: st = startRegion(grl, tx, T, -3, 9)
Value
a data.table with features
appendZeroes 15
appendZeroes Append zero values to data.table
Description
For every position in width max.pos - min.pos + 1, append 0 values in data.table. Needed when
coveragePerTiling was run on coverage window with drop.zero.dt as TRUE and you need to plot 0
positions after a transformation by coverageScorings.
Usage
appendZeroes(dt, max.pos, min.pos = 1L, fractions = unique(dt$fraction))
Arguments
dt a data.table from coverageByTiling that is normalized by coverageScorings.
max.pos integer, max position of dt
min.pos integer, default 1L. Minimum position of dt
fractions default unique(dt$fraction), will repeat each fraction max.pos - min.pos + 1
times.
Value
a data.table with appended 0 values
artificial.orfs Create small artificial orfs from cds
Description
Usefull to see if short ORFs prediction is dependent on length.
Split cds first in two, a start part and stop part. Then say how large the two parts can be and merge
them together. It will sample a value in range give.
Parts will be forced to not overlap and can not extend outside original cds
Usage
artificial.orfs(
cds,
start5 = 1,
end5 = 4,
start3 = -4,
end3 = 0,
bin.if.few = TRUE
)
16 assignAnnotations
Arguments
cds a GRangesList of orfs, must have width %% 3 == 0 and length >= 6
start5 integer, default: 1 (start of orf)
end5 integer, default: 4 (max 4 codons from start codon)
start3 integer, default -4 (max 4 codons from stop codon)
end3 integer, default: 0 (end of orf)
bin.if.few logical, default TRUE, instead of per codon, do per 2, 3, 4 codons if you have
few samples compared to lengths wanted, If you have 4 cds’ and you want 7
different lengths, which is the standard, it will give you possible nt length: 6-12-
18-24 instead of original 6-9-12-15-18-21-24.
If you have more than 30x cds than lengths wanted this is skipped. (for default
arguments this is: 7*30 = 210 cds)
Details
If artificial cds length is not divisible by 2, like 3 codons, the second codon will always be from the
start region etc.
Also If there are many very short original cds, the distribution will be skewed towards more smaller
artificial cds.
Value
GRangesList of new ORFs (sorted: + strand increasing start, - strand decreasing start)
Examples
txdb <- ORFik.template.experiment()
#cds <- loadRegion(txdb, "cds")
## To get enough CDSs, just replicate them
# cds <- rep(cds, 100)
#artificial.orfs(cds)
assignAnnotations Overlaps GRanges object with provided annotations.
Description
It will return same list of GRanges, but with metdata columns: trainscript_id - id of transcripts
that overlap with each ORF gene_id - id of gene that this transcript belongs to isoform - for coding
protein alignment in relation to cds on coresponding transcript, for non-coding transcripts alignment
in relation to the transcript.
Usage
assignAnnotations(ORFs, con)
assignFirstExonsStartSite 17
Arguments
ORFs - GRanges or GRangesList object of your ORFs.
con - Path to gtf file with annotations.
Value
A GRanges object of your ORFs with metadata columns ’gene’, ’transcript’, isoform’ and ’biotype’.
assignFirstExonsStartSite
Reassign the start positions of the first exons per group in grl
Description
Per group in GRangesList, assign the most upstream site.
Usage
assignFirstExonsStartSite(
grl,
newStarts,
is.circular = all(isCircular(grl) %in% TRUE)
)
Arguments
grl a GRangesList object
newStarts an integer vector of same length as grl, with new start values (absolute coordi-
nates, not relative)
is.circular logical, default FALSE if not any is: all(isCircular(grl) Where grl is the ranges
checked. If TRUE, allow ranges to extend below position 1 on chromosome.
Since circular genomes can have negative coordinates.
Details
make sure your grl is sorted, since start of "-" strand objects should be the max end in group, use
ORFik:::sortPerGroup(grl) to get sorted grl.
Value
the same GRangesList with new start sites
See Also
Other GRanges: assignLastExonsStopSite(), downstreamFromPerGroup(), downstreamOfPerGroup(),
upstreamFromPerGroup(), upstreamOfPerGroup()
18 assignLastExonsStopSite
assignLastExonsStopSite
Reassign the stop positions of the last exons per group
Description
Per group in GRangesList, assign the most downstream site.
Usage
assignLastExonsStopSite(
grl,
newStops,
is.circular = all(isCircular(grl) %in% TRUE)
)
Arguments
grl a GRangesList object
newStops an integer vector of same length as grl, with new start values (absolute coordi-
nates, not relative)
is.circular logical, default FALSE if not any is: all(isCircular(grl) Where grl is the ranges
checked. If TRUE, allow ranges to extend below position 1 on chromosome.
Since circular genomes can have negative coordinates.
Details
make sure your grl is sorted, since stop of "-" strand objects should be the min start in group, use
ORFik:::sortPerGroup(grl) to get sorted grl.
Value
the same GRangesList with new stop sites
See Also
Other GRanges: assignFirstExonsStartSite(), downstreamFromPerGroup(), downstreamOfPerGroup(),
upstreamFromPerGroup(), upstreamOfPerGroup()
assignTSSByCage 19
assignTSSByCage Input a txdb and add a 5’ leader for each transcript, that does not have
one.
Description
For all cds in txdb, that does not have a 5’ leader: Start at 1 base upstream of cds and use CAGE,
to assign leader start. All these leaders will be 1 exon based, if you really want exon splicings, you
can use exon prediction tools, or run sequencing experiments.
Usage
assignTSSByCage(
txdb,
cage,
extension = 1000,
filterValue = 1,
restrictUpstreamToTx = FALSE,
removeUnused = FALSE,
preCleanup = TRUE,
pseudoLength = 1
)
Arguments
txdb a TxDb file, a path to one of: (.gtf ,.gff, .gff2, .gff2, .db or .sqlite) or an ORFik
experiment
cage Either a filePath for the CageSeq file as .bed .bam or .wig, with possible com-
pressions (".gzip", ".gz", ".bgz"), or already loaded CageSeq peak data as GRanges
or GAlignment. NOTE: If it is a .bam file, it will add a score column by run-
ning: convertToOneBasedRanges(cage, method = "5prime", addScoreColumn =
TRUE) The score column is then number of replicates of read, if score column
is something else, like read length, set the score column to NULL first.
extension The maximum number of basses upstream of the TSS to search for CageSeq
peak.
filterValue The minimum number of reads on cage position, for it to be counted as possible
new tss. (represented in score column in CageSeq data) If you already filtered,
set it to 0.
restrictUpstreamToTx
a logical (FALSE). If TRUE: restrict leaders to not extend closer than 5 bases
from closest upstream leader, set this to TRUE.
removeUnused logical (FALSE), if False: (standard is to set them to original annotation), If
TRUE: remove leaders that did not have any cage support.
preCleanup logical (TRUE), if TRUE, remove all reads in region (-5:-1, 1:5) of all original
tss in leaders. This is to keep original TSS if it is only +/- 5 bases from the
original.
20 asTX
pseudoLength a numeric, default 1. Either if no CAGE supports the leader, or if CAGE is set
to NULL, add a pseudo length for all the UTRs. Will not extend a leader if it
would make it go outside the defined seqlengths of the genome. So this length
is not guaranteed for all!
Details
Given a TxDb object, reassign the start site per transcript using max peaks from CageSeq data. A
max peak is defined as new TSS if it is within boundary of 5’ leader range, specified by ‘extension‘
in bp. A max peak must also be higher than minimum CageSeq peak cutoff specified in ‘filter-
Value‘. The new TSS will then be the positioned where the cage read (with highest read count in
the interval). If no CAGE supports a leader, the width will be set to 1 base.
Value
a TxDb obect of reassigned transcripts
See Also
Other CAGE: reassignTSSbyCage(), reassignTxDbByCage()
Examples
txdbFile <- system.file("extdata", "hg19_knownGene_sample.sqlite",
package = "GenomicFeatures")
cagePath <- system.file("extdata", "cage-seq-heart.bed.bgz",
package = "ORFik")
## Not run:
assignTSSByCage(txdbFile, cagePath)
#Minimum 20 cage tags for new TSS
assignTSSByCage(txdbFile, cagePath, filterValue = 20)
# Create pseudo leaders for the ones without hits
assignTSSByCage(txdbFile, cagePath, pseudoLength = 100)
# Create only pseudo leaders (in example 2 leaders are added)
assignTSSByCage(txdbFile, cage = NULL, pseudoLength = 100)
## End(Not run)
asTX Map genomic to transcript coordinates by reference
Description
Map range coordinates between features in the genome and transcriptome (reference) space.
asTX 21
Usage
asTX(
grl,
reference,
ignore.strand = FALSE,
x.is.sorted = TRUE,
tx.is.sorted = TRUE
)
Arguments
grl a GRangesList of ranges within the reference, grl must have column called
names that gives grouping for result
reference a GRangesList of ranges that include and are bigger or equal to grl ig. cds is grl
and gene can be reference
ignore.strand When ignore.strand is TRUE, strand is ignored in overlaps operations (i.e., all
strands are considered "+") and the strand in the output is ’*’.
When ignore.strand is FALSE (default) strand in the output is taken from the
transcripts argument. When transcripts is a GRangesList, all inner list elements
of a common list element must have the same strand or an error is thrown.
Mapped position is computed by counting from the transcription start site (TSS)
and is not affected by the value of ignore.strand.
x.is.sorted if x is a GRangesList object, are "-" strand groups pre-sorted in decreasing order
within group, default: TRUE
tx.is.sorted if transcripts is a GRangesList object, are "-" strand groups pre-sorted in de-
creasing order within group, default: TRUE
Details
Similar to GenomicFeatures’ pmapToTranscripts, but in this version the grl ranges are compared to
reference ranges with same name, not by index. And it has a security fix.
Value
a GRangesList in transcript coordinates
See Also
Other ExtendGenomicRanges: coveragePerTiling(), extendLeaders(), extendTrailers(),
reduceKeepAttr(), tile1(), txSeqsFromFa(), windowPerGroup()
Examples
seqname <- c("tx1", "tx2", "tx3")
seqs <- c("ATGGGTATTTATA", "AAAAA", "ATGGGTAATA")
grIn1 <- GRanges(seqnames = "1",
ranges = IRanges(start = c(21, 10), end = c(23, 19)),
strand = "-")
22 bamVarName
grIn2 <- GRanges(seqnames = "1",
ranges = IRanges(start = c(1), end = c(5)),
strand = "-")
grIn3 <- GRanges(seqnames = "1",
ranges = IRanges(start = c(1010), end = c(1019)),
strand = "-")
grl <- GRangesList(grIn1, grIn2, grIn3)
names(grl) <- seqname
# Find ORFs
test_ranges <- findMapORFs(grl, seqs,
"ATG|TGG|GGG",
"TAA|AAT|ATA",
longestORF = FALSE,
minimumLength = 0)
# Genomic coordinates ORFs
test_ranges
# Transcript coordinate ORFs
asTX(test_ranges, reference = grl)
# seqnames will here be index of transcript it came from
bamVarName Get library variable names from ORFik experiment
Description
What will each sample be called given the columns of the experiment? A column is included if
more than 1 unique element value exist in that column.
Usage
bamVarName(
df,
skip.replicate = length(unique(df$rep)) == 1,
skip.condition = length(unique(df$condition)) == 1,
skip.stage = length(unique(df$stage)) == 1,
skip.fraction = length(unique(df$fraction)) == 1,
skip.experiment = !df@expInVarName,
skip.libtype = FALSE,
fraction_prepend_f = TRUE
)
Arguments
df an ORFik experiment
skip.replicate a logical (FALSE), don’t include replicate in variable name.
skip.condition a logical (FALSE), don’t include condition in variable name.
skip.stage a logical (FALSE), don’t include stage in variable name.
bamVarNamePicker 23
skip.fraction a logical (FALSE), don’t include fraction
skip.experiment
a logical (FALSE), don’t include experiment
skip.libtype a logical (FALSE), don’t include libtype
fraction_prepend_f
a logical (TRUE), include "f" in front of fraction, useful for knowing what frac-
tion is.
Value
variable names of libraries (character vector)
See Also
Other ORFik_experiment: ORFik.template.experiment(), ORFik.template.experiment.zf(),
create.experiment(), experiment-class, filepath(), libraryTypes(), organism,experiment-method,
outputLibs(), read.experiment(), save.experiment(), validateExperiments()
Examples
df <- ORFik.template.experiment()
bamVarName(df)
## without libtype
bamVarName(df, skip.libtype = TRUE)
## Without experiment name
bamVarName(df, skip.experiment = TRUE)
bamVarNamePicker Get variable name per filepath in experiment
Description
Get variable name per filepath in experiment
Usage
bamVarNamePicker(
df,
skip.replicate = FALSE,
skip.condition = FALSE,
skip.stage = FALSE,
skip.fraction = FALSE,
skip.experiment = FALSE,
skip.libtype = FALSE,
fraction_prepend_f = TRUE
)
24 batchNames
Arguments
df an ORFik experiment
skip.replicate a logical (FALSE), don’t include replicate in variable name.
skip.condition a logical (FALSE), don’t include condition in variable name.
skip.stage a logical (FALSE), don’t include stage in variable name.
skip.fraction a logical (FALSE), don’t include fraction
skip.experiment
a logical (FALSE), don’t include experiment
skip.libtype a logical (FALSE), don’t include libtype
fraction_prepend_f
a logical (TRUE), include "f" in front of fraction, useful for knowing what frac-
tion is.
Value
variable name of library (character vector)
batchNames Get batch name variants
Description
Used to standardize nomeclature for experiments.
Example: Biological samples (batches) batch will become b1
Usage
batchNames()
Value
a data.table with 2 columns, the main name, and all name variants of the main name in second
column as a list.
See Also
Other experiment_naming: cellLineNames(), cellTypeNames(), conditionNames(), fractionNames(),
inhibitorNames(), libNames(), mainNames(), repNames(), stageNames(), tissueNames()
bedToGR 25
bedToGR Converts bed style data.frame to GRanges
Description
For info on columns, see: https://www.ensembl.org/info/website/upload/bed.html
Usage
bedToGR(x, skip.name = TRUE)
Arguments
x A data.frame from imported bed-file, to convert to GRanges
skip.name default (TRUE), skip name column (column 4)
Value
a GRanges object from bed
See Also
Other utils: convertToOneBasedRanges(), export.bed12(), export.bigWig(), export.fstwig(),
export.wiggle(), fimport(), findFa(), fread.bed(), optimizeReads(), readBam(), readBigWig(),
readWig()
browseSRA Open SRA in browser for specific bioproject
Description
Open SRA in browser for specific bioproject
Usage
browseSRA(x, browser = getOption("browser"))
Arguments
x character, bioproject ID.
browser a non-empty character string giving the name of the program to be used as the
HTML browser. It should be in the PATH, or a full path specified. Alternatively,
an R function to be called to invoke the browser.
Under Windows NULL is also allowed (and is the default), and implies that the
file association mechanism will be used.
26 cellLineNames
Value
invisible(NULL), opens webpage only
See Also
Other sra: download.SRA(), download.SRA.metadata(), download.ebi(), get_bioproject_candidates(),
install.sratoolkit(), rename.SRA.files()
Examples
#browseSRA("PRJNA336542")
#' # For windows make sure a valid browser is defined:
browser <- getOption("browser")
#browseSRA("PRJNA336542", browser)
cellLineNames Get cell-line name variants
Description
Used to standardize nomeclature for experiments.
Example: THP1 is main naming, but a variant is THP-1 THP-1 will then be renamed to THP1
(variables in R, can not have - in them)
Usage
cellLineNames(convertToTissue = FALSE)
Arguments
convertToTissue
logical, FALSE. If TRUE, return tissue type. NONE is returned for general
non-differentiated cell lines like 3T3.
Value
a data.table with 2 columns, the main name, and all name variants of the main name in second
column as a list.
See Also
Other experiment_naming: batchNames(), cellTypeNames(), conditionNames(), fractionNames(),
inhibitorNames(), libNames(), mainNames(), repNames(), stageNames(), tissueNames()
cellTypeNames 27
cellTypeNames Get cell type name variants
Description
Used to standardize nomeclature for experiments.
Example: 1 is main naming, but a variant is rep1 rep1 will then be renamed to 1
Usage
cellTypeNames()
Value
a data.table with 2 columns, the main name, and all name variants of the main name in second
column as a list.
See Also
Other experiment_naming: batchNames(), cellLineNames(), conditionNames(), fractionNames(),
inhibitorNames(), libNames(), mainNames(), repNames(), stageNames(), tissueNames()
changePointAnalysis Get the offset for specific RiboSeq read width
Description
Creates sliding windows of transcript normalized counts per position and check which window has
most in upstream window vs downstream window. Pick the position with highest absolute value
maximum of the window difference. Checks windows with split sites between positions -17 to -7,
where 0 is TIS. Normally you expect the shift around -12 for Ribo-seq, in TCP-seq / RCP-seq it is
usually a bit higher, usually because of cross-linking variations.
Usage
changePointAnalysis(
x,
feature = "start",
max.pos = 40L,
interval = seq.int(14L, 24L),
center.pos = 12,
info = NULL,
verbose = FALSE
)
28 checkRFP
Arguments
x a vector with count per position to analyse, assumes the zero position (TIS) is in
the middle + 1 (position 0). Default it is size 60, from -30 to 29 in p-shifting
feature (character) either "start" or "stop"
max.pos integer, default 40L, subset x to go from index 1 to max.pos, if tail is not relevant.
interval integer vector , default seq.int(14L, 24L). The possible shift locations, default
Seperation points for upstream and downstream windows. That is (+/- 5 from
-12) position.
center.pos integer, default 12. Centering position for likely p-site. A first qualified guess to
save time. 12 means 12 bases before TIS.
info specify read length if wanted for verbose output.
verbose logical, default FALSE. Report details of change point analysis.
Details
For visual explanation, see the supl. data of ORFik paper: Transcript normalized means per CDS
TIS region, count reads per position, divide that number per position by the total of that transcript,
then sum up these numbers per position for all transcripts.
Value
a single numeric offset, -12 would mean p-site is 12 bases upstream
See Also
Other pshifting: detectRibosomeShifts(), shiftFootprints(), shiftFootprintsByExperiment(),
shiftPlots(), shifts.load()
checkRFP Helper Function to check valid RFP input
Description
Helper Function to check valid RFP input
Usage
checkRFP(class)
Arguments
class the given class of RFP object
Value
NULL, stop if invalid object
checkRNA 29
See Also
Other validity: checkRNA(), is.ORF(), is.gr_or_grl(), is.grl(), is.range(), validGRL(),
validSeqlevels()
checkRNA Helper Function to check valid RNA input
Description
Helper Function to check valid RNA input
Usage
checkRNA(class)
Arguments
class the given class of RNA object
Value
NULL, stop if unvalid object
See Also
Other validity: checkRFP(), is.ORF(), is.gr_or_grl(), is.grl(), is.range(), validGRL(),
validSeqlevels()
codonSumsPerGroup Get read hits per codon
Description
Helper for entropy function, normally not used directly Seperate each group into tuples (abstract
codons) Gives sum for each tuple within each group
Usage
codonSumsPerGroup(grl, reads, weight = "score", is.sorted = FALSE)
30 codon_usage
Arguments
grl a GRangesList of 5’ utrs, CDS, transcripts, etc.
reads a GAlignments, GRanges, or precomputed coverage as covRle (one for each
strand) of RiboSeq, RnaSeq etc.
Weigths for scoring is default the ’score’ column in ’reads’. Can also be random
access paths to bigWig or fstwig file. Do not use random access for more than a
few genes, then loading the entire files is usually better. File streaming is still in
beta, so use with care!
weight (default: ’score’), if defined a character name of valid meta column in subject.
GRanges("chr1", 1, "+", score = 5), would mean score column tells that this
alignment region was found 5 times. ORFik ofst, bedoc and .bedo files contains
a score column like this. As do CAGEr CAGE files and many other package
formats. You can also assign a score column manually.
is.sorted logical (FALSE), is grl sorted. That is + strand groups in increasing ranges
(1,2,3), and - strand groups in decreasing ranges (3,2,1)
Details
Example: counts c(1,0,0,1), with reg_len = 2, gives c(1,0) and c(0,1), these are summed and re-
turned as data.table 10 bases, will give 3 codons, 1 base codons does not exist.
Value
a data.table with codon sums
codon_usage Codon usage
Description
Per AA / codon, analyse the coverage, get a multitude of features. For both A sites and P-sites
(Input reads must be P-sites for now) This function takes inspiration from the codonDT paper, and
among others returns the negative binomial estimates, but in addition many other features.
Usage
codon_usage(
reads,
cds,
mrna,
faFile,
filter_table,
filter_cds_mod3 = TRUE,
min_counts_cds_filter = max(min(quantile(filter_table, 0.5), 1000), 1000),
with_A_sites = TRUE,
aligned_position = "center",
code = GENETIC_CODE
)
codon_usage 31
Arguments
reads either a single library (GRanges, GAlignment, GAlignmentPairs), or a list of
libraries returned from outputLibs(df) with p-sites. If list, the list must have
names coresponding to the library names.
cds a GRangesList
mrna a GRangesList
faFile a FaFile from genome
filter_table a matrix / vector of length equal to cds
filter_cds_mod3
logical, default TRUE. Remove all ORFs that are not mod3, this speeds up the
computation a lot, and usually removes malformed ORFs you would not want
anyway.
min_counts_cds_filter
numeric, default: max(min(quantile(filter_table, 0.50), 100), 100). Min-
imum number of counts from the ’filter_table’ argument.
with_A_sites logical, default TRUE. Not used yet, will also return A site scores.
aligned_position
what positions should be taken to calculate per-codon coverage. By default:
"center", meaning that positions -1,0,1 will be taken. Alternative: "left", then
positions 0,1,2 are taken.
code a named character vector of size 64. Default: GENETIC_CODE. Change if
organism does not use the standard code.
Details
The primary column to use is "mean_txNorm", this is the fair normalized score.
Value
a data.table of rows per codon / AA. All values are given per library, per site (A or P), sorted by the
mean_txNorm_percentage column of the first library in the set, the columns are:
• variable (character)Library name
• seq (character)Amino acid:codon
• sum (integer)total counts per seq
• sum_txNorm (integer)total counts per seq normalized per tx
• var (numeric)variance of total counts per seq
• N (integer)total number of codons of that type
• mean_txNorm (numeric)Default use output, the fair codon usage, normalized both for gene
and genome level for codon and read counts
• ...
• alpha (numeric)dirichlet alpha MOM estimator (imagine mean and variance of probability in
1 value, the lower the value, the higher the variance, mean is decided by the relative value
between samples)
32 codon_usage_exp
• sum_txNorm (integer)total counts per seq normalized per tx
• relative_to_max_score (integer)Percentage use of codon
• type (factor(character))Either "P" or "A"
References
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196831/
See Also
Other codon: codon_usage_exp(), codon_usage_plot()
Examples
df <- ORFik.template.experiment()[9:10,] # Subset to 2 Ribo-seq libs
## For single library
reads <- fimport(filepath(df[1,], "pshifted"))
cds <- loadRegion(df, "cds", filterTranscripts(df))
mrna <- loadRegion(df, "mrna", names(cds))
filter_table <- assay(countTable(df, type = "summarized")[names(cds)])
faFile <- findFa(df)
res <- codon_usage(reads, cds, mrna, faFile = faFile,
filter_table = filter_table, min_counts_cds_filter = 10)
codon_usage_exp Codon analysis for ORFik experiment
Description
Per AA / codon, analyse the coverage, get a multitude of features. For both A sites and P-sites
(Input reads must be P-sites for now) This function takes inspiration from the codonDT paper, and
among others returns the negative binomial estimates, but in addition many other features.
Usage
codon_usage_exp(
df,
reads,
cds = loadRegion(df, "cds", filterTranscripts(df)),
mrna = loadRegion(df, "mrna", names(cds)),
filter_cds_mod3 = TRUE,
filter_table = assay(countTable(df, type = "summarized")[names(cds)]),
faFile = df@fafile,
min_counts_cds_filter = max(min(quantile(filter_table, 0.5), 1000), 1000),
with_A_sites = TRUE,
code = GENETIC_CODE,
aligned_position = "center"
)
codon_usage_exp 33
Arguments
df an ORFik experiment
reads either a single library (GRanges, GAlignment, GAlignmentPairs), or a list of
libraries returned from outputLibs(df) with p-sites. If list, the list must have
names coresponding to the library names.
cds a GRangesList, the coding sequences, default: loadRegion(df, "cds", filterTranscripts(df)),
longest isoform per gene.
mrna a GRangesList, the full mRNA sequences (matching by names the cds sequences),
default: loadRegion(df, "mrna", names(cds)).
filter_cds_mod3
logical, default TRUE. Remove all ORFs that are not mod3, this speeds up the
computation a lot, and usually removes malformed ORFs you would not want
anyway.
filter_table an numeric(integer) matrix, where rownames are the names of the full set of
mRNA transcripts. This will be subsetted to the cds subset you use. Then CDSs
are filtered from this table by the ’min_counts_cds_filter’ argument.
faFile FaFile, BSgenome, fasta/index file path or an ORFik experiment. This file is
usually used to find the transcript sequences from some GRangesList.
min_counts_cds_filter
numeric, default: max(min(quantile(filter_table, 0.50), 100), 100). Min-
imum number of counts from the ’filter_table’ argument.
with_A_sites logical, default TRUE. Not used yet, will also return A site scores.
code a named character vector of size 64. Default: GENETIC_CODE. Change if
organism does not use the standard code.
aligned_position
what positions should be taken to calculate per-codon coverage. By default:
"center", meaning that positions -1,0,1 will be taken. Alternative: "left", then
positions 0,1,2 are taken.
Details
The primary column to use is "mean_txNorm", this is the fair normalized score.
Value
a data.table of rows per codon / AA. All values are given per library, per site (A or P), sorted by the
mean_txNorm_percentage column of the first library in the set, the columns are:
• variable (character)Library name
• seq (character)Amino acid:codon
• sum (integer)total counts per seq
• sum_txNorm (integer)total counts per seq normalized per tx
• var (numeric)variance of total counts per seq
• N (integer)total number of codons of that type
34 codon_usage_plot
• mean_txNorm (numeric)Default use output, the fair codon usage, normalized both for gene
and genome level for codon and read counts
• ...
• alpha (numeric)dirichlet alpha MOM estimator (imagine mean and variance of probability in
1 value, the lower the value, the higher the variance, mean is decided by the relative value
between samples)
• sum_txNorm (integer)total counts per seq normalized per tx
• relative_to_max_score (integer)Percentage use of codon
• type (factor(character))Either "P" or "A"
References
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196831/
See Also
Other codon: codon_usage(), codon_usage_plot()
Examples
df <- ORFik.template.experiment()[9:10,] # Subset to 2 Ribo-seq libs
## For single library
res <- codon_usage_exp(df, fimport(filepath(df[1,], "pshifted")),
min_counts_cds_filter = 10)
# mean_txNorm is adviced scoring column
# codon_usage_plot(res, res$mean_txNorm)
# Default for plot function is the percentage scaled version of mean_txNorm
# codon_usage_plot(res) # This gives check error
## For multiple libs
res2 <- codon_usage_exp(df, outputLibs(df, type = "pshifted", output.mode = "list"),
min_counts_cds_filter = 10)
# codon_usage_plot(res2)
codon_usage_plot Plot codon_usage
Description
Plot codon_usage
Usage
codon_usage_plot(
res,
score_column = res$relative_to_max_score,
ylab = "Ribo-seq library",
legend.position = "none",
collapse.by.scores 35
limit = c(0, max(score_column)),
midpoint = limit/2,
monospace_font = TRUE
)
Arguments
res a data.table of output from a codon_usage function
score_column numeric, default: res$relative_to_max_score. Which parameter to use as score
column.
ylab character vector, names for libraries to show on Y axis
legend.position
character, default "none", do not display legend.
limit numeric, 2 values for plot color limits. Default: c(0, max(score_column))
midpoint numeric, default: limit/2. midpoint of color limit.
monospace_font logical, default TRUE. Use monospace font, this does not work on systems (re-
quire specific font packages), set to FALSE if it crashes for you.
Value
a ggplot object
See Also
Other codon: codon_usage(), codon_usage_exp()
Examples
df <- ORFik.template.experiment()[9:10,] # Subset to 2 Ribo-seq libs
## For multiple libs
res2 <- codon_usage_exp(df, outputLibs(df, type = "pshifted", output.mode = "list"),
min_counts_cds_filter = 10)
# codon_usage_plot(res2, monospace_font = TRUE) # This gives check error
codon_usage_plot(res2, monospace_font = FALSE) # monospace font looks better
collapse.by.scores Merge reads by sum of existing scores
Description
If you have multiple reads a same location but different read lengths, specified in meta column
"size", it will sum up the scores (number of replicates) for all reads at that position
Usage
collapse.by.scores(x)
36 collapse.fastq
Arguments
x a GRanges object
Value
merged GRanges object
Examples
gr_s1 <- rep(GRanges("chr1", 1:10,"+"), 2)
gr_s2 <- GRanges("chr1", 1:12,"+")
gr2 <- GRanges("chr1", 21:40,"+")
gr <- c(gr_s1, gr_s2, gr2)
res <- convertToOneBasedRanges(gr,
addScoreColumn = TRUE, addSizeColumn = TRUE)
ORFik:::collapse.by.scores(res)
collapse.fastq Very fast fastq/fasta collapser
Description
For each unique read in the file, collapse into 1 and state in the fasta header how many reads existed
of that type. This is done after trimming usually, works best for reads < 50 read length. Not so
effective for 150 bp length mRNA-seq etc.
Usage
collapse.fastq(
files,
outdir = file.path(dirname(files[1]), "collapsed"),
header.out.format = "ribotoolkit",
compress = FALSE,
prefix = "collapsed_"
)
Arguments
files paths to fasta / fastq files to collapse. I tries to detect format per file, if file does
not have .fastq, .fastq.gz, .fq or fq.gz extensions, it will be treated as a .fasta file
format.
outdir outdir to save files, default: file.path(dirname(files[1]), "collapsed").
Inside same folder as input files, then create subfolder "collapsed", and add a
prefix of "collapsed_" to the output names in that folder.
collapseDuplicatedReads 37
header.out.format
character, default "ribotoolkit", else must be "fastx". How the read header of the
output fasta should be formated: ribotoolkit: ">seq1_x55", sequence 1 has 55
duplicated reads collapsed. fastx: ">1-55", sequence 1 has 55 duplicated reads
collapsed
compress logical, default FALSE
prefix character, default "collapsed_" Prefix to name of output file.
Value
invisible(NULL), files saved to disc in fasta format.
Examples
fastq.folder <- tempdir() # <- Your fastq files
infiles <- dir(fastq.folder, "*.fastq", full.names = TRUE)
# collapse.fastq(infiles)
collapseDuplicatedReads
Collapse duplicated reads
Description
For every GRanges, GAlignments read, with the same: seqname, start, (cigar) / width and strand,
collapse and give a new meta column called "score", which contains the number of duplicates of
that read. If score column already exists, will return input object!
Usage
collapseDuplicatedReads(x, addScoreColumn = TRUE, ...)
Arguments
x a GRanges, GAlignments or GAlignmentPairs object
addScoreColumn logical, default: (TRUE), if FALSE, only collapse and not keep score column of
counts for collapsed reads. Returns directly without collapsing if reuse.score.column
is FALSE and score is already defined.
... alternative arguments for class instances. For example, see: ?'collapseDuplicatedReads,GRanges-method'
Value
a GRanges, GAlignments, GAlignmentPairs or data.table object, same as input
Examples
gr <- rep(GRanges("chr1", 1:10,"+"), 2)
collapseDuplicatedReads(gr)
38 collapseDuplicatedReads,data.table-method
collapseDuplicatedReads,data.table-method
Collapse duplicated reads
Description
For every GRanges, GAlignments read, with the same: seqname, start, (cigar) / width and strand,
collapse and give a new meta column called "score", which contains the number of duplicates of
that read. If score column already exists, will return input object!
Usage
## S4 method for signature 'data.table'
collapseDuplicatedReads(
x,
addScoreColumn = TRUE,
addSizeColumn = FALSE,
reuse.score.column = TRUE,
keepCigar = FALSE
)
Arguments
x a GRanges, GAlignments or GAlignmentPairs object
addScoreColumn logical, default: (TRUE), if FALSE, only collapse and not keep score column of
counts for collapsed reads. Returns directly without collapsing if reuse.score.column
is FALSE and score is already defined.
addSizeColumn logical (FALSE), if TRUE, add a size column that for each read, that gives orig-
inal width of read. Useful if you need original read lengths. This takes care of
soft clips etc. If collapsing reads, each unique range will be grouped also by
size.
reuse.score.column
logical (TRUE), if addScoreColumn is TRUE, and a score column exists, will
sum up the scores to create a new score. If FALSE, will skip old score column
and create new according to number of replicated reads after conversion. If
addScoreColumn is FALSE, this argument is ignored.
keepCigar logical, default FALSE. Keep the cigar information
Value
a GRanges, GAlignments, GAlignmentPairs or data.table object, same as input
Examples
gr <- rep(GRanges("chr1", 1:10,"+"), 2)
collapseDuplicatedReads(gr)
collapseDuplicatedReads,GAlignmentPairs-method 39
collapseDuplicatedReads,GAlignmentPairs-method
Collapse duplicated reads
Description
For every GRanges, GAlignments read, with the same: seqname, start, (cigar) / width and strand,
collapse and give a new meta column called "score", which contains the number of duplicates of
that read. If score column already exists, will return input object!
Usage
## S4 method for signature 'GAlignmentPairs'
collapseDuplicatedReads(x, addScoreColumn = TRUE)
Arguments
x a GRanges, GAlignments or GAlignmentPairs object
addScoreColumn logical, default: (TRUE), if FALSE, only collapse and not keep score column of
counts for collapsed reads. Returns directly without collapsing if reuse.score.column
is FALSE and score is already defined.
Value
a GRanges, GAlignments, GAlignmentPairs or data.table object, same as input
Examples
gr <- rep(GRanges("chr1", 1:10,"+"), 2)
collapseDuplicatedReads(gr)
collapseDuplicatedReads,GAlignments-method
Collapse duplicated reads
Description
For every GRanges, GAlignments read, with the same: seqname, start, (cigar) / width and strand,
collapse and give a new meta column called "score", which contains the number of duplicates of
that read. If score column already exists, will return input object!
Usage
## S4 method for signature 'GAlignments'
collapseDuplicatedReads(x, addScoreColumn = TRUE, reuse.score.column = TRUE)
40 collapseDuplicatedReads,GRanges-method
Arguments
x a GRanges, GAlignments or GAlignmentPairs object
addScoreColumn logical, default: (TRUE), if FALSE, only collapse and not keep score column of
counts for collapsed reads. Returns directly without collapsing if reuse.score.column
is FALSE and score is already defined.
reuse.score.column
logical (TRUE), if addScoreColumn is TRUE, and a score column exists, will
sum up the scores to create a new score. If FALSE, will skip old score column
and create new according to number of replicated reads after conversion. If
addScoreColumn is FALSE, this argument is ignored.
Value
a GRanges, GAlignments, GAlignmentPairs or data.table object, same as input
Examples
gr <- rep(GRanges("chr1", 1:10,"+"), 2)
collapseDuplicatedReads(gr)
collapseDuplicatedReads,GRanges-method
Collapse duplicated reads
Description
For every GRanges, GAlignments read, with the same: seqname, start, (cigar) / width and strand,
collapse and give a new meta column called "score", which contains the number of duplicates of
that read. If score column already exists, will return input object!
Usage
## S4 method for signature 'GRanges'
collapseDuplicatedReads(
x,
addScoreColumn = TRUE,
addSizeColumn = FALSE,
reuse.score.column = TRUE
)
Arguments
x a GRanges, GAlignments or GAlignmentPairs object
addScoreColumn logical, default: (TRUE), if FALSE, only collapse and not keep score column of
counts for collapsed reads. Returns directly without collapsing if reuse.score.column
is FALSE and score is already defined.
combn.pairs 41
addSizeColumn logical (FALSE), if TRUE, add a size column that for each read, that gives orig-
inal width of read. Useful if you need original read lengths. This takes care of
soft clips etc. If collapsing reads, each unique range will be grouped also by
size.
reuse.score.column
logical (TRUE), if addScoreColumn is TRUE, and a score column exists, will
sum up the scores to create a new score. If FALSE, will skip old score column
and create new according to number of replicated reads after conversion. If
addScoreColumn is FALSE, this argument is ignored.
Value
a GRanges, GAlignments, GAlignmentPairs or data.table object, same as input
Examples
gr <- rep(GRanges("chr1", 1:10,"+"), 2)
collapseDuplicatedReads(gr)
combn.pairs Create all unique combinations pairs possible
Description
Given a character vector, get all unique combinations of 2.
Usage
combn.pairs(x)
Arguments
x a character vector, will unique elements for you.
Value
a list of character vector pairs
Examples
df <- ORFik.template.experiment()
ORFik:::combn.pairs(df[, "libtype"])
42 computeFeatures
computeFeatures Get all main features in ORFik
Description
If you want to get all the NGS and/or sequence features easily, you can use this function. Each
feature have a link to an article describing its creation and idea behind it. Look at the functions in
the feature family (in the "see also" section below) to see all of them. Example, if you want to know
what the "te" column is, check out: ?translationalEff.
A short description of each feature is also shown here:
** NGS features ** If not stated otherwise stated, the feature apply to Ribo-seq.
• countRFP : raw counts of Ribo-seq
• fpkmRFP : FPKM
• fpkmRNA : FPKM of RNA-seq
• te : Translation efficiency Ribo-seq / RNA-seq FPKM
• floss : Fragment length similarity score
• entropyRFP : Positional entropy
• disengagementScores : downstream coverage from ORF
• RRS: Ribosome release score
• RSS: Ribosome staling score
• ORFScores: Periodicity score, does frame 0 have more reads
• ioScore: inside outside score: coverage ORF / coverage rest of transcript
• startCodonCoverage: Coverage over start codon + 2nt before start codon
• startRegionCoverage: Coverage over codon 2 & 3
• startRegionRelative: Peakness of TIS, startCodonCoverage / startRegionCoverage, 0-n
** Sequence features **
• kozak : Similarity to kozak sequence for organism score, 0-1
• gc : GC percentage, 0-1
• StartCodons : Start codon as a string, "ATG"
• StopCodons : stop codon as a string, "TAA"
• fractionLengths : ORF length compared to transcript, 0-1
** uORF features **
• distORFCDS : Distance from ORF stop site to CDS, -n:n
• inFrameCDS : Is ORF in frame with downstream CDS, T/F
• isOverlappingCds : Is ORF overlapping with downstream CDS, T/F
• rankInTx : ORF with most upstream start codon is 1, 1-n
computeFeatures 43
Usage
computeFeatures(
grl,
RFP,
RNA = NULL,
Gtf,
faFile = NULL,
riboStart = 26,
riboStop = 34,
sequenceFeatures = TRUE,
uorfFeatures = TRUE,
grl.is.sorted = FALSE,
weight.RFP = 1L,
weight.RNA = 1L
)
Arguments
grl a GRangesList object with usually ORFs, but can also be either leaders, cds’,
3’ utrs, etc. This is the regions you want to score.
RFP RiboSeq reads as GAlignments , GRanges or GRangesList object
RNA RnaSeq reads as GAlignments , GRanges or GRangesList object
Gtf a TxDb object of a gtf file or path to gtf, gff .sqlite etc.
faFile a path to fasta indexed genome, an open FaFile, a BSgenome, or path to ORFik
experiment with valid genome.
riboStart usually 26, the start of the floss interval, see ?floss
riboStop usually 34, the end of the floss interval
sequenceFeatures
a logical, default TRUE, include all sequence features, that is: Kozak, fraction-
Lengths, distORFCDS, isInFrame, isOverlapping and rankInTx. uorfFeatures =
FALSE will remove the 4 last.
uorfFeatures a logical, default TRUE, include all uORF sequence features, that is: distOR-
FCDS, isInFrame, isOverlapping and rankInTx
grl.is.sorted logical (F), a speed up if you know argument grl is sorted, set this to TRUE.
weight.RFP a vector (default: 1L). Can also be character name of column in RFP. As in trans-
lationalEff(weight = "score") for: GRanges("chr1", 1, "+", score = 5), would
mean score column tells that this alignment region was found 5 times.
weight.RNA Same as weightRFP but for RNA weights. (default: 1L)
Details
If you used CageSeq to reannotate your leaders, your txDB object must contain the reassigned
leaders. Use [reassignTxDbByCage()] to get the txdb.
As a note the library is reduced to only reads overlapping ’tx’, so the library size in fpkm calculation
is done on this subset. This will help remove rRNA and other contaminants.
Also if you have only unique reads with a weight column, explaining the number of duplicated
reads, set weights to make calculations correct. See getWeights
44 computeFeaturesCage
Value
a data.table with scores, each column is one score type, name of columns are the names of the
scores, i.g [floss()] or [fpkm()]
See Also
Other features: computeFeaturesCage(), countOverlapsW(), disengagementScore(), distToCds(),
distToTSS(), entropy(), floss(), fpkm(), fpkm_calc(), fractionLength(), initiationScore(),
insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(), orfScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
# Here we make an example from scratch
# Usually the ORFs are found in orfik, which makes names for you etc.
gtf <- system.file("extdata/Danio_rerio_sample", "annotations.gtf",
package = "ORFik") ## location of the gtf file
suppressWarnings(txdb <- loadTxdb(gtf))
# use cds' as ORFs for this example
ORFs <- loadRegion(txdb, "cds")
ORFs <- makeORFNames(ORFs) # need ORF names
# make Ribo-seq data,
RFP <- unlistGrl(firstExonPerGroup(ORFs))
computeFeatures(ORFs, RFP, Gtf = txdb)
# For more details see vignettes.
computeFeaturesCage Get all main features in ORFik
Description
If you have a txdb with correctly reassigned transcripts, use: [computeFeatures()]
Usage
computeFeaturesCage(
grl,
RFP,
RNA = NULL,
Gtf = NULL,
tx = NULL,
fiveUTRs = NULL,
cds = NULL,
threeUTRs = NULL,
faFile = NULL,
computeFeaturesCage 45
riboStart = 26,
riboStop = 34,
sequenceFeatures = TRUE,
uorfFeatures = TRUE,
grl.is.sorted = FALSE,
weight.RFP = 1L,
weight.RNA = 1L
)
Arguments
grl a GRangesList object with usually ORFs, but can also be either leaders, cds’,
3’ utrs, etc. This is the regions you want to score.
RFP RiboSeq reads as GAlignments , GRanges or GRangesList object
RNA RnaSeq reads as GAlignments , GRanges or GRangesList object
Gtf a TxDb object of a gtf file or path to gtf, gff .sqlite etc.
tx a GRangesList of transcripts, normally called from: exonsBy(Gtf, by = "tx",
use.names = T) only add this if you are not including Gtf file If you are using
CAGE, you do not need to reassign these to the cage peaks, it will do it for you.
fiveUTRs fiveUTRs as GRangesList, if you used cage-data to extend 5’ utrs, remember to
input CAGE assigned version and not original!
cds a GRangesList of coding sequences
threeUTRs a GRangesList of transcript 3’ utrs, normally called from: threeUTRsByTran-
script(Gtf, use.names = T)
faFile a path to fasta indexed genome, an open FaFile, a BSgenome, or path to ORFik
experiment with valid genome.
riboStart usually 26, the start of the floss interval, see ?floss
riboStop usually 34, the end of the floss interval
sequenceFeatures
a logical, default TRUE, include all sequence features, that is: Kozak, fraction-
Lengths, distORFCDS, isInFrame, isOverlapping and rankInTx. uorfFeatures =
FALSE will remove the 4 last.
uorfFeatures a logical, default TRUE, include all uORF sequence features, that is: distOR-
FCDS, isInFrame, isOverlapping and rankInTx
grl.is.sorted logical (F), a speed up if you know argument grl is sorted, set this to TRUE.
weight.RFP a vector (default: 1L). Can also be character name of column in RFP. As in trans-
lationalEff(weight = "score") for: GRanges("chr1", 1, "+", score = 5), would
mean score column tells that this alignment region was found 5 times.
weight.RNA Same as weightRFP but for RNA weights. (default: 1L)
Details
A specialized version if you don’t have a correct txdb, for example with CAGE reassigned leaders
while txdb is not updated. It is 2x faster for tested data. The point of this function is to give you the
ability to input transcript etc directly into the function, and not load them from txdb. Each feature
have a link to an article describing feature, try ?floss
46 computeFeaturesCage
Value
a data.table with scores, each column is one score type, name of columns are the names of the
scores, i.g [floss()] or [fpkm()]
See Also
Other features: computeFeatures(), countOverlapsW(), disengagementScore(), distToCds(),
distToTSS(), entropy(), floss(), fpkm(), fpkm_calc(), fractionLength(), initiationScore(),
insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(), orfScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
# a small example without cage-seq data:
# we will find ORFs in the 5' utrs
# and then calculate features on them
if (requireNamespace("BSgenome.Hsapiens.UCSC.hg19")) {
library(GenomicFeatures)
# Get the gtf txdb file
txdbFile <- system.file("extdata", "hg19_knownGene_sample.sqlite",
package = "GenomicFeatures")
txdb <- loadDb(txdbFile)
# Extract sequences of fiveUTRs.
fiveUTRs <- fiveUTRsByTranscript(txdb, use.names = TRUE)[1:10]
faFile <- BSgenome.Hsapiens.UCSC.hg19::Hsapiens
tx_seqs <- extractTranscriptSeqs(faFile, fiveUTRs)
# Find all ORFs on those transcripts and get their genomic coordinates
fiveUTR_ORFs <- findMapORFs(fiveUTRs, tx_seqs)
unlistedORFs <- unlistGrl(fiveUTR_ORFs)
# group GRanges by ORFs instead of Transcripts
fiveUTR_ORFs <- groupGRangesBy(unlistedORFs, unlistedORFs$names)
# make some toy ribo seq and rna seq data
starts <- unlistGrl(ORFik:::firstExonPerGroup(fiveUTR_ORFs))
RFP <- promoters(starts, upstream = 0, downstream = 1)
score(RFP) <- rep(29, length(RFP)) # the original read widths
# set RNA seq to duplicate transcripts
RNA <- unlistGrl(exonsBy(txdb, by = "tx", use.names = TRUE))
#ORFik:::computeFeaturesCage(grl = fiveUTR_ORFs, RFP = RFP,
# RNA = RNA, Gtf = txdb, faFile = faFile)
}
# See vignettes for more examples
conditionNames 47
conditionNames Get condition name variants
Description
Used to standardize nomeclature for experiments.
Example: WT is main naming, but a variant is control control will then be renamed to WT
Usage
conditionNames()
Value
a data.table with 2 columns, the main name, and all name variants of the main name in second
column as a list.
See Also
Other experiment_naming: batchNames(), cellLineNames(), cellTypeNames(), fractionNames(),
inhibitorNames(), libNames(), mainNames(), repNames(), stageNames(), tissueNames()
config Read directory config for ORFik experiments
Description
Defines a folder for: 1. fastq files (raw data)
2. bam files (processed data)
3. references (organism annotation and STAR index)
4. experiments (Location to store and load all experiment .csv files) Update or use another config
using config.save() function.
Usage
config(
file = config_file(old_config_location = old_config_location),
old_config_location = "~/Bio_data/ORFik_config.csv"
)
Arguments
file location of config csv, default: config_file(old_config_location = old_config_location)
old_config_location
path, old config location before BiocFileCache implementation. Will copy this
to cache directory and delete old version. This is done to follow bioc rules on
not writing to user home directory.
48 config.exper
Value
a named character vector of length 3
Examples
## Make with default config path
#config()
config.exper Set directories for experiment
Description
Defines a folder for: 1. fastq files (raw_data)
2. bam files (processed data)
3. references (organism annotation and STAR index)
4. Experiment (name of experiment)
Usage
config.exper(experiment, assembly, type, config = ORFik::config())
Arguments
experiment short name of experiment (must be valid as a folder name)
assembly name of organism and assembly (must be valid as a folder name)
type name of sequencing type, Ribo-seq, RNA-seq, CAGE.. Can be more than one.
config a named character vector of length 3, default: ORFik::config()
Value
named character vector of paths for experiment
Examples
## Save to default config location
#config.exper("Alexaki_Human", "Homo_sapiens_GRCh38_101", c("Ribo-seq", "RNA-seq"))
config.save 49
config.save Save/update directory config for ORFik experiments
Description
Defines a folder for fastq files (raw_data), bam files (processed data) and references (organism
annotation and STAR index)
Usage
config.save(
file = config_file(),
fastq.dir = file.path(base.dir, "raw_data"),
bam.dir = file.path(base.dir, "processed_data"),
reference.dir = file.path(base.dir, "references"),
exp.dir = file.path(base.dir, "ORFik_experiments/"),
base.dir = "~/Bio_data",
conf = data.frame(type = c("fastq", "bam", "ref", "exp"), directory = c(fastq.dir,
bam.dir, reference.dir, exp.dir))
)
Arguments
file location of config csv, default: config_file(old_config_location = old_config_location)
fastq.dir directory where ORFik puts fastq file directories, default: file.path(base.dir,
"raw_data"), which is retrieved with: config()["fastq"]
bam.dir directory where ORFik puts bam file directories, default: file.path(base.dir, "pro-
cessed_data"), which is retrieved with: config()["bam"]
reference.dir directory where ORFik puts reference file directories, default: file.path(base.dir,
"references"), which is retrieved with: config()["ref"]
exp.dir directory where ORFik puts experiment csv files, default: file.path(base.dir,
"ORFik_experiments/"), which is retrieved with: config()["exp"]
base.dir base directory for all output directories, default: "~/Bio_data"
conf data.frame of complete conf object, default: data.frame(type = c("fastq", "bam",
"ref", "exp"), directory = c(fastq.dir, bam.dir, reference.dir, exp.dir))
Value
invisible(NULL), file saved to disc
Examples
# Overwrite default config, with new base directory for files
#config.save(base.dir = "/media/Bio_data/") # Output files go here instead
# of ~/Bio_data
## Dont do this, but for understanding here is how to make a second config
50 config_file
#new_config_path <- config_file(query = "ORFik_config_2")
#config.save(new_config_path, "/media/Bio_data/raw_data/",
# "/media/Bio_data/processed_data", /media/Bio_data/references/)
config_file Get path for ORFik config in cache
Description
Get path for ORFik config in cache
Usage
config_file(
cache = BiocFileCache::getBFCOption("CACHE"),
query = "ORFik_config",
ask = interactive(),
old_config_location = "~/Bio_data/ORFik_config.csv"
)
Arguments
cache path to bioc cache directory with rname from query argument. Default is:
BiocFileCache::getBFCOption("CACHE") For info, see: [BiocFileCache::BiocFileCache()]
query default: "ORFik_config". Exact rname of the file in cache.
ask logical, default interactive().
old_config_location
path, old config location before BiocFileCache implementation. Will copy this
to cache directory and delete old version. This is done to follow bioc rules on
not writing to user home directory.
Value
a file path in cache
Examples
config_file()
# Another config path
config_file(query = "ORFik_config_2")
convertLibs 51
convertLibs Converted format of NGS libraries
Description
Export as either .ofst, .wig, .bigWig,.bedo (legacy format) or .bedoc (legacy format) files:
Export files as .ofst for fastest load speed into R.
Export files as .wig / bigWig for use in IGV or other genome browsers.
The input files are checked if they exist from: envExp(df).
Usage
convertLibs(
df,
out.dir = libFolder(df),
addScoreColumn = TRUE,
addSizeColumn = TRUE,
must.overlap = NULL,
method = "None",
type = "ofst",
input.type = "ofst",
reassign.when.saving = FALSE,
envir = envExp(df),
BPPARAM = bpparam()
)
Arguments
df an ORFik experiment
out.dir optional output directory, default: libFolder(df), if it is NULL, it will just reas-
sign R objects to simplified libraries. Will then create a final folder specfied as:
paste0(out.dir, "/", type, "/"). Here the files will be saved in format given by the
type argument.
addScoreColumn logical, default TRUE, if FALSE will not add replicate numbers as score col-
umn, see ORFik::convertToOneBasedRanges.
addSizeColumn logical, default TRUE, if FALSE will not add size (width) as size column, see
ORFik::convertToOneBasedRanges. Does not apply for (GAlignment version
of.ofst) or .bedoc. Since they contain the original cigar.
must.overlap default (NULL), else a GRanges / GRangesList object, so only reads that over-
lap (must.overlap) are kept. This is useful when you only need the reads over
transcript annotation or subset etc.
method character, default "None", the method to reduce ranges, for more info see convertToOneBasedRanges
type character, output format, default "ofst". Alternatives: "ofst", "bigWig", "wig","bedo"
or "bedoc". Which format you want. Will make a folder within out.dir with this
name containing the files.
52 convertToOneBasedRanges
input.type character, input type "ofst". Remember this function uses the loaded libraries if
existing, so this argument is usually ignored. Only used if files do not already
exist.
reassign.when.saving
logical, default FALSE. If TRUE, will reassign library to converted form after
saving. Ignored when out.dir = NULL.
envir environment to save to, default envExp(df), which defaults to .GlobalEnv, but
can be set with envExp(df) <- new.env() etc.
BPPARAM how many cores/threads to use? default: bpparam(). To see number of threads
used, do bpparam()$workers. You can also add a time remaining bar, for a
more detailed pipeline.
Details
We advice you to not use this directly, as other function are more safe for library type conversions.
See family description below. This is mostly used internally in ORFik. It is only adviced to use if
large bam files are already loaded in R and conversions are wanted from those.
See export.ofst, export.wiggle, export.bedo and export.bedoc for information on file for-
mats.
If libraries of the experiment are already loaded into environment (default: .globalEnv) is will ex-
port using those files as templates. If they are not in environment the .ofst files from the bam files
are loaded (unless you are converting to .ofst then the .bam files are loaded).
Value
NULL (saves files to disc or R .GlobalEnv)
See Also
Other lib_converters: convert_bam_to_ofst(), convert_to_bigWig(), convert_to_covRle(),
convert_to_covRleList()
Examples
df <- ORFik.template.experiment()
#convertLibs(df)
# Keep only 5' ends of reads
#convertLibs(df, method = "5prime")
convertToOneBasedRanges
Convert a GRanges Object to 1 width reads
convertToOneBasedRanges 53
Description
There are 5 ways of doing this
1. Take 5’ ends, reduce away rest (5prime)
2. Take 3’ ends, reduce away rest (3prime)
3. Tile to 1-mers and include all (tileAll)
4. Take middle point per GRanges (middle)
5. Get original with metacolumns (None)
You can also do multiple at a time, then output is GRangesList, where each list group is the operation
(5prime is [1], 3prime is [2] etc)
Many other ways to do this have their own functions, like startSites and stopSites etc. To retain
information on original width, set addSizeColumn to TRUE. To compress data, 1 GRanges object
per unique read, set addScoreColumn to TRUE. This will give you a score column with how many
duplicated reads there were in the specified region.
Usage
convertToOneBasedRanges(
gr,
method = "5prime",
addScoreColumn = FALSE,
addSizeColumn = FALSE,
after.softclips = TRUE,
along.reference = FALSE,
reuse.score.column = TRUE
)
Arguments
gr GRanges, GAlignment or GAlignmentPairs object to reduce.
method character, default "5prime", the method to reduce ranges, see NOTE for more
info.
addScoreColumn logical (FALSE), if TRUE, add a score column that sums up the hits per unique
range. This will make each read unique, so that each read is 1 time, and score
column gives the number of collapsed hits. A useful compression. If add-
SizeColumn is FALSE, it will not differentiate between reads with same start
and stop, but different length. If addSizeColumn is FALSE, it will remove it.
Collapses after conversion.
addSizeColumn logical (FALSE), if TRUE, add a size column that for each read, that gives orig-
inal width of read. Useful if you need original read lengths. This takes care of
soft clips etc. If collapsing reads, each unique range will be grouped also by
size.
after.softclips
logical (TRUE), include softclips in width. Does not apply if along.reference is
TRUE.
along.reference
logical (FALSE), example: The cigar "26MI2" is by default width 28, but if
along.reference is TRUE, it will be 26. The length of the read along the refer-
54 convert_bam_to_ofst
ence. Also "1D20M" will be 21 if by along.reference is TRUE. Intronic regions
(cigar: N) will be removed. So: "1M200N19M" is 20, not 220.
reuse.score.column
logical (TRUE), if addScoreColumn is TRUE, and a score column exists, will
sum up the scores to create a new score. If FALSE, will skip old score column
and create new according to number of replicated reads after conversion. If
addScoreColumn is FALSE, this argument is ignored.
Details
NOTE: Note: For cigar based ranges (GAlignments), the 5’ end is the first non clipped base (neither
soft clipped or hard clipped from 5’). This is following the default of bioconductor. For special case
of GAlignmentPairs, 5prime will only use left (first) 5’ end and read and 3prime will use only right
(last) 3’ end of read in pair. tileAll and middle can possibly find poinst that are not in the reads
since: lets say pair is 1-5 and 10-15, middle is 7, which is not in the read.
Value
Converted GRanges object
See Also
Other utils: bedToGR(), export.bed12(), export.bigWig(), export.fstwig(), export.wiggle(),
fimport(), findFa(), fread.bed(), optimizeReads(), readBam(), readBigWig(), readWig()
Examples
gr <- GRanges("chr1", 1:10,"+")
# 5 prime ends
convertToOneBasedRanges(gr)
# is equal to convertToOneBasedRanges(gr, method = "5prime")
# 3 prime ends
convertToOneBasedRanges(gr, method = "3prime")
# With lengths
convertToOneBasedRanges(gr, addSizeColumn = TRUE)
# With score (# of replicates)
gr <- rep(gr, 2)
convertToOneBasedRanges(gr, addSizeColumn = TRUE, addScoreColumn = TRUE)
convert_bam_to_ofst Convert libraries to ofst
Description
Saved by default in folder "ofst" relative to default libraries of experiment. Speeds up loading of
full files compared to bam by large margins.
convert_bam_to_ofst 55
Usage
convert_bam_to_ofst(
df,
in_files = filepath(df, "default"),
out_dir = file.path(libFolder(df), "ofst"),
verbose = TRUE,
strandMode = rep(0, length(in_files))
)
Arguments
df an ORFik experiment, or NULL is allowed if both in_files and out_dir is spec-
ified manually.
in_files paths to input files, default: filepath(df, "default") with bam format files.
out_dir paths to output files, default file.path(libFolder(df), "cov_RLE").
verbose logical, default TRUE, message about library output status.
strandMode numeric, default 0. Only used for paired end bam files. One of (0: strand
= *, 1: first read of pair is +, 2: first read of pair is -). See ?strandMode.
Note: Sets default to 0 instead of 1, as readGAlignmentPairs uses 1. This is
to guarantee hits, but will also make mismatches of overlapping transcripts in
opposite directions.
Details
If you want to keep bam files loaded or faster conversion if you already have them loaded, use
ORFik::convertLibs instead
Value
invisible(NULL), files saved to disc
See Also
Other lib_converters: convertLibs(), convert_to_bigWig(), convert_to_covRle(), convert_to_covRleList()
Examples
df <- ORFik.template.experiment.zf()
## Usually do default folder, here we use tmpdir
folder_to_save <- file.path(tempdir(), "ofst")
convert_bam_to_ofst(df, out_dir = folder_to_save)
fimport(file.path(folder_to_save, "ribo-seq.ofst"))
56 convert_to_bigWig
convert_to_bigWig Convert to BigWig
Description
Convert to BigWig
Usage
convert_to_bigWig(
df,
in_files = filepath(df, "pshifted"),
out_dir = file.path(libFolder(df), "bigwig"),
split.by.strand = TRUE,
split.by.readlength = FALSE,
seq_info = seqinfo(df),
weight = "score",
is_pre_collapsed = FALSE,
verbose = TRUE
)
Arguments
df an ORFik experiment, or NULL is allowed if both in_files and out_dir is spec-
ified manually.
in_files paths to input files, default pshifted files: filepath(df, "pshifted") in ofst
format
out_dir paths to output files, default file.path(libFolder(df), "bigwig").
split.by.strand
logical, default TRUE, split into forward and reverse strand RleList inside cov-
Rle object.
split.by.readlength
logical, default FALSE, split into files for each readlength, defined by read-
Widths(x) for each file.
seq_info SeqInfo object, default seqinfo(findFa(df))
weight integer, numeric or single length character. Default "score". Use score column
in loaded in_files.
is_pre_collapsed
logical, default FALSE. Have you already collapsed reads with collapse.by.scores,
so each positions is only in 1 GRanges object with a score column per readlength?
Set to TRUE, only if you are sure, will give a speedup.
verbose logical, default TRUE, message about library output status.
Value
invisible(NULL), files saved to disc
convert_to_covRle 57
See Also
Other lib_converters: convertLibs(), convert_bam_to_ofst(), convert_to_covRle(), convert_to_covRleList()
Examples
df <- ORFik.template.experiment()[10,]
## Usually do default folder, here we use tmpdir
folder_to_save <- file.path(tempdir(), "bigwig")
convert_to_bigWig(df, out_dir = folder_to_save)
fimport(file.path(folder_to_save, c("RFP_Mutant_rep2_forward.bigWig",
"RFP_Mutant_rep2_reverse.bigWig")))
convert_to_covRle Convert libraries to covRle
Description
Saved by default in folder "cov_RLE" relative to default libraries of experiment
Usage
convert_to_covRle(
df,
in_files = filepath(df, "pshifted"),
out_dir = file.path(libFolder(df), "cov_RLE"),
split.by.strand = TRUE,
split.by.readlength = FALSE,
seq_info = seqinfo(df),
weight = "score",
verbose = TRUE
)
Arguments
df an ORFik experiment, or NULL is allowed if both in_files and out_dir is spec-
ified manually.
in_files paths to input files, default pshifted files: filepath(df, "pshifted") in ofst
format
out_dir paths to output files, default file.path(libFolder(df), "cov_RLE").
split.by.strand
logical, default TRUE, split into forward and reverse strand RleList inside cov-
Rle object.
split.by.readlength
logical, default FALSE, split into files for each readlength, defined by read-
Widths(x) for each file.
seq_info SeqInfo object, default seqinfo(findFa(df))
58 convert_to_covRleList
weight integer, numeric or single length character. Default "score". Use score column
in loaded in_files.
verbose logical, default TRUE, message about library output status.
Value
invisible(NULL), files saved to disc
See Also
Other lib_converters: convertLibs(), convert_bam_to_ofst(), convert_to_bigWig(), convert_to_covRleList()
Examples
df <- ORFik.template.experiment()[10,]
## Usually do default folder, here we use tmpdir
folder_to_save <- file.path(tempdir(), "cov_RLE")
convert_to_covRle(df, out_dir = folder_to_save)
fimport(file.path(folder_to_save, "RFP_Mutant_rep2.covrds"))
convert_to_covRleList Convert libraries to covRleList objects
Description
Useful to store reads separated by readlength, for much faster coverage calculation. Saved by
default in folder "cov_RLE_List" relative to default libraries of experiment
Usage
convert_to_covRleList(
df,
in_files = filepath(df, "pshifted"),
out_dir = file.path(libFolder(df), "cov_RLE_List"),
out_dir_merged = file.path(libFolder(df), "cov_RLE"),
split.by.strand = TRUE,
seq_info = seqinfo(df),
weight = "score",
verbose = TRUE
)
Arguments
df an ORFik experiment, or NULL is allowed if both in_files and out_dir is spec-
ified manually.
in_files paths to input files, default pshifted files: filepath(df, "pshifted") in ofst
format
convert_to_fstWig 59
out_dir paths to output files, default file.path(libFolder(df), "cov_RLE_List").
out_dir_merged character vector of paths, default: file.path(libFolder(df), "cov_RLE").
Paths to merged output files, Set to NULL to skip making merged covRle.
split.by.strand
logical, default TRUE, split into forward and reverse strand RleList inside cov-
Rle object.
seq_info SeqInfo object, default seqinfo(findFa(df))
weight integer, numeric or single length character. Default "score". Use score column
in loaded in_files.
verbose logical, default TRUE, message about library output status.
Value
invisible(NULL), files saved to disc
See Also
Other lib_converters: convertLibs(), convert_bam_to_ofst(), convert_to_bigWig(), convert_to_covRle()
Examples
df <- ORFik.template.experiment()[10,]
## Usually do default folder, here we use tmpdir
folder_to_save <- file.path(tempdir(), "cov_RLE_List")
folder_to_save_merged <- file.path(tempdir(), "cov_RLE")
ORFik:::convert_to_covRleList(df, out_dir = folder_to_save,
out_dir_merged = folder_to_save_merged)
fimport(file.path(folder_to_save, "RFP_Mutant_rep2.covrds"))
convert_to_fstWig Convert to fstwig
Description
Will split files by chromosome for faster loading for now. This feature might change in the future!
Usage
convert_to_fstWig(
df,
in_files = filepath(df, "pshifted"),
out_dir = file.path(libFolder(df), "fstwig"),
split.by.strand = TRUE,
split.by.readlength = FALSE,
seq_info = seqinfo(df),
weight = "score",
is_pre_collapsed = FALSE,
verbose = TRUE
)
60 correlation.plots
Arguments
df an ORFik experiment, or NULL is allowed if both in_files and out_dir is spec-
ified manually.
in_files paths to input files, default pshifted files: filepath(df, "pshifted") in ofst
format
out_dir paths to output files, default file.path(libFolder(df), "bigwig").
split.by.strand
logical, default TRUE, split into forward and reverse strand RleList inside cov-
Rle object.
split.by.readlength
logical, default FALSE, split into files for each readlength, defined by read-
Widths(x) for each file.
seq_info SeqInfo object, default seqinfo(findFa(df))
weight integer, numeric or single length character. Default "score". Use score column
in loaded in_files.
is_pre_collapsed
logical, default FALSE. Have you already collapsed reads with collapse.by.scores,
so each positions is only in 1 GRanges object with a score column per readlength?
Set to TRUE, only if you are sure, will give a speedup.
verbose logical, default TRUE, message about library output status.
Value
invisible(NULL), files saved to disc
correlation.plots Correlation plots between all samples
Description
Get correlation plot of raw counts and/or log2(count + 1) over selected region in: c("mrna", "lead-
ers", "cds", "trailers")
Note on correlation: Pearson correlation, using pairwise observations to fill in NA values for the
covariance matrix.
Usage
correlation.plots(
df,
output.dir,
region = "mrna",
type = "fpkm",
height = 400,
correlation.plots 61
width = 400,
size = 0.15,
plot.ext = ".pdf",
complex.correlation.plots = TRUE,
data_for_pairs = countTable(df, region, type = type),
as_gg_list = FALSE,
text_size = 4,
method = c("pearson", "spearman")[1]
)
Arguments
df an ORFik experiment
output.dir directory to save to, named : cor_plot, cor_plot_log2 and/or cor_plot_simple
with either .pdf or .png
region a character (default: mrna), make raw count matrices of whole mrnas or one of
(leaders, cds, trailers)
type which value to use, "fpkm", alternative "counts".
height numeric, default 400 (in mm)
width numeric, default 400 (in mm)
size numeric, size of dots, default 0.15. Deprecated.
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg".
complex.correlation.plots
logical, default TRUE. Add in addition to simple correlation plot two compu-
tationally heavy dots + correlation plots. Useful for deeper analysis, but takes
longer time to run, especially on low-quality gpu computers. Set to FALSE to
skip these.
data_for_pairs a data.table from ORFik::countTable of counts wanted. Default is fpkm of all
mRNA counts over all libraries.
as_gg_list logical, default FALSE. Return as a list of ggplot objects instead of as a grob.
Gives you the ability to modify plots more directly.
text_size size of correlation numbers
method c("pearson", "spearman")[1]
Value
invisible(NULL) / if as_gg_list is TRUE, return a list of raw plots.
62 cor_plot
cor_plot Get correlation between columns
Description
Get correlation between columns
Usage
cor_plot(
dt_cor,
col = c(low = "blue", high = "red", mid = "white", na.value = "white"),
limit = c(ifelse(min(dt_cor$Cor, na.rm = TRUE) < 0, -1, 0), 1),
midpoint = mean(limit),
label_name = "Pearson\nCorrelation",
text_size = 4,
legend.position = c(0.4, 0.7),
legend.direction = "horizontal"
)
Arguments
dt_cor a data.table, with column Cor
col colors c(low = "blue", high = "red", mid = "white", na.value = "white")
limit default (-1, 1), defined by: c(ifelse(min(dt_cor$Cor, na.rm = TRUE) < 0,
-1, 0), 1)
midpoint midpoint of correlation values in label coloring.
label_name name of correlation method, default "Pearson Correlation" with newline af-
ter Pearson.
text_size size of correlation numbers
legend.position
default c(0.4, 0.7), other: "top", "right",..
legend.direction
default "horizontal", or "vertical"
Value
a ggplot (heatmap)
cor_table 63
cor_table Get correlation between columns
Description
Get correlation between columns
Usage
cor_table(
dt,
method = c("pearson", "spearman")[1],
upper_triangle = TRUE,
decimals = 2,
melt = TRUE,
na.rm.melt = TRUE
)
Arguments
dt a data.table
method c("pearson", "spearman")[1]
upper_triangle logical, default TRUE. Make lower triangle values NA.
decimals numeric, default 2. How many decimals for correlation
melt logical, default TRUE.
na.rm.melt logical, default TRUE. Remove NA values from melted table.
Value
a data.table with 3 columns, Var1, Var2 and Cor
countOverlapsW CountOverlaps with weights
Description
Similar to countOverlaps, but takes an optional weight column. This is usually the score column
Usage
countOverlapsW(query, subject, weight = NULL, ...)
64 countTable
Arguments
query IRanges, IRangesList, GRanges, GRangesList object. Usually transcript a tran-
script region.
subject GRanges, GRangesList, GAlignment, usually reads.
weight (default: NULL), if defined either numeric or character name of valid meta col-
umn in subject. If weight is single numeric, it is used for all. A normall weight
is the score column given as weight = "score". GRanges("chr1", 1, "+", score =
5), would mean score column tells that this alignment region was found 5 times.
... additional arguments passed to countOverlaps/findOverlaps
Value
a named vector of number of overlaps to subject weigthed by ’weight’ column.
See Also
Other features: computeFeatures(), computeFeaturesCage(), disengagementScore(), distToCds(),
distToTSS(), entropy(), floss(), fpkm(), fpkm_calc(), fractionLength(), initiationScore(),
insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(), orfScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
gr1 <- GRanges(seqnames="chr1",
ranges=IRanges(start = c(4, 9, 10, 30),
end = c(4, 15, 20, 31)),
strand="+")
gr2 <- GRanges(seqnames="chr1",
ranges=IRanges(start = c(1, 4, 15, 25),
end = c(2, 4, 20, 26)),
strand=c("+"),
score=c(10, 20, 15, 5))
countOverlaps(gr1, gr2)
countOverlapsW(gr1, gr2, weight = "score")
countTable Extract count table directly from experiment
Description
Used to quickly load pre-created read count tables to R.
If df is experiment: Extracts by getting /QC_STATS directory, and searching for region Requires
ORFikQC to have been run on experiment, to get default count tables!
countTable 65
Usage
countTable(
df,
region = "mrna",
type = "count",
collapse = FALSE,
count.folder = "default"
)
Arguments
df an ORFik experiment or path to folder with countTable, use path if not same
folder as experiment libraries. Will subset to the count tables specified if df is
experiment. If experiment has 4 rows and you subset it to only 2, then only those
2 count tables will be outputted.
region a character vector (default: "mrna"), make raw count matrices of whole mrnas
or one of (leaders, cds, trailers).
type character, default: "count" (raw counts matrix). Which object type and normal-
ization do you want ? "summarized" (SummarizedExperiment object), "deseq"
(Deseq2 experiment, design will be all valid non-unique columns except repli-
cates, change by using DESeq2::design, normalization alternatives are: "fpkm",
"log2fpkm" or "log10fpkm".
collapse a logical/character (default FALSE), if TRUE all samples within the group SAM-
PLE will be collapsed to one. If "all", all groups will be merged into 1 col-
umn called merged_all. Collapse is defined as rowSum(elements_per_group) /
ncol(elements_per_group)
count.folder character, default "auto" (Use count tables from original bam files stored in
"QC_STATS", these are like HTseq count tables). To load your custome count
tables from pshifted reads, set to "pshifted" (remember to create the pshifted ta-
bles first!). If you have custom ranges, like reads over uORFs stored in a folder
called "/uORFs" relative to the bam files, set to "uORFs". Always create these
custom count tables with makeSummarizedExperimentFromBam. Always make
the location of the folder directly inside the bam file directory!
Details
If df is path to folder: Loads the the file in that directory with the regex region.rds, where region
is what is defined by argument, if multiple exist, see if any start with "countTable_", if so, subset.
If loaded as SummarizedExperiment or deseq, the colData will be made from ORFik.experiment
information.
Value
a data.table/SummarizedExperiment/DESeq object of columns as counts / normalized counts per
library, column name is name of library. Rownames must be unique for now. Might change.
66 countTable_regions
See Also
Other countTable: countTable_regions()
Examples
# Make experiment
df <- ORFik.template.experiment()
# Make QC report to get counts ++ (not needed for this template)
# ORFikQC(df)
# Get count Table of mrnas
# countTable(df, "mrna")
# Get count Table of cds
# countTable(df, "cds")
# Get count Table of mrnas as fpkm values
# countTable(df, "mrna", type = "count")
# Get count Table of mrnas with collapsed replicates
# countTable(df, "mrna", collapse = TRUE)
# Get count Table of mrnas as summarizedExperiment
# countTable(df, "mrna", type = "summarized")
# Get count Table of mrnas as DESeq2 object,
# for differential expression analysis
# countTable(df, "mrna", type = "deseq")
countTable_regions Make a list of count matrices from experiment
Description
By default will make count tables over mRNA, leaders, cds and trailers for all libraries in experi-
ment. region
Usage
countTable_regions(
df,
out.dir = libFolder(df),
longestPerGene = FALSE,
geneOrTxNames = "tx",
regions = c("mrna", "leaders", "cds", "trailers"),
type = "count",
lib.type = "ofst",
weight = "score",
rel.dir = "QC_STATS",
forceRemake = FALSE,
BPPARAM = bpparam()
)
countTable_regions 67
Arguments
df an ORFik experiment
out.dir optional output directory, default: resFolder(df). Will make a folder within
this called "QC_STATS" with all results in this directory. Warning: If you assign
not default path, you will have a hazzle to load files later. Much easier to load
count tables, statistics, ++ later with default. Update resFolder of df instead if
needed.
longestPerGene a logical (default FALSE), if FALSE all transcript isoforms per gene. Ignored if
"region" is not a character of either: "mRNA","tx", "cds", "leaders" or "trailers".
geneOrTxNames a character vector (default "tx"), should row names keep trancript names ("tx")
or change to gene names ("gene")
regions a character vector, default: c("mrna", "leaders", "cds", "trailers"), make raw
count matrices of whole regions specified. Can also be a custom GRangesList
of for example uORFs or a subset of cds etc.
type default: "count" (raw counts matrix), alternative is "fpkm", "log2fpkm" or "log10fpkm"
lib.type a character(default: "default"), load files in experiment or some precomputed
variant, either "ofst", "bedo", "bedoc" or "pshifted". These are made with OR-
Fik:::convertLibs() or shiftFootprintsByExperiment(). Can also be custom user
made folders inside the experiments bam folder.
weight numeric or character, a column to score overlaps by. Default "score", will check
for a metacolumn called "score" in libraries. If not found, will not use weights.
rel.dir relative output directory for out.dir, default: "QC_STATS". For pshifted, write
"pshifted".
forceRemake logical, default FALSE. If TRUE, will not look for existing file count table files.
BPPARAM how many cores/threads to use? default: bpparam()
Value
a list of data.table, 1 data.table per region. The regions will be the names the list elements.
See Also
Other countTable: countTable()
Examples
##Make experiment
df <- ORFik.template.experiment()
## Create count tables for all default regions
# countTable_regions(df)
## Pshifted reads (first create pshiftead libs)
# countTable_regions(df, lib.type = "pshifted", rel.dir = "pshifted")
68 coverageByTranscriptW
coverageByTranscriptC coverageByTranscript with coverage input
Description
Extends the function with direct genome coverage input, see coverageByTranscript for original
function.
Usage
coverageByTranscriptC(x, transcripts, ignore.strand = !strandMode(x))
Arguments
x a covRle (one RleList for each strand in object), must have defined and correct
seqlengths in its SeqInfo object.
transcripts GRangesList
ignore.strand a logical (default: length(x) == 1)
Value
Integer Rle of coverage, 1 per transcript
coverageByTranscriptW coverageByTranscript with weights
Description
Extends the function with weights, see coverageByTranscript for original function.
Usage
coverageByTranscriptW(
x,
transcripts,
ignore.strand = FALSE,
weight = 1L,
seqinfo.x.is.correct = FALSE
)
coverageGroupings 69
Arguments
x reads (GRanges, GAlignments)
transcripts GRangesList
ignore.strand a logical (default: FALSE)
weight a vector (default: 1L), if single number applies for all, else it must be the string
name of a defined meta column in "x", that gives number of times a read was
found. GRanges("chr1", 1, "+", score = 5), would mean score column tells that
this alignment was found 5 times.
seqinfo.x.is.correct
logical, default FALSE. If you know x, has correct seqinfo, then you can save
some computation time by setting this to TRUE.
Value
Integer Rle of coverage, 1 per transcript
coverageGroupings Get grouping for a coverage table in ORFik
Description
Either of two groupings: GF: Gene, fraction FGF: Fraction, position, feature It finds which of these
exists, and auto groups
Usage
coverageGroupings(logicals, grouping = "GF")
Arguments
logicals size 2 logical vector, the is.null checks for each column,
grouping which grouping to perform, default "GF" Gene & Fraction grouping. Alternative
"FGF", Fraction & position & feature.
Details
Normally not used directly!
Value
a quote of the grouping to pass to data.table
70 coverageHeatMap
coverageHeatMap Create a heatmap of coverage
Description
Creates a ggplot representing a heatmap of coverage:
• Rows : Position in region
• Columns : Read length
• Index intensity : (color) coverage scoring per index.
Coverage rows in heat map is fraction, usually fractions is divided into unique read lengths (standard
Illumina is 76 unique widths, with some minimum cutoff like 15.) Coverage column in heat map is
score, default zscore of counts. These are the relative positions you are plotting to. Like +/- relative
to TIS or TSS.
Usage
coverageHeatMap(
coverage,
output = NULL,
scoring = "zscore",
legendPos = "right",
addFracPlot = FALSE,
xlab = "Position relative to start site",
ylab = "Protected fragment length",
colors = "default",
title = NULL,
increments.y = "auto",
gradient.max = max(coverage$score)
)
Arguments
coverage a data.table, e.g. output of scaledWindowCoverage
output character string (NULL), if set, saves the plot as pdf or png to path given. If no
format is given, is save as pdf.
scoring character vector, default "zscore", Which scoring did you use to create? either
of zscore, transcriptNormalized, sum, mean, median, .. see ?coverageScorings
for info and more alternatives.
legendPos a character, Default "right". Where should the fill legend be ? ("top", "bottom",
"right", "left")
addFracPlot Add margin histogram plot on top of heatmap with fractions per positions
xlab the x-axis label, default "Position relative to start site"
coverageHeatMap 71
ylab the y-axis label, default "Protected fragment length"
colors character vector, default: "default", this gives you: c("white", "yellow2", "yel-
low3", "lightblue", "blue", "navy"), do "high" for more high contrasts, or specify
your own colors.
title a character, default NULL (no title), what is the top title of plot?
increments.y increments of y axis, default "auto". Or a numeric value < max position & >
min position.
gradient.max numeric, defualt: max(coverage$score). What data value should the top color be
? Good to use if you want to compare 2 samples, with the same color intensity,
in that case set this value to the max score of the 2 coverage tables.
Details
Colors: Remember if you want to change anything like colors, just return the ggplot object, and
reassign like: obj + scale_color_brewer() etc. Standard colors are:
• 0 reads in whole readlength :gray
• few reads in position :white
• medium reads in position :yellow
• many reads in position :dark blue
Value
a ggplot object of the coverage plot, NULL if output is set, then the plot will only be saved to
location.
See Also
Other heatmaps: heatMapL(), heatMapRegion(), heatMap_single()
Other coveragePlot: pSitePlot(), savePlot(), windowCoveragePlot()
Examples
# An ORF
grl <- GRangesList(tx1 = GRanges("1", IRanges(1, 6), "+"))
# Ribo-seq reads
range <- IRanges(c(rep(1, 3), 2, 3, rep(4, 2), 5, 6), width = 1 )
reads <- GRanges("1", range, "+")
reads$size <- c(rep(28, 5), rep(29, 4)) # read size
coverage <- windowPerReadLength(grl, reads = reads, upstream = 0,
downstream = 5)
coverageHeatMap(coverage)
# With top sum bar
coverageHeatMap(coverage, addFracPlot = TRUE)
# See vignette for more examples
72 coveragePerTiling
coveragePerTiling Get coverage per group
Description
It tiles each GRangesList group to width 1, and finds hits per position.
A range from 1:5 will split into c(1,2,3,4,5) and count hits on each. This is a safer speedup of
coverageByTranscript from GenomicFeatures. It also gives the possibility to return as data.table,
for faster computations.
Usage
coveragePerTiling(
grl,
reads,
is.sorted = FALSE,
keep.names = TRUE,
as.data.table = FALSE,
withFrames = FALSE,
weight = "score",
drop.zero.dt = FALSE,
fraction = NULL
)
Arguments
grl a GRangesList of 5’ utrs, CDS, transcripts, etc.
reads a GAlignments, GRanges, or precomputed coverage as covRle (one for each
strand) of RiboSeq, RnaSeq etc.
Weigths for scoring is default the ’score’ column in ’reads’. Can also be random
access paths to bigWig or fstwig file. Do not use random access for more than a
few genes, then loading the entire files is usually better. File streaming is still in
beta, so use with care!
is.sorted logical (FALSE), is grl sorted. That is + strand groups in increasing ranges
(1,2,3), and - strand groups in decreasing ranges (3,2,1)
keep.names logical (TRUE), keep names or not. If as.data.table is TRUE, names (genes
column) will be a factor column, if FALSE it will be an integer column (index
of gene), so first input grl element is 1. Dropping names gives ~ 20 % speedup.
If drop.zero.dt is FALSE, data.table will not return names, will use index (to
avoid memory explosion).
as.data.table a logical (FALSE), return as data.table with 2 columns, position and count.
withFrames a logical (FALSE), only available if as.data.table is TRUE, return the ORF
frame, 1,2,3, where position 1 is 1, 2 is 2 and 4 is 1 etc.
coveragePerTiling 73
weight (default: ’score’), if defined a character name of valid meta column in subject.
GRanges("chr1", 1, "+", score = 5), would mean score column tells that this
alignment region was found 5 times. ORFik ofst, bedoc and .bedo files contains
a score column like this. As do CAGEr CAGE files and many other package
formats. You can also assign a score column manually.
drop.zero.dt logical FALSE, if TRUE and as.data.table is TRUE, remove all 0 count posi-
tions. This greatly speeds up and most importantly, greatly reduces memory
usage. Will not change any plots, unless 0 positions are used in some sense.
(mean, median, zscore coverage will only scale differently)
fraction integer or character, a description column. Useful for grouping multiple outputs
together. If returned as Rle, this is added as: metadata(coverage) <- list(fraction
= fraction). If as.data.table it will be added as an additional column.
Details
NOTE: If reads contains a $score column, it will presume that this is the number of replicates per
reads, weights for the coverage() function. So delete the score column or set weight to something
else if this is not wanted.
Value
a numeric RleList, one numeric-Rle per group with # of hits per position. Or data.table if as.data.table
is TRUE, with column names c("count" [numeric or integer], "genes" [integer], "position" [integer])
See Also
Other ExtendGenomicRanges: asTX(), extendLeaders(), extendTrailers(), reduceKeepAttr(),
tile1(), txSeqsFromFa(), windowPerGroup()
Examples
ORF <- GRanges(seqnames = "1",
ranges = IRanges(start = c(1, 10, 20),
end = c(5, 15, 25)),
strand = "+")
grl <- GRangesList(tx1_1 = ORF)
RFP <- GRanges("1", IRanges(25, 25), "+")
coveragePerTiling(grl, RFP, is.sorted = TRUE)
# now as data.table with frames
coveragePerTiling(grl, RFP, is.sorted = TRUE, as.data.table = TRUE,
withFrames = TRUE)
# With score column (usually replicated reads on that position)
RFP <- GRanges("1", IRanges(25, 25), "+", score = 5)
dt <- coveragePerTiling(grl, RFP, is.sorted = TRUE,
as.data.table = TRUE, withFrames = TRUE)
class(dt$count) # numeric
# With integer score column (faster and less space usage)
RFP <- GRanges("1", IRanges(25, 25), "+", score = 5L)
dt <- coveragePerTiling(grl, RFP, is.sorted = TRUE,
as.data.table = TRUE, withFrames = TRUE)
74 coverageScorings
class(dt$count) # integer
coverageScorings Add a coverage scoring scheme
Description
Different scorings and groupings of a coverage representation.
Usage
coverageScorings(coverage, scoring = "zscore", copy.dt = TRUE)
Arguments
coverage a data.table containing at least columns (count, position), it is possible to have
additionals: (genes, fraction, feature)
scoring a character, one of (zscore, transcriptNormalized, mean, median, sum, log2sum,
log10sum, sumLength, meanPos and frameSum, periodic, NULL). More info in
details
copy.dt logical TRUE, copy object, to avoid overwriting original object. Set to false to
run function using reference to object, a speed up if original object is not needed.
Details
Usually output of metaWindow or scaledWindowPositions is input in this function.
Content of coverage data.table: It must contain the count and position columns.
genes column: If you have multiple windows, the genes column must define which gene/transcript
grouping the different counts belong to. If there is only a meta window or only 1 gene/transcript,
then this column is not needed.
fraction column: If you have coverage of i.e RNA-seq and Ribo-seq, or TCP -seq of large and small
subunite, divide into fractions. Like factor(RNA, RFP)
feature column: If gene group is subdivided into parts, like gene is transcripts, and feature column
can be c(leader, cds, trailer) etc.
Given a data.table coverage of counts, add a scoring scheme. per: the grouping given, if genes is
defined, group by per gene in default scoring.
Scorings:
• zscore (count-windowMean)/windowSD per)
• transcriptNormalized (sum(count / sum of counts per))
• mean (mean(count per))
• median (median(count per))
coverage_to_dt 75
• sum (count per)
• log2sum (count per)
• log10sum (count per)
• sumLength (count per) / number of windows
• meanPos (mean per position per gene) used in scaledWindowPositions
• sumPos (sum per position per gene) used in scaledWindowPositions
• frameSum (sum per frame per gene) used in ORFScore
• frameSumPerL (sum per frame per read length)
• frameSumPerLG (sum per frame per read length per gene)
• fracPos (fraction of counts per position per gene)
• periodic (Fourier transform periodicity of meta coverage per fraction)
• NULL (no grouping, return input directly)
Value
a data.table with new scores (size dependent on score used)
See Also
Other coverage: metaWindow(), regionPerReadLength(), scaledWindowPositions(), windowPerReadLength()
Examples
dt <- data.table::data.table(count = c(4, 1, 1, 4, 2, 3),
position = c(1, 2, 3, 4, 5, 6))
coverageScorings(dt, scoring = "zscore")
# with grouping gene
dt$genes <- c(rep("tx1", 3), rep("tx2", 3))
coverageScorings(dt, scoring = "zscore")
coverage_to_dt Convert coverage RleList to data.table
Description
Convert coverage RleList to data.table
76 covRle
Usage
coverage_to_dt(
coverage,
keep.names = TRUE,
withFrames = FALSE,
weight = "score",
drop.zero.dt = FALSE,
fraction = NULL
)
Arguments
coverage RleList with names
keep.names logical (TRUE), keep names or not. If as.data.table is TRUE, names (genes
column) will be a factor column, if FALSE it will be an integer column (index
of gene), so first input grl element is 1. Dropping names gives ~ 20 % speedup.
If drop.zero.dt is FALSE, data.table will not return names, will use index (to
avoid memory explosion).
withFrames a logical (FALSE), only available if as.data.table is TRUE, return the ORF
frame, 1,2,3, where position 1 is 1, 2 is 2 and 4 is 1 etc.
weight (default: ’score’), if defined a character name of valid meta column in subject.
GRanges("chr1", 1, "+", score = 5), would mean score column tells that this
alignment region was found 5 times. ORFik ofst, bedoc and .bedo files contains
a score column like this. As do CAGEr CAGE files and many other package
formats. You can also assign a score column manually.
drop.zero.dt logical FALSE, if TRUE and as.data.table is TRUE, remove all 0 count posi-
tions. This greatly speeds up and most importantly, greatly reduces memory
usage. Will not change any plots, unless 0 positions are used in some sense.
(mean, median, zscore coverage will only scale differently)
fraction integer or character, a description column. Useful for grouping multiple outputs
together. If returned as Rle, this is added as: metadata(coverage) <- list(fraction
= fraction). If as.data.table it will be added as an additional column.
Value
a data.table with column names c("count" [numeric or integer], "genes" [integer], "position" [inte-
ger])
covRle Coverage Rlelist for both strands
Description
Coverage Rlelist for both strands
covRle-class 77
Usage
covRle(forward = RleList(), reverse = RleList())
Arguments
forward a RleList with defined seqinfo for forward strand counts
reverse a RleList with defined seqinfo for reverse strand counts
Value
a covRle object
See Also
Other covRLE: covRle-class, covRleFromGR(), covRleList, covRleList-class
Examples
covRle()
covRle(RleList(), RleList())
chr_rle <- RleList(chr1 = Rle(c(1,2,3), c(1,2,3)))
covRle(chr_rle, chr_rle)
covRle-class Coverage Rle for both strands or single
Description
Given a run of coverage(x) where x are reads, this class combines the 2 strands into 1 object
Value
a covRLE object
See Also
Other covRLE: covRle, covRleFromGR(), covRleList, covRleList-class
78 covRleFromGR
covRleFromGR Convert GRanges to covRle
Description
Convert GRanges to covRle
Usage
covRleFromGR(x, weight = "AUTO", ignore.strand = FALSE)
Arguments
x a GRanges, GAlignment or GAlignmentPairs object. Note that coverage cal-
culation for GAlignment is slower, so usually best to call convertToOneBase-
dRanges on GAlignment object to speed it up.
weight default "AUTO", pick ’score’ column if exist, else all are 1L. Can also be a
manually assigned meta column like ’score2’ etc.
ignore.strand logical, default FALSE.
Value
covRle object
See Also
Other covRLE: covRle, covRle-class, covRleList, covRleList-class
Examples
seqlengths <- as.integer(c(200, 300))
names(seqlengths) <- c("chr1", "chr2")
gr <- GRanges(seqnames = c("chr1", "chr1", "chr2", "chr2"),
ranges = IRanges(start = c(10, 50, 100, 150), end = c(40, 80, 129, 179)),
strand = c("+", "+", "-", "-"), seqlengths = seqlengths)
cov_both_strands <- covRleFromGR(gr)
cov_both_strands
cov_ignore_strand <- covRleFromGR(gr, ignore.strand = TRUE)
cov_ignore_strand
strandMode(cov_both_strands)
strandMode(cov_ignore_strand)
covRleList 79
covRleList Coverage Rlelist for both strands
Description
Coverage Rlelist for both strands
Usage
covRleList(list, fraction = names(list))
Arguments
list a list or List of covRle objects of equal length and lengths
fraction character, default names(list). Names to elements of list, can be integers, as
readlengths etc.
Value
a covRleList object
See Also
Other covRLE: covRle, covRle-class, covRleFromGR(), covRleList-class
Examples
covRleList(List(covRle()))
covRleList-class List of covRle
Description
Given a run of coverage(x) where x are reads, this covRle combines the 2 strands into 1 object This
list can again combine these into 1 object, with accession functions and generalizations.
Value
a covRleList object
See Also
Other covRLE: covRle, covRle-class, covRleFromGR(), covRleList
80 create.experiment
create.experiment Create an ORFik experiment
Description
Create a single R object that stores and controls all results relevant to a specific Next generation
sequencing experiment. Click the experiment link above in the title if you are not sure what an
ORFik experiment is.
By using files in a folder / folders. It will make an experiment table with information per sam-
ple, this object allows you to use the extensive API in ORFik that works on experiments.
Information Auto-detection:
There will be several columns you can fill in, when creating the object, if the files have logical names
like (RNA-seq_WT_rep1.bam) it will try to auto-detect the most likely values for the columns. Like
if it is RNA-seq or Ribo-seq, Wild type or mutant, is this replicate 1 or 2 etc.
You will have to fill in the details that were not auto detected. Easiest way to fill in the blanks are in
a csv editor like libre Office or excel. You can also remake the experiment and specify the specific
column manually. Remember that each row (sample) must have a unique combination of values.
An extra column called "reverse" is made if there are paired data, like +/- strand wig files.
Usage
create.experiment(
dir,
exper,
saveDir = ORFik::config()["exp"],
txdb = "",
fa = "",
organism = "",
assembly = "",
pairedEndBam = FALSE,
viewTemplate = FALSE,
types = c("bam", "bed", "wig", "ofst"),
libtype = "auto",
stage = "auto",
rep = "auto",
condition = "auto",
fraction = "auto",
author = "",
files = findLibrariesInFolder(dir, types, pairedEndBam),
result_folder = NULL,
runIDs = extract_run_id(files)
)
create.experiment 81
Arguments
dir Which directory / directories to create experiment from, must be a directory
with NGS data from your experiment. Will include all files of file type specified
by "types" argument. So do not mix files from other experiments in the same
folder!
exper Short name of experiment. Will be name used to load experiment, and name
shown when running list.experiments
saveDir Directory to save experiment csv file, default: ORFik::config()["exp"], which
has default: "~/Bio_data/ORFik_experiments/". Set to NULL if you don’t want
to save it to disc.
txdb A path to TxDb (prefered) or gff/gtf (not adviced, slower) file with transcriptome
annotation for the organism.
fa A path to fasta genome/sequences used for libraries, remember the file must
have a fasta index too.
organism character, default: "" (no organism set), scientific name of organism. Homo
sapiens, Danio rerio, Rattus norvegicus etc. If you have a SRA metadata csv
file, you can set this argument to study$ScientificName[1], where study is the
SRA metadata for all files that was aligned.
assembly character, default: "" (no assembly set). The genome assembly name, like
GRCh38 etc. Useful to add if you want detailed metadata of experiment analy-
sis.
pairedEndBam logical FALSE, else TRUE, or a logical list of TRUE/FALSE per library you see
will be included (run first without and check what order the files will come in)
1 paired end file, then two single will be c(T, F, F). If you have a SRA metadata
csv file, you can set this argument to study$LibraryLayout == "PAIRED", where
study is the SRA metadata for all files that was aligned.
viewTemplate run View() on template when finished, default (FALSE). Usually gives you a
better view of result than using print().
types Default c("bam", "bed", "wig", "ofst"), which types of libraries to allow as
NGS data.
libtype character, default "auto". Library types, must be length 1 or equal length of
number of libraries. "auto" means ORFik will try to guess from file names.
Example: RFP (Ribo-seq), RNA (RNA-seq), CAGE, SSU (TCP-seq 40S), LSU
(TCP-seq 80S).
stage character, default "auto". Developmental stage, tissue or cell line, must be length
1 or equal length of number of libraries. "auto" means ORFik will try to guess
from file names. Example: HEK293 (Cell line), Sphere (zebrafish stage), ovary
(Tissue).
rep character, default "auto". Replicate numbering, must be length 1 or equal length
of number of libraries. "auto" means ORFik will try to guess from file names.
Example: 1 (rep 1), 2 rep(2). Insert only numbers here!
condition character, default "auto". Library conditions, must be length 1 or equal length
of number of libraries. "auto" means ORFik will try to guess from file names.
Example: WT (wild type), mutant, etc.
82 create.experiment
fraction character, default "auto". Fractionation of library, must be length 1 or equal
length of number of libraries. "auto" means ORFik will try to guess from file
names. This columns is used to make experiment unique, if the other columns
are not sufficient. Example: cyto (cytosolic fraction), dmso (dmso treated frac-
tion), etc.
author character, default "". Main author of experiment, usually last name is enough.
When printing will state "author et al" in info.
files character vector or data.table of library paths in dir. Default: findLibrariesInFolder(dir,
types, pairedEndBam). Do not touch unless you want to do some subsetting,
it will automatically remove files that are not of file format defined by ’type’
argument. Note that sorting on number that: 10 is before 2, so 1, 2, 10, is
sorted as: 1, 10, 2. If you want to fix this, you could update this argument with:
ORFik:::findLibrariesInFolder()[1,3,2] to get order back to 1,2,10 etc.
result_folder character, default NULL. The folder to output analysis results like QC, count
tables etc. By default the libFolder(df) folder is used, the folder of first library
in experiment. If you are making a new experiment which is a collection of other
experiments, set this to a new folder, to not contaminate your other experiment
directories.
runIDs character ids, usually SRR, ERR, or DRR identifiers, default is to search for any
of these 3 in the filename by: extract_run_id(files). They are optional.
Value
a data.frame, NOTE: this is not a ORFik experiment, only a template for it!
See Also
Other ORFik_experiment: ORFik.template.experiment(), ORFik.template.experiment.zf(),
bamVarName(), experiment-class, filepath(), libraryTypes(), organism,experiment-method,
outputLibs(), read.experiment(), save.experiment(), validateExperiments()
Examples
# 1. Pick directory
dir <- system.file("extdata/Homo_sapiens_sample", "", package = "ORFik")
# 2. Pick an experiment name
exper <- "ORFik"
# 3. Pick .gff/.gtf location
txdb <- system.file("extdata/Homo_sapiens_sample", "Homo_sapiens_dummy.gtf.db", package = "ORFik")
# 4. Pick fasta genome of organism
fa <- system.file("extdata/Homo_sapiens_sample", "Homo_sapiens_dummy.fasta", package = "ORFik")
# 5. Set organism (optional)
org <- "Homo sapiens"
# Create temple not saved on disc yet:
template <- create.experiment(dir = dir, exper, txdb = txdb,
saveDir = NULL,
fa = fa, organism = org,
viewTemplate = FALSE)
defineIsoform 83
## Now fix non-unique rows: either is libre office, microsoft excel, or in R
template$X5[6] <- "heart"
# read experiment (if you set correctly)
df <- read.experiment(template)
# Save with: save.experiment(df, file = "path/to/save/experiment.csv")
## Create and save experiment directly:
## Default location: "~/Bio_data/ORFik_experiments/"
#template <- create.experiment(dir = dir, exper, txdb = txdb,
# fa = fa, organism = org,
# viewTemplate = FALSE)
## Custom location (If you work in a team, use a shared folder)
#template <- create.experiment(dir = dir, exper, txdb = txdb,
# saveDir = "~/MY/CUSTOME/LOCATION",
# fa = fa, organism = org,
# viewTemplate = FALSE)
defineIsoform Overlaps GRanges object with provided annotations.
Description
Overlaps GRanges object with provided annotations.
Usage
defineIsoform(
rel_orf,
tran,
isoform_names = c("perfect_match", "elong_START_match", "trunc_START_match",
"elong_STOP_match", "trunc_STOP_match", "overlap_inside", "overlap_both",
"overlap_upstream", "overlap_downstream", "upstream", "downstram", "none")
)
Arguments
rel_orf - GRanges object of your ORF.
tran - GRanges object of annotation (transcript or cds) that overlapped in some way
rel_orf.
isoform_names - A vector of strings that will be used instead of these defaults: ’perfect_match’
- start and stop matches the tran object strand wise ’elong_START_match’ -
rel_orf is extension from the STOP side of the tran ’trunc_START_match’ -
rel_orf is truncation from the STOP side of the tran ’elong_STOP_match’ -
rel_orf is extension from the START side of the tran ’trunc_STOP_match’ -
rel_orf is truncation from the START side of the tran ’overlap_inside’ - rel_orf
is inside tran object ’overlap_both’ - rel_orf contains tran object inside ’over-
lap_upstream’ - rel_orf is overlaping upstream part of the tran ’overlap_downstream’
84 defineTrailer
- rel_orf is overlaping downstream part of the tran ’upstream’ - rel_orf is up-
stream towards the tran ’downstream’ - rel_orf is downstream towards the tran
’none’ - when none of the above options is true
Value
A string object of defined isoform towards transcript.
defineTrailer Defines trailers for ORF.
Description
Creates GRanges object as a trailer for ORFranges representing ORF, maintaining restrictions of
transcriptRanges. Assumes that ORFranges is on the transcriptRanges, strands and seqlevels are in
agreement. When lengthOFtrailer is smaller than space left on the transcript than all available space
is returned as trailer.
Usage
defineTrailer(ORFranges, transcriptRanges, lengthOftrailer = 200)
Arguments
ORFranges GRanges object of your Open Reading Frame.
transcriptRanges
GRanges object of transtript.
lengthOftrailer
Numeric. Default is 10.
Details
It assumes that ORFranges and transcriptRanges are not sorted when on minus strand. Should be
like: (200, 600) (50, 100)
Value
A GRanges object of trailer.
See Also
Other ORFHelpers: longestORFs(), mapToGRanges(), orfID(), startCodons(), startSites(),
stopCodons(), stopSites(), txNames(), uniqueGroups(), uniqueOrder()
DEG.analysis 85
Examples
ORFranges <- GRanges(seqnames = Rle(rep("1", 3)),
ranges = IRanges(start = c(1, 10, 20),
end = c(5, 15, 25)),
strand = "+")
transcriptRanges <- GRanges(seqnames = Rle(rep("1", 5)),
ranges = IRanges(start = c(1, 10, 20, 30, 40),
end = c(5, 15, 25, 35, 45)),
strand = "+")
defineTrailer(ORFranges, transcriptRanges)
DEG.analysis Run differential TE analysis
Description
Expression analysis of 1 dimension, usually between conditions of RNA-seq.
Using the standardized DESeq2 pipeline flow.
Creates a DESeq model (given x is the target.contrast argument) (usually ’condition’ column)
1. RNA-seq model: design = ~ x (differences between the x groups in RNA-seq)
Usage
DEG.analysis(
df,
target.contrast = design[1],
design = ORFik::design(df),
p.value = 0.05,
counts = countTable(df, "mrna", type = "summarized"),
batch.effect = TRUE,
pairs = combn.pairs(unlist(df[, target.contrast]))
)
Arguments
df an experiment of usually RNA-seq.
target.contrast
a character vector, default design[1]. The column in the ORFik experiment that
represent the comparison contrasts. By default: the first design factor of the full
experimental design. This is the factor you will do the comparison on. DESeq
will normalize the counts based on the full design, but the log fold change values
will be based on this contrast only. It is usually the ’condition’ column.
design a character vector, default design(df.rfp). The full experiment design. Which
factors have more than 1 level. Example: stage column are all HEK293, so it
can not be a design factor. The condition column has 2 possible values, WT
86 DEG.analysis
and mutant, so it is a factor of the experiment. Replicates column is not part
of design, that is inserted later with setting batch.effect = TRUE. Library type
’libtype’ column, can also no be part of initial design, it is always added inside
the function, after initial setup.
p.value a numeric, default 0.05 in interval (0,1) or "" to not show. What p-value used for
the analysis? Will be shown as a caption.
counts a SummarizedExperiment, default: countTable(df, "mrna", type = "summa-
rized"), all transcripts. Assign a subset if you don’t want to analyze all genes. It
is recommended to not subset, to give DESeq2 data for variance analysis.
batch.effect logical, default TRUE. Makes replicate column of the experiment part of the
design.
If you believe you might have batch effects, keep as TRUE. Batch effect usually
means that you have a strong variance between biological replicates. Check
out pcaExperiment and see if replicates cluster together more than the design
factor, to verify if you need to set it to TRUE.
pairs list of character pairs, the experiment contrasts. Default: combn.pairs(unlist(df.rfp[,
target.contrast])
Details
#’ Analysis is done between each possible combination of levels in the target contrast If target con-
trast is the condition column, with factor levels: WT, mut1 and mut2 with 3 replicates each. You
get comparison of WT vs mut1, WT vs mut2 and mut1 vs mut2.
The respective result categories are defined as: (given a user defined p value, shown here as 0.05):
Significant - p-value adjusted < 0.05 (p-value cutoff decided by ’p.value argument)
The LFC values are shrunken by lfcShrink(type = "normal").
Remember that DESeq by default can not do global change analysis, it can only find subsets with
changes in LFC!
Value
a data.table with columns: (contrast variable, gene id, regulation status, log fold changes, p.adjust
values, mean counts)
References
doi: 10.1002/cpmb.108
See Also
Other DifferentialExpression: DEG.plot.static(), DEG_model(), DTEG.plot(), te.table(),
te_rna.plot()
DEG.plot.static 87
Examples
## Simple example (use ORFik template, then use only RNA-seq)
df <- ORFik.template.experiment()
df.rna <- df[df$libtype == "RNA",]
design(df.rna) # The full experimental design
design(df.rna)[1] # Default target contrast
#dt <- DEG.analysis(df.rna)
DEG.plot.static Plot DEG result
Description
Plot setup:
X-axis: mean counts Y-axis: Log2 fold changes For explanation of plot, see DEG.analysis
Usage
DEG.plot.static(
dt,
output.dir = NULL,
p.value = 0.05,
plot.title = "",
plot.ext = ".pdf",
width = 6,
height = 6,
dot.size = 0.4,
xlim = "auto",
ylim = "bidir.max",
relative.name = paste0("DEG_plot", plot.ext)
)
Arguments
dt a data.table with the results from DEG.analysis
output.dir a character path, default NULL(no save), or a directory to save to a file. Relative
name of file, specified by ’relative.name’ argument.
p.value a numeric, default 0.05 in interval (0,1) or "" to not show. What p-value used for
the analysis? Will be shown as a caption.
plot.title title for plots, usually name of experiment etc
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg".
width numeric, default 6 (in inches)
height numeric, default 6 (in inches)
dot.size numeric, default 0.4, size of point dots in plot.
88 DEG_model
xlim numeric vector or character preset, default: "bidir.max" (Equal in both + / -
direction, using max value + 0.5 of meanCounts column in dt). If you want
ggplot to decide limit, set to "auto". For numeric vector, specify min and max x
limit: like c(-5, 5)
ylim numeric vector or character preset, default: "bidir.max" (Equal in both + / -
direction, using max value + 0.5 of LFC column in dt). If you want ggplot to
decide limit, set to "auto". For numeric vector, specify min and max y limit: like
c(-10, 10)
relative.name character, Default: paste0("DEG_plot", plot.ext) Relative name of file to
be saved in folder specified in output.dir. Change to .pdf if you want pdf file
instead of png.
Value
a ggplot object
See Also
Other DifferentialExpression: DEG_model(), DTEG.analysis(), DTEG.plot(), te.table(), te_rna.plot()
Examples
df <- ORFik.template.experiment()
df.rna <- df[df$libtype == "RNA",]
#dt <- DEG.analysis(df.rna)
#Default scaling
#DEG.plot.static(dt)
#Manual scaling
#DEG.plot.static(dt, xlim = c(-2, 2), ylim = c(-2, 2))
DEG_model Get DESeq2 model without running results
Description
This is the preparation step of DESeq2 analysis using ORFik::DEG.analysis. It is exported so that
you can do this step in standalone, usually you want to use DEG.analysis directly.
Usage
DEG_model(
df,
target.contrast = design[1],
design = ORFik::design(df),
p.value = 0.05,
counts = countTable(df, "mrna", type = "summarized"),
batch.effect = TRUE
)
DEG_model 89
Arguments
df an experiment of usually RNA-seq.
target.contrast
a character vector, default design[1]. The column in the ORFik experiment that
represent the comparison contrasts. By default: the first design factor of the full
experimental design. This is the factor you will do the comparison on. DESeq
will normalize the counts based on the full design, but the log fold change values
will be based on this contrast only. It is usually the ’condition’ column.
design a character vector, default design(df.rfp). The full experiment design. Which
factors have more than 1 level. Example: stage column are all HEK293, so it
can not be a design factor. The condition column has 2 possible values, WT
and mutant, so it is a factor of the experiment. Replicates column is not part
of design, that is inserted later with setting batch.effect = TRUE. Library type
’libtype’ column, can also no be part of initial design, it is always added inside
the function, after initial setup.
p.value a numeric, default 0.05 in interval (0,1) or "" to not show. What p-value used for
the analysis? Will be shown as a caption.
counts a SummarizedExperiment, default: countTable(df, "mrna", type = "summa-
rized"), all transcripts. Assign a subset if you don’t want to analyze all genes. It
is recommended to not subset, to give DESeq2 data for variance analysis.
batch.effect logical, default TRUE. Makes replicate column of the experiment part of the
design.
If you believe you might have batch effects, keep as TRUE. Batch effect usually
means that you have a strong variance between biological replicates. Check
out pcaExperiment and see if replicates cluster together more than the design
factor, to verify if you need to set it to TRUE.
Value
a DESeqDataSet object with results stored as metadata columns.
See Also
Other DifferentialExpression: DEG.plot.static(), DTEG.analysis(), DTEG.plot(), te.table(),
te_rna.plot()
Examples
## Simple example (use ORFik template, then use only RNA-seq)
df <- ORFik.template.experiment()
df.rna <- df[df$libtype == "RNA",]
design(df.rna) # The full experimental design
target.contrast <- design(df.rna)[1] # Default target contrast
#ddsMat_rna <- DEG_model(df.rna, target.contrast)
90 DEG_model_results
DEG_model_results Get DESeq2 model results from DESeqDataSet
Description
Get DESeq2 model results from DESeqDataSet
Usage
DEG_model_results(ddsMat_rna, target.contrast, pairs, p.value = 0.05)
Arguments
ddsMat_rna a DESeqDataSet object with results stored as metadata columns.
target.contrast
a character vector, default design[1]. The column in the ORFik experiment that
represent the comparison contrasts. By default: the first design factor of the full
experimental design. This is the factor you will do the comparison on. DESeq
will normalize the counts based on the full design, but the log fold change values
will be based on this contrast only. It is usually the ’condition’ column.
pairs list of character pairs, the experiment contrasts. Default: combn.pairs(unlist(df.rfp[,
target.contrast])
p.value a numeric, default 0.05 in interval (0,1) or "" to not show. What p-value used for
the analysis? Will be shown as a caption.
Value
a data.table
Examples
## Simple example (use ORFik template, then use only RNA-seq)
df <- ORFik.template.experiment()
df.rna <- df[df$libtype == "RNA",]
design(df.rna) # The full experimental design
target.contrast <- design(df.rna)[1] # Default target contrast
#ddsMat_rna <- DEG_model(df.rna, target.contrast)
#pairs <- combn.pairs(unlist(df[, target.contrast]))
#dt <- DEG_model_results(ddsMat_rna, target.contrast, pairs)
DEG_model_simple 91
DEG_model_simple Simple Fpkm ratio test DEG
Description
If you do not have a valid DESEQ2 experimental setup (contrast), you can use this simplified test
Usage
DEG_model_simple(
df,
target.contrast = design[1],
design = ORFik::design(df),
p.value = 0.05,
counts = countTable(df, "mrna", type = "summarized"),
batch.effect = FALSE
)
Arguments
df an experiment of usually RNA-seq.
target.contrast
a character vector, default design[1]. The column in the ORFik experiment that
represent the comparison contrasts. By default: the first design factor of the full
experimental design. This is the factor you will do the comparison on. DESeq
will normalize the counts based on the full design, but the log fold change values
will be based on this contrast only. It is usually the ’condition’ column.
design a character vector, default design(df.rfp). The full experiment design. Which
factors have more than 1 level. Example: stage column are all HEK293, so it
can not be a design factor. The condition column has 2 possible values, WT
and mutant, so it is a factor of the experiment. Replicates column is not part
of design, that is inserted later with setting batch.effect = TRUE. Library type
’libtype’ column, can also no be part of initial design, it is always added inside
the function, after initial setup.
p.value a numeric, default 0.05 in interval (0,1) or "" to not show. What p-value used for
the analysis? Will be shown as a caption.
counts a SummarizedExperiment, default: countTable(df, "mrna", type = "summa-
rized"), all transcripts. Assign a subset if you don’t want to analyze all genes. It
is recommended to not subset, to give DESeq2 data for variance analysis.
batch.effect logical, default TRUE. Makes replicate column of the experiment part of the
design.
If you believe you might have batch effects, keep as TRUE. Batch effect usually
means that you have a strong variance between biological replicates. Check
out pcaExperiment and see if replicates cluster together more than the design
factor, to verify if you need to set it to TRUE.
92 design,experiment-method
Value
a data.table of fpkm ratios
Examples
## Simple example (use ORFik template, then use only RNA-seq)
df <- ORFik.template.experiment()
df <- df[df$libtype == "RNA",]
#dt <- DEG_model_simple(df)
design,experiment-method
Get experimental design Find the column/columns that create a sepa-
ration between samples, by default skips replicate and choose first that
is from either: libtype, condition, stage and fraction.
Description
Get experimental design Find the column/columns that create a separation between samples, by
default skips replicate and choose first that is from either: libtype, condition, stage and fraction.
Usage
## S4 method for signature 'experiment'
design(
object,
batch.correction.design = FALSE,
as.formula = FALSE,
multi.factor = TRUE
)
Arguments
object an ORFik experiment
batch.correction.design
logical, default FALSE. If true, add replicate as a second design factor (only if
>= 2 replicates exists).
as.formula logical, default FALSE. If TRUE, return as formula
multi.factor logical, default TRUE If FALSE, return first factor only (+ rep, if batch.correction.design
is true). Order of picking is: libtype, if not then: stage, if not then: condition, if
not then: fraction.
Value
a character (name of column) or a formula
detectRibosomeShifts 93
Examples
df <- ORFik.template.experiment()
design(df) # The 2 columns that decides the design here
# If we subset it changes
design(df[df$libtype == "RFP",])
# Only single factor design, it picks first
design(df, multi.factor = FALSE)
detectRibosomeShifts Detect ribosome shifts
Description
Utilizes periodicity measurement (Fourier transform), and change point analysis to detect ribosomal
footprint shifts for each of the ribosomal read lengths. Returns subset of read lengths and their shifts
for which top covered transcripts follow periodicity measure. Each shift value assumes 5’ anchoring
of the reads, so that output offsets values will shift 5’ anchored footprints to be on the p-site of the
ribosome. The E-site will be shift + 3 and A site will be shift - 3. So update to these, if you rather
want those.
Usage
detectRibosomeShifts(
footprints,
txdb,
start = TRUE,
stop = FALSE,
top_tx = 10L,
minFiveUTR = 30L,
minCDS = 150L,
minThreeUTR = if (stop) {
30
} else NULL,
txNames = filterTranscripts(txdb, minFiveUTR, minCDS, minThreeUTR),
firstN = 150L,
tx = NULL,
min_reads = 1000,
min_reads_TIS = 50,
accepted.lengths = 26:34,
heatmap = FALSE,
must.be.periodic = TRUE,
strict.fft = TRUE,
verbose = FALSE
)
94 detectRibosomeShifts
Arguments
footprints GAlignments object of RiboSeq reads - footprints, can also be path to the .bam
/.ofst file. If GAlignment object has a meta column called "score", this will be
used as replicate numbering for that read. So be careful if you have custom files
with score columns, with another meaning.
txdb a TxDb file, a path to one of: (.gtf ,.gff, .gff2, .gff2, .db or .sqlite) or an ORFik
experiment
start (logical) Whether to include predictions based on the start codons. Default
TRUE.
stop (logical) Whether to include predictions based on the stop codons. Default
FASLE. Only use if there exists 3’ UTRs for the annotation. If peridicity around
stop codon is stronger than at the start codon, use stop instead of start region for
p-shifting.
top_tx (integer), default 10. Specify which % of the top TIS coverage transcripts to use
for estimation of the shifts. By default we take top 10 top covered transcripts as
they represent less noisy data-set. This is only applicable when there are more
than 1000 transcripts.
minFiveUTR (integer) minimum bp for 5’ UTR during filtering for the transcripts. Set to
NULL if no 5’ UTRs exists for annotation.
minCDS (integer) minimum bp for CDS during filtering for the transcripts
minThreeUTR (integer) minimum bp for 3’ UTR during filtering for the transcripts. Set to
NULL if no 3’ UTRs exists for annotation.
txNames a character vector of subset of CDS to use. Default: txNames = filterTran-
scripts(txdb, minFiveUTR, minCDS, minThreeUTR)
Example: c("ENST1000005"), will use only that transcript (You should use at
least 100!). Remember that top_tx argument, will by default specify to use top
10 % of those CDSs. Set that to 100, to use all these specified transcripts.
firstN (integer) Represents how many bases of the transcripts downstream of start
codons to use for initial estimation of the periodicity.
tx a GRangesList, if you do not have 5’ UTRs in annotation, send your own ver-
sion. Example: extendLeaders(tx, 30) Where 30 bases will be new "leaders".
Since each original transcript was either only CDS or non-coding (filtered out).
min_reads default (1000), how many reads must a read-length have in total to be considered
for periodicity.
min_reads_TIS default (50), how many reads must a read-length have in the TIS region to be
considered for periodicity.
accepted.lengths
accepted read lengths, default 26:34, usually ribo-seq is strongest between 27:32.
heatmap a logical or character string, default FALSE. If TRUE, will plot heatmap of raw
reads before p-shifting to console, to see if shifts given make sense. You can
also set a filepath to save the file there.
detectRibosomeShifts 95
must.be.periodic
logical TRUE, if FALSE will not filter on periodic read lengths. (The Fourier
transform filter will be skipped). This is useful if you are not going to do period-
icity analysis, that is: for you more coverage depth (more read lengths) is more
important than only keeping the high quality periodic read lengths.
strict.fft logical, TRUE. Use a FFT without noise filter. This means keep only reads
lengths that are "periodic for the human eye". If you want more coverage, set to
FALSE, to also get read lengths that are "messy", but the noise filter detects the
periodicity of 3. This should only be done when you do not need high quality
periodic reads! Example would be differential translation analysis by counts
over each ORF.
verbose logical, default FALSE. Report details of analysis/periodogram. Good if you are
not sure if the analysis was correct.
Details
Check out vignette for the examples of plotting RiboSeq metaplots over start and stop codons, so
that you can verify visually whether this function detects correct shifts.
For how the Fourier transform works, see: isPeriodic
For how the changepoint analysis works, see: changePointAnalysis
NOTE: It will remove softclips from valid width, the CIGAR 3S30M is qwidth 33, but will remove
3S so final read width is 30 in ORFik. This is standard for ribo-seq.
Value
a data.table with lengths of footprints and their predicted coresponding offsets
References
https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4912-6
See Also
Other pshifting: changePointAnalysis(), shiftFootprints(), shiftFootprintsByExperiment(),
shiftPlots(), shifts.load()
Examples
## Basic run
# Transcriptome annotation ->
gtf_file <- system.file("extdata/Danio_rerio_sample", "annotations.gtf", package = "ORFik")
# Ribo seq data ->
riboSeq_file <- system.file("extdata/Danio_rerio_sample", "ribo-seq.bam", package = "ORFik")
## Not run:
footprints <- readBam(riboSeq_file)
## Using CDS start site as reference point:
detectRibosomeShifts(footprints, gtf_file)
## Using CDS start site and stop site as 2 reference points:
96 detect_ribo_orfs
#detectRibosomeShifts(footprints, gtf_file, stop = TRUE)
## Debug and detailed information for accepted reads lengths and p-site:
detectRibosomeShifts(footprints, gtf_file, heatmap = TRUE, verbose = TRUE)
## Debug why read length 31 was not accepted or wrong p-site:
#detectRibosomeShifts(footprints, gtf_file, must.be.periodic = FALSE,
# accepted.lengths = 31, heatmap = TRUE, verbose = TRUE)
## Subset bam file
param = ScanBamParam(flag = scanBamFlag(
isDuplicate = FALSE,
isSecondaryAlignment = FALSE))
footprints <- readBam(riboSeq_file, param = param)
detectRibosomeShifts(footprints, gtf_file, stop = TRUE)
## Without 5' Annotation
library(GenomicFeatures)
txdb <- loadTxdb(gtf_file)
tx <- exonsBy(txdb, by = "tx", use.names = TRUE)
tx <- extendLeaders(tx, 30)
## Now run function, without 5' and 3' UTRs
detectRibosomeShifts(footprints, txdb, start = TRUE, minFiveUTR = NULL,
minCDS = 150L, minThreeUTR = NULL, firstN = 150L,
tx = tx)
## End(Not run)
detect_ribo_orfs Detect ORFs by Ribosome profiling data
Description
Finding all ORFs: 1. Find all ORFs in mRNA using ORFik findORFs, with defined parameters.
To create the candidate ORFs (all ORFs returned):
Steps (candidate set):
Define a candidate search set by these 3 rules:
1.a Allowed ORF type: uORF, NTE, etc (only keep these in candidate list)
1.b Must have at least x reads over whole orf (default 10 reads)
1.c Must have at least x reads over start site (default 3 reads)
The total list is defined by these names, and saved according to allowed ORF type/types.
To create the prediction status (TRUE/FALSE) per candidate
Steps (prediction status)
(UP_NT is a 20nt window upstream of ORF, that stops 2NT before ORF starts) :
1. ORF mean reads per NT > (UP_NT mean reads per NT * 1.3)
2. ORFScore > 2.5
3. TIS total reads + 3 > ORF median reads per NT
4. Given expression above, a TRUE prediction is defined with the AND operatior: 1. & 2. & 3.
detect_ribo_orfs 97
In code that is:
predicted <- (orfs_cov_stats$mean > upstream_cov_stats$mean*1.3) & orfs_cov_stats$ORFScores
> 2.5 & ((reads_start[candidates] + 3) > orfs_cov_stats$median)
Usage
detect_ribo_orfs(
df,
out_folder,
ORF_categories_to_keep,
prefix_result = paste(c(ORF_categories_to_keep, gsub(" ", "_", organism(df))), collapse
= "_"),
mrna = loadRegion(df, "mrna"),
cds = loadRegion(df, "cds"),
libraries = outputLibs(df, type = "pshifted", output = "envirlist"),
orf_candidate_ranges = findORFs(seqs = txSeqsFromFa(mrna, df, TRUE), longestORF =
longestORF, startCodon = startCodon, stopCodon = stopCodon, minimumLength =
minimumLength),
export_metrics_table = TRUE,
longestORF = FALSE,
startCodon = startDefinition(1),
stopCodon = stopDefinition(1),
minimumLength = 0,
minimum_reads_ORF = 10,
minimum_reads_start = 3
)
Arguments
df an ORFik experiment
out_folder Directory to save files
ORF_categories_to_keep
options, any subset of: c("uORF", "uoORF", "annotated", "NTE","NTT", "internal",
"doORF", "dORF", "a_error").
• uORF: Upstream ORFs (Starting in 5’ UTR), not overlapping CDS
• uoORF: Upstream ORFs (Starting in 5’ UTR), overlapping CDS
• annotated: The defined CDS for that transcript
• NTE: 5’ Start codon extension of annotated CDS
• NTT: 5’ Start codon truncation of annotated CDS
• internal: Starting inside CDS, ending before CDS ends
• doORF: Downstream ORFs (Ending in 3’ UTR), overlapping CDS
• dORF: Downstream ORFs (Ending in 3’ UTR), not overlapping CDS
• a_error: Any ORF detect not in the above categories
prefix_result the prefix name of output files to out_folder. Default: paste(c(ORF_categories_to_keep,
gsub(" ", "_", organism(df))), collapse = "_")
98 detect_ribo_orfs
mrna = loadRegion(df, "mrna")
cds = loadRegion(df, "cds")
libraries the ribo-seq libraries loaded into R as list, default: outputLibs(df, type =
"pshifted", output = "envirlist")
orf_candidate_ranges
IRangesList, = findORFs(seqs = txSeqsFromFa(mrna, df, TRUE),longestORF
= longestORF, startCodon = startCodon, stopCodon = stopCodon,minimumLength
= minimumLength)
export_metrics_table
logical, default TRUE. Export table of statistics to file with suffix: "_predic-
tion_table.rds"
longestORF (logical) Default TRUE. Keep only the longest ORF per unique stopcodon: (se-
qname, strand, stopcodon) combination, Note: Not longest per transcript! You
can also use function longestORFs after creation of ORFs for same result.
startCodon (character vector) Possible START codons to search for. Check startDefinition
for helper function. Note that it is case sensitive, so "atg" would give 0 hits for
a sequence with only capital "ATG" ORFs.
stopCodon (character vector) Possible STOP codons to search for. Check stopDefinition
for helper function. Note that it is case sensitive, so "tga" would give 0 hits for
a sequence with only capital "TGA" ORFs.
minimumLength (integer) Default is 0. Which is START + STOP = 6 bp. Minimum length
of ORF, without counting 3bps for START and STOP codons. For example
minimumLength = 8 will result in size of ORFs to be at least START + 8*3 (bp)
+ STOP = 30 bases. Use this param to restrict search.
minimum_reads_ORF
numeric, default 10, orf removed if less reads overlap whole orf
minimum_reads_start
numeric, default 3, orf removed if less reads overlap start
Value
invisible(NULL), all ORF results saved to disc
Examples
# Pre requisites
# 1. Create ORFik experiment
# ORFik::create.experiment(...)
# 2. Create ORFik optimized annotation:
# makeTxdbFromGenome(gtf = ORFik:::getGtfPathFromTxdb(df), genome = df@fafile, organism = organism(df), optimize = TRUE)
# 3. There must exist pshifted reads, either as default files, or in a relative folder called
# "./pshifted/". See ?shiftFootprintsByExperiment
# EXAMPLE:
df <- ORFik.template.experiment()
df <- df[df$libtype == "RFP",][c(1,2),]
result_folder <- riboORFsFolder(df, tempdir())
results <- detect_ribo_orfs(df, result_folder, c("uORF", "uoORF", "annotated", "NTE"))
disengagementScore 99
# Load results of annotated ORFs
table <- riboORFs(df[1,], type = "table", result_folder)
table # See all statistics
sum(table$predicted) # How many were predicted as Ribo-seq ORFs
# Load 2 results
table <- riboORFs(df[1:2,], type = "table", result_folder)
table # See all statistics
sum(table$predicted) # How many were predicted as Ribo-seq ORFs
# Load GRangesList
candidates_gr <- riboORFs(df[1,], type = "ranges_candidates", result_folder)
prediction <- riboORFs(df[1,], type = "predictions", result_folder)
predicted_gr <- riboORFs(df[1:2,], type = "ranges_predictions", result_folder)
identical(predicted_gr[[1]], candidates_gr[[1]][prediction[[1]]])
## Inspect predictions in RiboCrypt
# library(RiboCrypt)
# Inspect Predicted
view <- predicted_gr[[1]][1]
#multiOmicsPlot_ORFikExp(view, df, view, leader_extension = 100, trailer_extension = 100)
# Inspect not predicted
view <- candidates_gr[[1]][!prediction[[1]]][1]
#multiOmicsPlot_ORFikExp(view, df, view, leader_extension = 100, trailer_extension = 100)
disengagementScore Disengagement score (DS)
Description
Disengagement score is defined as
(RPFs over ORF)/(RPFs downstream to transcript end)
A pseudo-count of one is added to both the ORF and downstream sums.
Usage
disengagementScore(
grl,
RFP,
GtfOrTx,
RFP.sorted = FALSE,
weight = 1L,
overlapGrl = NULL
)
100 disengagementScore
Arguments
grl a GRangesList object with usually either leaders, cds’, 3’ utrs or ORFs.
RFP RiboSeq reads as GAlignments, GRanges or GRangesList object
GtfOrTx If it is TxDb object transcripts will be extracted using exonsBy(Gtf, by = "tx",
use.names = TRUE). Else it must be GRangesList
RFP.sorted logical (FALSE), an optimizer, have you ran this line: RFP <- sort(RFP[countOverlaps(RFP,
tx, type = "within") > 0]) Normally not touched, for internal optimization
purposes.
weight a vector (default: 1L, if 1L it is identical to countOverlaps()), if single number
(!= 1), it applies for all, if more than one must be equal size of ’reads’. else it
must be the string name of a defined meta column in subject "reads", that gives
number of times a read was found. GRanges("chr1", 1, "+", score = 5), would
mean "score" column tells that this alignment region was found 5 times.
overlapGrl an integer, (default: NULL), if defined must be countOverlaps(grl, RFP), added
for speed if you already have it
Value
a named vector of numeric values of scores
References
doi: 10.1242/dev.098344
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), distToCds(),
distToTSS(), entropy(), floss(), fpkm(), fpkm_calc(), fractionLength(), initiationScore(),
insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(), orfScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
ORF <- GRanges(seqnames = "1",
ranges = IRanges(start = c(1, 10, 20), end = c(5, 15, 25)),
strand = "+")
grl <- GRangesList(tx1_1 = ORF)
tx <- GRangesList(tx1 = GRanges("1", IRanges(1, 50), "+"))
RFP <- GRanges("1", IRanges(c(1,10,20,30,40), width = 3), "+")
disengagementScore(grl, RFP, tx)
distToCds 101
distToCds Get distances between ORF ends and starts of their transcripts cds.
Description
Will calculate distance between each ORF end and begining of the corresponding cds (main ORF).
Matching is done by transcript names. This is applicable practically to the upstream (fiveUTRs)
ORFs only. The cds start site, will be presumed to be on + 1 of end of fiveUTRs.
Usage
distToCds(ORFs, fiveUTRs, cds = NULL)
Arguments
ORFs orfs as GRangesList, names of orfs must be transcript names
fiveUTRs fiveUTRs as GRangesList, remember to use CAGE version of 5’ if you did
CAGE reassignment!
cds cds’ as GRangesList, only add if you have ORFs going into CDS.
Value
an integer vector, +1 means one base upstream of cds, -1 means 2nd base in cds, 0 means orf stops
at cds start.
References
doi: 10.1074/jbc.R116.733899
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToTSS(), entropy(), floss(), fpkm(), fpkm_calc(), fractionLength(), initiationScore(),
insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(), orfScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
grl <- GRangesList(tx1_1 = GRanges("1", IRanges(1, 10), "+"))
fiveUTRs <- GRangesList(tx1 = GRanges("1", IRanges(1, 20), "+"))
distToCds(grl, fiveUTRs)
102 distToTSS
distToTSS Get distances between ORF Start and TSS of its transcript
Description
Matching is done by transcript names. This is applicable practically to any region in Transcript If
ORF is not within specified search space in tx, this function will crash.
Usage
distToTSS(ORFs, tx)
Arguments
ORFs orfs as GRangesList, names of orfs must be txname_[rank]
tx transcripts as GRangesList.
Value
an integer vector, 1 means on TSS, 2 means second base of Tx.
References
doi: 10.1074/jbc.R116.733899
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), entropy(), floss(), fpkm(), fpkm_calc(), fractionLength(), initiationScore(),
insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(), orfScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
grl <- GRangesList(tx1_1 = GRanges("1", IRanges(5, 10), "+"))
tx <- GRangesList(tx1 = GRanges("1", IRanges(2, 20), "+"))
distToTSS(grl, tx)
download.ebi 103
download.ebi Faster download of fastq files
Description
Uses ftp download from vol1 drive on EBI ftp server, for faster download of ERR, SRR or DRR
files. But does not support subsetting or custom settings of files!
Usage
download.ebi(
info,
outdir,
rename = TRUE,
ebiDLMethod = "auto",
timeout = 1000,
BPPARAM = bpparam()
)
Arguments
info character vector of only SRR numbers or a data.frame with SRA metadata in-
formation including the SRR numbers in a column called "Run" or "SRR". Can
be SRR, ERR or DRR numbers. If only SRR numbers can not rename, since no
additional information is given.
outdir directory to store runs, files are named by default (rename = TRUE) by informa-
tion from SRA metadata table, if (rename = FALSE) named according to SRR
numbers.
rename logical or character, default TRUE (Auto guess new names). False: Skip renam-
ing. A character vector of equal size as files wanted can also be given. Priority
of renaming from the metadata is to check for unique names in the Library-
Name column, then the sample_title column if no valid names in LibraryName.
If new names found and still duplicates, will add "_rep1", "_rep2" to make them
unique. If no valid names, will not rename, that is keep the SRR numbers, you
then can manually rename files to something more meaningful.
ebiDLMethod character, default "auto". Which download protocol to use in download.file
when using ebi ftp download. Sometimes "curl" is might not work (the de-
fault auto usually), in those cases use wget. See "method" argument of ?down-
load.file, for more info.
timeout 1000, how many seconds before killing download if still active? Will overwrite
global option until R session is closed. Increase value if you are on a very slow
connection.
BPPARAM how many cores/threads to use? default: bpparam(). To see number of threads
used, do bpparam()$workers
104 download.SRA
Value
character, full filepath of downloaded files
See Also
Other sra: browseSRA(), download.SRA(), download.SRA.metadata(), get_bioproject_candidates(),
install.sratoolkit(), rename.SRA.files()
download.SRA Download read libraries from SRA
Description
Multicore version download, see documentation for SRA toolkit for more information.
Usage
download.SRA(
info,
outdir,
rename = TRUE,
fastq.dump.path = install.sratoolkit(),
settings = paste("--skip-technical", "--split-files"),
subset = NULL,
compress = TRUE,
use.ebi.ftp = is.null(subset),
ebiDLMethod = "auto",
timeout = 1000,
BPPARAM = bpparam()
)
Arguments
info character vector of only SRR numbers or a data.frame with SRA metadata in-
formation including the SRR numbers in a column called "Run" or "SRR". Can
be SRR, ERR or DRR numbers. If only SRR numbers can not rename, since no
additional information is given.
outdir directory to store runs, files are named by default (rename = TRUE) by informa-
tion from SRA metadata table, if (rename = FALSE) named according to SRR
numbers.
rename logical or character, default TRUE (Auto guess new names). False: Skip renam-
ing. A character vector of equal size as files wanted can also be given. Priority
of renaming from the metadata is to check for unique names in the Library-
Name column, then the sample_title column if no valid names in LibraryName.
If new names found and still duplicates, will add "_rep1", "_rep2" to make them
unique. If no valid names, will not rename, that is keep the SRR numbers, you
then can manually rename files to something more meaningful.
download.SRA 105
fastq.dump.path
path to fastq-dump binary, default: path returned from install.sratoolkit()
settings a string of arguments for fastq-dump, default: paste("–gzip", "–skip-technical",
"–split-files")
subset an integer or NULL, default NULL (no subset). If defined as a integer will
download only the first n reads specified by subset. If subset is defined, will
force to use fastq-dump which is slower than ebi download.
compress logical, default TRUE. Download compressed files ".gz".
use.ebi.ftp logical, default: is.null(subset). Use ORFiks much faster download function
that only works when subset is null, if subset is defined, it uses fastqdump, it
is slower but supports subsetting. Force it to use fastqdump by setting this to
FALSE.
ebiDLMethod character, default "auto". Which download protocol to use in download.file
when using ebi ftp download. Sometimes "curl" is might not work (the de-
fault auto usually), in those cases use wget. See "method" argument of ?down-
load.file, for more info.
timeout 1000, how many seconds before killing download if still active? Will overwrite
global option until R session is closed. Increase value if you are on a very slow
connection.
BPPARAM how many cores/threads to use? default: bpparam(). To see number of threads
used, do bpparam()$workers
Value
a character vector of download files filepaths
References
https://ncbi.github.io/sra-tools/fastq-dump.html
See Also
Other sra: browseSRA(), download.SRA.metadata(), download.ebi(), get_bioproject_candidates(),
install.sratoolkit(), rename.SRA.files()
Examples
SRR <- c("SRR453566") # Can be more than one
## Simple single SRR run of YEAST
outdir <- tempdir() # Specify output directory
# Download, get 5 first reads
#download.SRA(SRR, outdir, rename = FALSE, subset = 5)
## Using metadata column to get SRR numbers and to be able to rename samples
outdir <- tempdir() # Specify output directory
info <- download.SRA.metadata("SRP226389", outdir) # By study id
## Download, 5 first reads of each library and rename
106 download.SRA.metadata
#files <- download.SRA(info, outdir, subset = 5)
#Biostrings::readDNAStringSet(files[1], format = "fastq")
## Download full libraries of experiment
## (note, this will take some time to download!)
#download.SRA(info, outdir)
download.SRA.metadata Downloads metadata from SRA
Description
Given a experiment identifier, query information from different locations of SRA to get a complete
metadata table of the experiment. It first finds Runinfo for each library, then sample info, if pubmed
id is not found searches for that and searches for author through pubmed.
Usage
download.SRA.metadata(
SRP,
outdir = tempdir(),
remove.invalid = TRUE,
auto.detect = FALSE,
abstract = "printsave",
force = FALSE,
rich.format = FALSE
)
Arguments
SRP a string, a study ID as either the PRJ, SRP, ERP, DRPor GSE of the study,
examples would be "SRP226389" or "ERP116106". If GSE it will try to convert
to the SRP to find the files. The call works as long the runs are registered on
the efetch server, as their is a linked SRP link from bioproject or GSE. Example
which fails is "PRJNA449388", which does not have a linking like this.
outdir directory to save file, default: tempdir(). The file will be called "SraRunInfo_SRP.csv",
where SRP is the SRP argument. We advice to use bioproject IDs "PRJNA...".
The directory will be created if not existing.
remove.invalid logical, default TRUE. Remove Runs with 0 reads (spots)
auto.detect logical, default FALSE. If TRUE, ORFik will add additional columns:
LIBRARYTYPE: (is this Ribo-seq or mRNA-seq, CAGE etc),
REPLICATE: (is this replicate 1, 2 etc),
STAGE: (Which time point, cell line or tissue is this, HEK293, TCP-1, 24hpf
etc),
CONDITION: (is this Wild type control or a mutant etc).
These values are only qualified guesses from the metadata, so always double
check!
download.SRA.metadata 107
abstract character, default "printsave". If abstract for project exists, print and save it (save
the file to same directory as runinfo). Alternatives: "print", Only print first time
downloaded, will not be able to print later.
save" save it, no print
"no" skip download of abstract
force logical, default FALSE. If TRUE, will redownload all files needed even though
they exists. Useuful if you wanted auto.detection, but already downloaded with-
out it.
rich.format logical, default FALSE. If TRUE, will fetch all Experiment and Sample at-
tributes. It means, that different studies can have different set of columns if
set to TRUE.
Details
A common problem is that the project is not linked to an article, you will then not get a pubmed id.
The algorithm works like this:
If GEO identifier, find the SRP.
Then search Entrez for project and get sample identifier.
From that extract the run information and collect into a final table.
Value
a data.table of the metadata, 1 row per sample, SRR run number defined in ’Run’ column.
References
doi: 10.1093/nar/gkq1019
See Also
Other sra: browseSRA(), download.SRA(), download.ebi(), get_bioproject_candidates(),
install.sratoolkit(), rename.SRA.files()
Examples
## Originally on SRA
download.SRA.metadata("SRP226389")
## Now try with auto detection (guessing additional library info)
## Need to specify output dir as tempfile() to re-download
#download.SRA.metadata("SRP226389", tempfile(), auto.detect = TRUE)
## Originally on ENA (RCP-seq data)
# download.SRA.metadata("ERP116106")
## Originally on GEO (GSE) (save to directory to keep info with fastq files)
# download.SRA.metadata("GSE61011")
## Bioproject ID
# download.SRA.metadata("PRJNA231536")
108 downstreamFromPerGroup
downstreamFromPerGroup
Get rest of objects downstream (inclusive)
Description
Per group get the part downstream of position. downstreamFromPerGroup(tx, startSites(threeUTRs,
asGR = TRUE)) will return the 3’ utrs per transcript as GRangesList, usually used for interesting
parts of the transcripts.
Usage
downstreamFromPerGroup(
tx,
downstreamFrom,
is.circular = all(isCircular(tx) %in% TRUE)
)
Arguments
tx a GRangesList, usually of Transcripts to be changed
downstreamFrom a vector of integers, for each group in tx, where is the new start point of first
valid exon.
is.circular logical, default FALSE if not any is: all(isCircular(grl) Where grl is the ranges
checked. If TRUE, allow ranges to extend below position 1 on chromosome.
Since circular genomes can have negative coordinates.
Details
If you don’t want to include the points given in the region, use downstreamOfPerGroup
Value
a GRangesList of downstream part
See Also
Other GRanges: assignFirstExonsStartSite(), assignLastExonsStopSite(), downstreamOfPerGroup(),
upstreamFromPerGroup(), upstreamOfPerGroup()
downstreamN 109
downstreamN Restrict GRangesList
Description
Will restrict GRangesList to ‘N‘ bp downstream from the first base.
Usage
downstreamN(grl, firstN = 150L)
Arguments
grl (GRangesList)
firstN (integer) Allow only this many bp downstream, maximum.
Value
a GRangesList of reads restricted to firstN and tiled by 1
downstreamOfPerGroup Get rest of objects downstream (exclusive)
Description
Per group get the part downstream of position. downstreamOfPerGroup(tx, stopSites(cds, asGR =
TRUE)) will return the 3’ utrs per transcript as GRangesList, usually used for interesting parts of
the transcripts.
Usage
downstreamOfPerGroup(tx, downstreamOf)
Arguments
tx a GRangesList, usually of Transcripts to be changed
downstreamOf a vector of integers, for each group in tx, where is the new start point of first
valid exon. Can also be a GRangesList, then stopsites will be used.
Details
If you want to include the points given in the region, use downstreamFromPerGroup
Value
a GRangesList of downstream part
110 DTEG.analysis
See Also
Other GRanges: assignFirstExonsStartSite(), assignLastExonsStopSite(), downstreamFromPerGroup(),
upstreamFromPerGroup(), upstreamOfPerGroup()
DTEG.analysis Run differential TE analysis
Description
Expression analysis of 2 dimensions, usually Ribo-seq vs RNA-seq.
Using an equal reimplementation of the deltaTE algorithm (see reference).
Creates a total of 3 DESeq models (given x is the target.contrast argument) (usually ’condition’
column) and libraryType is RNA-seq and Ribo-seq):
1. Ribo-seq model: design = ~ x (differences between the x groups in Ribo-seq)
2. RNA-seq model: design = ~ x (differences between the x groups in RNA-seq)
3. TE model: design = ~ x + libraryType + libraryType:x (differences between the x and libraryType
groups and the interaction between them)
You need at least 2 groups and 2 replicates per group. By default, the Ribo-seq counts will be over
CDS and RNA-seq counts over whole mRNAs, per transcript.
Usage
DTEG.analysis(
df.rfp,
df.rna,
output.dir = QCfolder(df.rfp),
target.contrast = design[1],
design = ORFik::design(df.rfp),
p.value = 0.05,
RFP_counts = countTable(df.rfp, "cds", type = "summarized"),
RNA_counts = countTable(df.rna, "mrna", type = "summarized"),
batch.effect = FALSE,
pairs = combn.pairs(unlist(df.rfp[, design])),
plot.title = "",
plot.ext = ".pdf",
width = 6,
height = 6,
dot.size = 0.4,
relative.name = paste0("DTEG_plot", plot.ext),
complex.categories = FALSE
)
Arguments
df.rfp a experiment of usually Ribo-seq or 80S from TCP-seq. (the numerator of the
experiment, usually having a primary role)
DTEG.analysis 111
df.rna a experiment of usually RNA-seq. (the denominator of the experiment, usually
having a normalizing function)
output.dir character, default QCfolder(df.rfp). output.dir directory to save plots, plot
will be named "TE_between". If NULL, will not save.
target.contrast
a character vector, default design[1]. The column in the ORFik experiment that
represent the comparison contrasts. By default: the first design factor of the full
experimental design. This is the factor you will do the comparison on. DESeq
will normalize the counts based on the full design, but the log fold change values
will be based on this contrast only. It is usually the ’condition’ column.
design a character vector, default design(df.rfp). The full experiment design. Which
factors have more than 1 level. Example: stage column are all HEK293, so it
can not be a design factor. The condition column has 2 possible values, WT
and mutant, so it is a factor of the experiment. Replicates column is not part
of design, that is inserted later with setting batch.effect = TRUE. Library type
’libtype’ column, can also no be part of initial design, it is always added inside
the function, after initial setup.
p.value a numeric, default 0.05 in interval (0,1) or "" to not show. What p-value used for
the analysis? Will be shown as a caption.
RFP_counts a SummarizedExperiment, default: countTable(df.rfp, "cds", type = "summarized"),
unshifted libraries, all transcript CDSs. If you have pshifted reads and countTa-
bles, do: countTable(df.rfp, "cds", type = "summarized", count.folder
= "pshifted") Assign a subset if you don’t want to analyze all genes. It is rec-
ommended to not subset, to give DESeq2 data for variance analysis.
RNA_counts a SummarizedExperiment, default: countTable(df.rna, "mrna", type = "summa-
rized"), all transcripts. Assign a subset if you don’t want to analyze all genes. It
is recommended to not subset, to give DESeq2 data for variance analysis.
batch.effect logical, default TRUE. Makes replicate column of the experiment part of the
design.
If you believe you might have batch effects, keep as TRUE. Batch effect usually
means that you have a strong variance between biological replicates. Check
out pcaExperiment and see if replicates cluster together more than the design
factor, to verify if you need to set it to TRUE.
pairs list of character pairs, the experiment contrasts. Default: combn.pairs(unlist(df.rfp[,
target.contrast])
plot.title title for plots, usually name of experiment etc
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg".
width numeric, default 6 (in inches)
height numeric, default 6 (in inches)
dot.size numeric, default 0.4, size of point dots in plot.
relative.name character, Default: paste0("DTEG_plot", plot.ext) Relative name of file to
be saved in folder specified in output.dir. Change to .pdf if you want pdf file
instead of png.
complex.categories
logical, default FALSE. Seperate into more groups, will add Inverse (opposite
diagonal of mRNA abundance) and Expression (only significant mRNA-seq)
112 DTEG.analysis
Details
Log fold changes and p-values are created from a Walds test on the comparison contrast described
bellow. The RNA-seq and Ribo-seq LFC values are shrunken using DESeq2::lfcShrink(type =
"normal"). Note that the TE LFC values are not shrunken (as following specifications from deltaTE
paper)
Analysis is done between each possible combination of levels in the target contrast If target con-
trast is condition column, with factor levels: WT, mut1 and mut2 with 3 replicates each. You get
comparison of WT vs mut1, WT vs mut2 and mut1 vs mut2.
The respective result categories are defined as: (given a user defined p value, shown here as 0.05):
1. Translation - te.p.adj < 0.05 & rfp.p.adj < 0.05 & rna.p.adj > 0.05
2. mRNA abundance - te.p.adj > 0.05 & rfp.p.adj < 0.05 & rna.p.adj > 0.05
3. Buffering - te.p.adj < 0.05 & rfp.p.adj > 0.05 & rna.p.adj > 0.05
Buffering will be broken down into sub-categories if you set complex.categories = TRUE See Fig-
ure 1 in the reference article for a clear definition of the groups!
If you do not need isoform variants, subset to longest isoform per gene either before or in the re-
turned object (See examples). If you do not have RNA-seq controls, you can still use DESeq on
Ribo-seq alone.
The LFC values are shrunken by lfcShrink(type = "normal").
Remember that DESeq by default can not do global change analysis, it can only find subsets with
changes in LFC!
Value
a data.table with columns: (contrast variable, gene id, regulation status, log fold changes, p.adjust
values, mean counts)
References
doi: 10.1002/cpmb.108
See Also
Other DifferentialExpression: DEG.plot.static(), DEG_model(), DTEG.plot(), te.table(),
te_rna.plot()
Examples
## Simple example (use ORFik template, then split on Ribo and RNA)
df <- ORFik.template.experiment()
df.rfp <- df[df$libtype == "RFP",]
df.rna <- df[df$libtype == "RNA",]
design(df.rfp) # The experimental design, per libtype
design(df.rfp)[1] # Default target contrast
#dt <- DTEG.analysis(df.rfp, df.rna)
DTEG.plot 113
## If you want to use the pshifted libs for analysis:
#dt <- DTEG.analysis(df.rfp, df.rna,
# RFP_counts = countTable(df.rfp, region = "cds",
# type = "summarized", count.folder = "pshifted"))
## Restrict DTEGs by log fold change (LFC):
## subset to abs(LFC) < 1.5 for both rfp and rna
#dt[abs(rfp) < 1.5 & abs(rna) < 1.5, Regulation := "No change"]
## Only longest isoform per gene:
#tx_longest <- filterTranscripts(df.rfp, 0, 1, 0)
#dt <- dt[id %in% tx_longest,]
## Convert to gene id
#dt[, id := txNamesToGeneNames(id, df.rfp)]
## To get by gene symbol, use biomaRt conversion
## To flip directionality of contrast pair nr 2:
#design <- "condition"
#pairs <- combn.pairs(unlist(df.rfp[, design])
#pairs[[2]] <- rev(pars[[2]])
#dt <- DTEG.analysis(df.rfp, df.rna,
# RFP_counts = countTable(df.rfp, region = "cds",
# type = "summarized", count.folder = "pshifted"),
# pairs = pairs)
DTEG.plot Plot DTEG result
Description
For explanation of plot catagories, see DTEG.analysis
Usage
DTEG.plot(
dt,
output.dir = NULL,
p.value = 0.05,
plot.title = "",
plot.ext = ".pdf",
width = 6,
height = 6,
dot.size = 0.4,
xlim = "bidir.max",
ylim = "bidir.max",
relative.name = paste0("DTEG_plot", plot.ext)
)
114 DTEG.plot
Arguments
dt a data.table with the results from DTEG.analysis
output.dir a character path, default NULL(no save), or a directory to save to a file. Relative
name of file, specified by ’relative.name’ argument.
p.value a numeric, default 0.05 in interval (0,1) or "" to not show. What p-value used for
the analysis? Will be shown as a caption.
plot.title title for plots, usually name of experiment etc
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg".
width numeric, default 6 (in inches)
height numeric, default 6 (in inches)
dot.size numeric, default 0.4, size of point dots in plot.
xlim numeric vector or character preset, default: "bidir.max" (Equal in both + / -
direction, using max value + 0.5 of rna column in dt). If you want ggplot to
decide limit, set to "auto". For numeric vector, specify min and max x limit: like
c(-5, 5)
ylim numeric vector or character preset, default: "bidir.max" (Equal in both + / -
direction, using max value + 0.5 of rfp column in dt). If you want ggplot to
decide limit, set to "auto". For numeric vector, specify min and max y limit: like
c(-10, 10)
relative.name character, Default: paste0("DTEG_plot", plot.ext) Relative name of file to
be saved in folder specified in output.dir. Change to .pdf if you want pdf file
instead of png.
Value
a ggplot object
See Also
Other DifferentialExpression: DEG.plot.static(), DEG_model(), DTEG.analysis(), te.table(),
te_rna.plot()
Examples
df <- ORFik.template.experiment()
df.rfp <- df[df$libtype == "RFP",]
df.rna <- df[df$libtype == "RNA",]
#dt <- DTEG.analysis(df.rfp, df.rna)
#Default scaling
#DTEG.plot(dt)
#Manual scaling
#DTEG.plot(dt, xlim = c(-2, 2), ylim = c(-2, 2))
entropy 115
entropy Percentage of maximum entropy
Description
Calculates percentage of maximum entropy of the ‘reads‘ coverage over each ORF in ‘grl‘ group.
The entropy value per group is a real number in the interval (0:1), where 0 indicates no variance in
reads over all codons of group For example c(0,0,0,0) has 0 entropy, since no reads overlap.
Interval: [0]: No reads or all reads in 1 place
Interval: [0.01-0.99]: >= 2 positions covered
Interval: [1]: all positions covered perfectly in frame
Usage
entropy(grl, reads, weight = 1L, is.sorted = FALSE, overlapGrl = NULL)
Arguments
grl a GRangesList object can be either transcripts, 5’ utrs, cds’, 3’ utrs or ORFs as
a special case (uORFs, potential new cds’ etc). If regions are not spliced you
can send a GRanges object.
reads a GAlignments, GRanges or GRangesList object, usually of RiboSeq, RnaSeq,
CageSeq, etc.
weight a vector (default: 1L, if 1L it is identical to countOverlaps()), if single number
(!= 1), it applies for all, if more than one must be equal size of ’reads’. else it
must be the string name of a defined meta column in subject "reads", that gives
number of times a read was found. GRanges("chr1", 1, "+", score = 5), would
mean "score" column tells that this alignment region was found 5 times.
is.sorted logical (FALSE), is grl sorted. That is + strand groups in increasing ranges
(1,2,3), and - strand groups in decreasing ranges (3,2,1)
overlapGrl an integer, (default: NULL), if defined must be countOverlaps(grl, RFP), added
for speed if you already have it
Value
A numeric vector containing one entropy value per element in ‘grl‘
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), floss(), fpkm(), fpkm_calc(), fractionLength(), initiationScore(),
insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(), orfScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
116 envExp,experiment-method
Examples
# a toy example with ribo-seq p-shifted reads
ORF <- GRangesList(tx1 = GRanges("1", IRanges(1, width = 9), "+"))
entropy(ORF, GRanges()) # 0
entropy(ORF, GRanges("1", IRanges(c(1)), "+")) # 0
entropy(ORF, GRanges("1", IRanges(c(1,4,6,7)), "+")) # 0.94
entropy(ORF, GRanges("1", IRanges(c(1,4,7)), "+", score = c(1,2,1)),
weight = "score") # 0.94
entropy(ORF, GRanges("1", IRanges(c(1,4,7)), "+")) # Perfect = 1
envExp Get ORFik experiment environment
Description
More correctly, get the pointer reference, default is .GlobalEnv
Usage
envExp(x)
Arguments
x an ORFik experiment
Value
environment pointer, name of environment: pointer
envExp,experiment-method
Get ORFik experiment environment
Description
More correctly, get the pointer reference, default is .GlobalEnv
Usage
## S4 method for signature 'experiment'
envExp(x)
Arguments
x an ORFik experiment
Value
environment pointer, name of environment: pointer
envExp<- 117
envExp<- Set ORFik experiment environment
Description
More correctly, set the pointer reference, default is .GlobalEnv
Usage
envExp(x) <- value
Arguments
x an ORFik experiment
value environment pointer to assign to experiment
Value
an ORFik experiment with updated environment
envExp<-,experiment-method
Set ORFik experiment environment
Description
More correctly, set the pointer reference, default is .GlobalEnv
Usage
## S4 replacement method for signature 'experiment'
envExp(x) <- value
Arguments
x an ORFik experiment
value environment pointer to assign to experiment
Value
an ORFik experiment with updated environment
118 exists.ftp.file.fast
exists.ftp.dir.fast A fast ftp directory check
Description
Check if ftp directory exists
Usage
exists.ftp.dir.fast(url.dir, report.error = FALSE)
Arguments
url.dir character, url to a ftp directory.
report.error logical, FALSE. If TRUE, stop and report error.
Value
logical, TRUE if url directory exists
exists.ftp.file.fast A fast ftp file check
Description
Check if ftp file exists
Usage
exists.ftp.file.fast(url, report.error = FALSE)
Arguments
url character, url to a ftp file
report.error logical, FALSE. If TRUE, stop and report error.
Value
logical, TRUE if file exists
experiment-class 119
experiment-class experiment class definition
Description
It is an object that simplify and error correct your NGS workflow, creating a single R object that
stores and controls all results relevant to a specific experiment.
It contains following important parts:
• filepaths : and info for each library in the experiment (for multiple files formats: bam, bed,
wig, ofst, ..)
• genome : annotation files of the experiment (fasta genome, index, gtf, txdb)
• organism : name (for automatic GO, sequence analysis..)
• description : and author information (list.experiments(), show all experiments you have made
with ORFik, easy to find and load them later)
• API : ORFik supports a rich API for using the experiment, like outputLibs(experiment, type =
"wig") will load all libraries converted to wig format into R, loadTxdb(experiment) will load
the txdb (gtf) of experiment, transcriptWindow() will automatically plot metacoverage of all
libraries in the experiment, countTable(experiment) will load count tables, etc..)
• Safety : It is also a safety in that it verifies your experiments contain no duplicate, empty or
non-accessible files.
Act as a way of extension of SummarizedExperiment by allowing more ease to find not only counts,
but rather information about libraries, and annotation, so that more tasks are possible. Like cover-
age per position in some transcript etc.
## Constructor:
Simplest way to make is to call:
create.experiment(dir)
On some folder with NGS libraries (usually bam files) and see what you get. Some of the fields
might be needed to fill in manually. Each resulting row must be unique (not including filepath, they
are always unique), that means if it has replicates then that must be said explicit. And all filepaths
must be unique and have files with size > 0.
Here all the columns in the experiment will be described: name (column info): examples
libtype library type: rna-seq, ribo-seq, CAGE etc
stage stage or tissue: 64cell, Shield, HEK293
rep replicate: 1,2,3 etc
condition treatment or condition: : WT (wild-type), control, target, mzdicer, starved
fraction fraction of total: 18, 19 (TCP / RCP fractions), or other ways to split library.
filepath Full filepath to file
reverse optional: 2nd filepath or info, only used if paired files
120 experiment-class
Details
Special rules:
Supported:
Single/paired end bam, bed, wig, ofst + compressions of these
The reverse column of the experiments says "paired-end" if bam file. If a pair of wig files, forward
and reverse strand, reverse is filepath to ’-’ strand wig file. Paired forward / reverse wig files, must
have same name except _forward / _reverse in name
Paired end bam, when creating experiment, set pairedEndBam = c(T, T, T, F). For 3 paired end
libraries, then one single end.
Naming: Will try to guess naming for tissues / stages, replicates etc. If it finds more than one hit for
one file, it will not guess. Always check that it guessed correctly.
Value
a ORFik experiment
See Also
Other ORFik_experiment: ORFik.template.experiment(), ORFik.template.experiment.zf(),
bamVarName(), create.experiment(), filepath(), libraryTypes(), organism,experiment-method,
outputLibs(), read.experiment(), save.experiment(), validateExperiments()
Examples
## To see an internal ORFik example
df <- ORFik.template.experiment()
## See libraries in experiment
df
## See organism of experiment
organism(df)
## See file paths in experiment
filepath(df, "default")
## Output NGS libraries in R, to .GlobalEnv
#outputLibs(df)
## Output cds of experiment annotation
#loadRegion(df, "cds")
## This is how to make it:
## Not run:
library(ORFik)
# 1. Update path to experiment data directory (bam, bed, wig files etc)
exp_dir = "/data/processed_data/RNA-seq/Lee_zebrafish_2013/aligned/"
# 2. Set a short character name for experiment, (Lee et al 2013 -> Lee13, etc)
exper_name = "Lee13"
# 3. Create a template experiment (gtf and fasta genome)
temp <- create.experiment(exp_dir, exper_name, saveDir = NULL,
txdb = "/data/references/Zv9_zebrafish/Danio_rerio.Zv9.79.gtf",
fa = "/data/references/Zv9_zebrafish/Danio_rerio.Zv9.fa",
experiment.colors 121
organism = "Homo sapiens")
# 4. Make sure each row(sample) is unique and correct
# You will get a view open now, check the data.frame that it is correct:
# library type (RNA-seq, Ribo-seq), stage, rep, condition, fraction.
# Let say it did not figure out it is RNA-seq, then we do:"
temp[5:6, 1] <- "RNA" # [row 5 and 6, col 1] are library types
# You can also do this in your spread sheet program (excel, libre office)
# Now save new version, if you did not use spread sheet.
saveName <- paste0("/data/processed_data/experiment_tables_for_R/",
exper_name,".csv")
save.experiment(temp, saveName)
# 5. Load experiment, this will validate that you actually made it correct
df <- read.experiment(saveName)
# Set experiment name not to be assigned in R variable names
df@expInVarName <- FALSE
df
## End(Not run)
experiment.colors Decide color for libraries by grouping
Description
Pick the grouping wanted for colors, by default only group by libtype. Like RNA-seq(skyblue4)
and Ribo-seq(orange).
Usage
experiment.colors(
df,
color_list = "default",
skip.libtype = FALSE,
skip.stage = TRUE,
skip.replicate = TRUE,
skip.fraction = TRUE,
skip.condition = TRUE
)
Arguments
df an ORFik experiment
122 export.bed12
color_list a character vector of colors, default "default". That is the vector c("skyblue4",
’orange’, "green", "red", "gray", "yellow", "blue", "red2", "orange3"). Picks
number of colors needed to make groupings have unique color
skip.libtype a logical (FALSE), don’t include libtype
skip.stage a logical (FALSE), don’t include stage in variable name.
skip.replicate a logical (FALSE), don’t include replicate in variable name.
skip.fraction a logical (FALSE), don’t include fraction
skip.condition a logical (FALSE), don’t include condition in variable name.
Value
a character vector of colors
export.bed12 Export as bed12 format
Description
bed format for multiple exons per group, as transcripts. Can be use as alternative as a sparse .gff
format for ORFs. Can be direct input for ucsc browser or IGV
Usage
export.bed12(grl, file, rgb = 0)
Arguments
grl A GRangesList
file a character path to valid output file name
rgb integer vector, default (0), either single integer or vector of same size as grl to
specify groups. It is adviced to not use more than 8 different groups
Details
If grl has no names, groups will be named 1,2,3,4..
Value
NULL (File is saved as .bed)
See Also
Other utils: bedToGR(), convertToOneBasedRanges(), export.bigWig(), export.fstwig(),
export.wiggle(), fimport(), findFa(), fread.bed(), optimizeReads(), readBam(), readBigWig(),
readWig()
export.bedo 123
Examples
grl <- GRangesList(GRanges("1", c(1,3,5), "+"))
# export.bed12(grl, "output/path/orfs.bed")
export.bedo Store GRanges object as .bedo
Description
.bedo is .bed ORFik, an optimized bed format for coverage reads with read lengths .bedo is a text
based format with columns (6 maximum):
1. chromosome
2. start
3. end
4. strand
5. ref width (cigar # M’s, match/mismatch total)
6. duplicates of that read
Usage
export.bedo(object, out)
Arguments
object a GRanges object
out a character, location on disc (full path)
Details
Positions are 1-based, not 0-based as .bed. End will be removed if all ends equals all starts. Import
with import.bedo
Value
NULL, object saved to disc
124 export.bigWig
export.bedoc Store GAlignments object as .bedoc
Description
A fast way to store, load and use bam files. (we now recommend using link{export.ofst} in-
stead!)
.bedoc is .bed ORFik, an optimized bed format for coverage reads with cigar and replicate number.
.bedoc is a text based format with columns (5 maximum):
1. chromosome
2. cigar: (cigar # M’s, match/mismatch total)
3. start (left most position)
4. strand (+, -, *)
5. score: duplicates of that read
Usage
export.bedoc(object, out)
Arguments
object a GAlignments object
out a character, location on disc (full path)
Details
Positions are 1-based, not 0-based as .bed. Import with import.bedoc
Value
NULL, object saved to disc
export.bigWig Export as bigWig format
Description
Will create 2 files, 1 for + strand (*_forward.bigWig) and 1 for - strand (*_reverse.bigWig). If all
ranges are * stranded, will output 1 file. Can be direct input for ucsc browser or IGV
export.bigWig 125
Usage
export.bigWig(
x,
file,
split.by.strand = TRUE,
is_pre_collapsed = FALSE,
seq_info = seqinfo(x)
)
Arguments
x A GRangesList, GAlignment GAlignmentPairs with score column. Will be con-
verted to 5’ end position of original range. If score column does not exist, will
group ranges and give replicates as score column. Since bigWig needs a score
column to represent counts!
file a character path to valid output file name
split.by.strand
logical, default TRUE. Split bigWig into 2 files, one for each strand.
is_pre_collapsed
logical, default FALSE. Have you already collapsed reads with collapse.by.scores,
so each positions is only in 1 GRanges object with a score column per readlength?
Set to TRUE, only if you are sure, will give a speedup.
seq_info a Seqinfo object, default seqinfo(x). Must have non NA seqlengths defined!
Value
invisible(NULL) (File is saved as 2 .bigWig files)
References
https://genome.ucsc.edu/goldenPath/help/bigWig.html
See Also
Other utils: bedToGR(), convertToOneBasedRanges(), export.bed12(), export.fstwig(), export.wiggle(),
fimport(), findFa(), fread.bed(), optimizeReads(), readBam(), readBigWig(), readWig()
Examples
x <- c(GRanges("1", c(1,3,5), "-"), GRanges("1", c(1,3,5), "+"))
seqlengths(x) <- 10
file <- file.path(tempdir(), "rna.bigWig")
# export.bigWig(x, file)
# export.bigWig(covRleFromGR(x), file)
126 export.fstwig
export.fstwig Export as fstwig (fastwig) format
Description
Will create 2 files, 1 for + strand (*_forward.fstwig) and 1 for - strand (*_reverse.fstwig). If all
ranges are * stranded, will output 1 file.
Usage
export.fstwig(
x,
file,
by.readlength = TRUE,
by.chromosome = TRUE,
compress = 50
)
Arguments
x A GRangesList, GAlignment GAlignmentPairs with score column or coverage
RLElist Will be converted to 5’ end position of original range. If score column
does not exist, will group ranges and give replicates as score column.
file a character path to valid output file name
by.readlength logical, default TRUE
by.chromosome logical, default TRUE
compress value in the range 0 to 100, indicating the amount of compression to use. Lower
values mean larger file sizes. The default compression is set to 50.
Value
invisible(NULL) (File is saved as 2 .fstwig files)
References
"TODO"
See Also
Other utils: bedToGR(), convertToOneBasedRanges(), export.bed12(), export.bigWig(), export.wiggle(),
fimport(), findFa(), fread.bed(), optimizeReads(), readBam(), readBigWig(), readWig()
export.ofst 127
Examples
x <- c(GRanges("1", c(1,3,5), "-"), GRanges("1", c(1,3,5), "+"))
x$size <- rep(c(28, 29), length.out = length(x))
x$score <- c(5,1,2,5,1,6)
seqlengths(x) <- 5
# export.fstwig(x, "~/Desktop/ribo")
export.ofst Store GRanges / GAlignments object as .ofst
Description
A much faster way to store, load and use bam files.
.ofst is ORFik fast serialized object, an optimized format for coverage reads with cigar and replicate
number. It uses the fst format as back-end: fst-package.
A .ofst ribo seq file can compress the information in a bam file from 5GB down to a few MB. This
new files has super fast reading time, only a few seconds, instead of minutes. It also has random
index access possibility of the file.
.ofst is represented as a data.frane format with minimum 4 columns:
1. chromosome
2. start (left most position)
3. strand (+, -, *)
4. width (not added if cigar exists)
5. cigar (not needed if width exists): (cigar # M’s, match/mismatch total)
5. score: duplicates of that read
6. size: qwidth according to reference of read
If file is from GAlignmentPairs, it will contain a cigar1, cigar2 instead of cigar and start1 and
start2 instead of start
Usage
export.ofst(x, ...)
Arguments
x a GRanges, GAlignments or GAlignmentPairs object
... additional arguments for write_fst
Details
Other columns can be named whatever you want and added to meta columns. Positions are 1-based,
not 0-based as .bed. Import with import.ofst
Value
NULL, object saved to disc
128 export.ofst,GAlignmentPairs-method
Examples
## GRanges
gr <- GRanges("1:1-3:-")
# export.ofst(gr, file = "path.ofst")
## GAlignment
# Make input data.frame
df <- data.frame(seqnames = "1", cigar = "3M", start = 1L, strand = "+")
ga <- ORFik:::getGAlignments(df)
# export.ofst(ga, file = "path.ofst")
export.ofst,GAlignmentPairs-method
Store GRanges / GAlignments object as .ofst
Description
A much faster way to store, load and use bam files.
.ofst is ORFik fast serialized object, an optimized format for coverage reads with cigar and replicate
number. It uses the fst format as back-end: fst-package.
A .ofst ribo seq file can compress the information in a bam file from 5GB down to a few MB. This
new files has super fast reading time, only a few seconds, instead of minutes. It also has random
index access possibility of the file.
.ofst is represented as a data.frane format with minimum 4 columns:
1. chromosome
2. start (left most position)
3. strand (+, -, *)
4. width (not added if cigar exists)
5. cigar (not needed if width exists): (cigar # M’s, match/mismatch total)
5. score: duplicates of that read
6. size: qwidth according to reference of read
If file is from GAlignmentPairs, it will contain a cigar1, cigar2 instead of cigar and start1 and
start2 instead of start
Usage
## S4 method for signature 'GAlignmentPairs'
export.ofst(x, file, ...)
Arguments
x a GRanges, GAlignments or GAlignmentPairs object
file a character, location on disc (full path)
... additional arguments for write_fst
export.ofst,GAlignments-method 129
Details
Other columns can be named whatever you want and added to meta columns. Positions are 1-based,
not 0-based as .bed. Import with import.ofst
Value
NULL, object saved to disc
Examples
## GRanges
gr <- GRanges("1:1-3:-")
# export.ofst(gr, file = "path.ofst")
## GAlignment
# Make input data.frame
df <- data.frame(seqnames = "1", cigar = "3M", start = 1L, strand = "+")
ga <- ORFik:::getGAlignments(df)
# export.ofst(ga, file = "path.ofst")
export.ofst,GAlignments-method
Store GRanges / GAlignments object as .ofst
Description
A much faster way to store, load and use bam files.
.ofst is ORFik fast serialized object, an optimized format for coverage reads with cigar and replicate
number. It uses the fst format as back-end: fst-package.
A .ofst ribo seq file can compress the information in a bam file from 5GB down to a few MB. This
new files has super fast reading time, only a few seconds, instead of minutes. It also has random
index access possibility of the file.
.ofst is represented as a data.frane format with minimum 4 columns:
1. chromosome
2. start (left most position)
3. strand (+, -, *)
4. width (not added if cigar exists)
5. cigar (not needed if width exists): (cigar # M’s, match/mismatch total)
5. score: duplicates of that read
6. size: qwidth according to reference of read
If file is from GAlignmentPairs, it will contain a cigar1, cigar2 instead of cigar and start1 and
start2 instead of start
Usage
## S4 method for signature 'GAlignments'
export.ofst(x, file, ...)
130 export.ofst,GRanges-method
Arguments
x a GRanges, GAlignments or GAlignmentPairs object
file a character, location on disc (full path)
... additional arguments for write_fst
Details
Other columns can be named whatever you want and added to meta columns. Positions are 1-based,
not 0-based as .bed. Import with import.ofst
Value
NULL, object saved to disc
Examples
## GRanges
gr <- GRanges("1:1-3:-")
# export.ofst(gr, file = "path.ofst")
## GAlignment
# Make input data.frame
df <- data.frame(seqnames = "1", cigar = "3M", start = 1L, strand = "+")
ga <- ORFik:::getGAlignments(df)
# export.ofst(ga, file = "path.ofst")
export.ofst,GRanges-method
Store GRanges / GAlignments object as .ofst
Description
A much faster way to store, load and use bam files.
.ofst is ORFik fast serialized object, an optimized format for coverage reads with cigar and replicate
number. It uses the fst format as back-end: fst-package.
A .ofst ribo seq file can compress the information in a bam file from 5GB down to a few MB. This
new files has super fast reading time, only a few seconds, instead of minutes. It also has random
index access possibility of the file.
.ofst is represented as a data.frane format with minimum 4 columns:
1. chromosome
2. start (left most position)
3. strand (+, -, *)
4. width (not added if cigar exists)
5. cigar (not needed if width exists): (cigar # M’s, match/mismatch total)
5. score: duplicates of that read
6. size: qwidth according to reference of read
If file is from GAlignmentPairs, it will contain a cigar1, cigar2 instead of cigar and start1 and
start2 instead of start
export.wiggle 131
Usage
## S4 method for signature 'GRanges'
export.ofst(x, file, ...)
Arguments
x a GRanges, GAlignments or GAlignmentPairs object
file a character, location on disc (full path)
... additional arguments for write_fst
Details
Other columns can be named whatever you want and added to meta columns. Positions are 1-based,
not 0-based as .bed. Import with import.ofst
Value
NULL, object saved to disc
Examples
## GRanges
gr <- GRanges("1:1-3:-")
# export.ofst(gr, file = "path.ofst")
## GAlignment
# Make input data.frame
df <- data.frame(seqnames = "1", cigar = "3M", start = 1L, strand = "+")
ga <- ORFik:::getGAlignments(df)
# export.ofst(ga, file = "path.ofst")
export.wiggle Export as wiggle format
Description
Will create 2 files, 1 for + strand (*_forward.wig) and 1 for - strand (*_reverse.wig). If all ranges
are * stranded, will output 1 file. Can be direct input for ucsc browser or IGV
Usage
export.wiggle(x, file)
Arguments
x A GRangesList, GAlignment GAlignmentPairs with score column. Will be con-
verted to 5’ end position of original range. If score column does not exist, will
group ranges and give replicates as score column.
file a character path to valid output file name
132 extendLeaders
Value
invisible(NULL) (File is saved as 2 .wig files)
References
https://genome.ucsc.edu/goldenPath/help/wiggle.html
See Also
Other utils: bedToGR(), convertToOneBasedRanges(), export.bed12(), export.bigWig(), export.fstwig(),
fimport(), findFa(), fread.bed(), optimizeReads(), readBam(), readBigWig(), readWig()
Examples
x <- c(GRanges("1", c(1,3,5), "-"), GRanges("1", c(1,3,5), "+"))
# export.wiggle(x, "output/path/rna.wig")
extendLeaders Extend the leaders transcription start sites.
Description
Will extend the leaders or transcripts upstream (5’ end) by extension. The extension is general
not relative, that means splicing will not be taken into account. Requires the grl to be sorted
beforehand, use sortPerGroup to get sorted grl.
Usage
extendLeaders(
grl,
extension = 1000L,
cds = NULL,
is.circular = all(isCircular(grl) %in% TRUE)
)
Arguments
grl usually a GRangesList of 5’ utrs or transcripts. Can be used for any extension
of groups.
extension an integer, how much to extend upstream (5’ end). Eiter single value that will
apply for all, or same as length of grl which will give 1 update value per grl
object. Or a GRangesList where start / stops by strand are the positions to use
as new starts.
cds a GRangesList of coding sequences, If you want to extend 5’ leaders down-
stream, to catch upstream ORFs going into cds, include it. It will add first cds
exon to grl matched by names. Do not add for transcripts, as they are already
included.
extendsTSSexons 133
is.circular logical, default FALSE if not any is: all(isCircular(grl) Where grl is the ranges
checked. If TRUE, allow ranges to extend below position 1 on chromosome.
Since circular genomes can have negative coordinates.
Value
an extended GRangeslist
See Also
Other ExtendGenomicRanges: asTX(), coveragePerTiling(), extendTrailers(), reduceKeepAttr(),
tile1(), txSeqsFromFa(), windowPerGroup()
Examples
library(GenomicFeatures)
samplefile <- system.file("extdata", "hg19_knownGene_sample.sqlite",
package = "GenomicFeatures")
txdb <- loadDb(samplefile)
fiveUTRs <- fiveUTRsByTranscript(txdb, use.names = TRUE) # <- extract only 5' leaders
tx <- exonsBy(txdb, by = "tx", use.names = TRUE)
cds <- cdsBy(txdb,"tx",use.names = TRUE)
## extend leaders upstream 1000
extendLeaders(fiveUTRs, extension = 1000)
## now try(extend upstream 1000, add all cds exons):
extendLeaders(fiveUTRs, extension = 1000, cds)
## when extending transcripts, don't include cds' of course,
## since they are already there
extendLeaders(tx, extension = 1000)
## Circular genome (allow negative coordinates)
circular_fives <- fiveUTRs
isCircular(circular_fives) <- rep(TRUE, length(isCircular(circular_fives)))
extendLeaders(circular_fives, extension = 32672841L)
extendsTSSexons Extend first exon of each transcript with length specified
Description
Extend first exon of each transcript with length specified
Usage
extendsTSSexons(fiveUTRs, extension = 1000)
134 extendTrailers
Arguments
fiveUTRs The 5’ leader sequences as GRangesList
extension The number of basses to extend transcripts upstream
Value
GRangesList object of fiveUTRs
extendTrailers Extend the Trailers transcription stop sites
Description
Will extend the trailers or transcripts downstream (3’ end) by extension. The extension is general
not relative, that means splicing will not be taken into account. Requires the grl to be sorted
beforehand, use sortPerGroup to get sorted grl.
Usage
extendTrailers(
grl,
extension = 1000L,
is.circular = all(isCircular(grl) %in% TRUE)
)
Arguments
grl usually a GRangesList of 3’ utrs or transcripts. Can be used for any extension
of groups.
extension an integer, how much to extend downstream (3’ end). Eiter single value that
will apply for all, or same as length of grl which will give 1 update value per grl
object. Or a GRangesList where start / stops sites by strand are the positions to
use as new starts.
is.circular logical, default FALSE if not any is: all(isCircular(grl) Where grl is the ranges
checked. If TRUE, allow ranges to extend below position 1 on chromosome.
Since circular genomes can have negative coordinates.
Value
an extended GRangeslist
See Also
Other ExtendGenomicRanges: asTX(), coveragePerTiling(), extendLeaders(), reduceKeepAttr(),
tile1(), txSeqsFromFa(), windowPerGroup()
extract_run_id 135
Examples
library(GenomicFeatures)
samplefile <- system.file("extdata", "hg19_knownGene_sample.sqlite",
package = "GenomicFeatures")
txdb <- loadDb(samplefile)
threeUTRs <- threeUTRsByTranscript(txdb) # <- extract only 5' leaders
tx <- exonsBy(txdb, by = "tx", use.names = TRUE)
## now try(extend downstream 1000):
extendTrailers(threeUTRs, extension = 1000)
## Or on transcripts
extendTrailers(tx, extension = 1000)
## Circular genome (allow negative coordinates)
circular_three <- threeUTRs
isCircular(circular_three) <- rep(TRUE, length(isCircular(circular_three)))
extendTrailers(circular_three, extension = 126200008L)[41] # <- negative stop coordinate
extract_run_id Extract SRR/ERR/DRR run IDs from string
Description
Extract SRR/ERR/DRR run IDs from string
Usage
extract_run_id(
x,
search = "(SRR[0-9]+|DRR[0-9]+|ERR[0-9]+)",
only_valid = FALSE
)
Arguments
x character vector to search through.
search the regex search, default: "(SRR[0-9]+|DRR[0-9]+|ERR[0-9]+)"
only_valid logical, default FALSE. If TRUE, return only the hits.
Value
a character vector of run accepted run ids according to search, if only_valid named character vector
for which indices are returned
Examples
search <- c("SRR1230123_absdb", "SRR1241204124_asdasd", "asd_ERR1231230213",
"DRR12412412_asdqwe", "ASDASD_ASDASD", "SRRASDASD")
ORFik:::extract_run_id(search)
ORFik:::extract_run_id(search, only_valid = TRUE)
136 f,covRle-method
f strandMode covRle
Description
strandMode covRle
Usage
f(x)
Arguments
x a covRle object
Value
the forward RleList
f,covRle-method strandMode covRle
Description
strandMode covRle
Usage
## S4 method for signature 'covRle'
f(x)
Arguments
x a covRle object
Value
the forward RleList
filepath 137
filepath Get filepaths to ORFik experiment
Description
If other type than "default" is given and that type is not found (and ’fallback’ is TRUE), it will return
you ofst files, if they do not exist, then default filepaths without warning.
Usage
filepath(
df,
type,
basename = FALSE,
fallback = type %in% c("pshifted", "bed", "ofst", "bedoc", "bedo"),
suffix_stem = "AUTO",
base_folders = libFolder(df)
)
Arguments
df an ORFik experiment
type a character(default: "default"), load files in experiment or some precomputed
variant, like "ofst" or "pshifted". These are made with ORFik:::convertLibs(),
shiftFootprintsByExperiment(), etc. Can also be custom user made folders in-
side the experiments bam folder. It acts in a recursive manner with priority: If
you state "pshifted", but it does not exist, it checks "ofst". If no .ofst files, it uses
"default", which always must exists.
Presets are (folder is relative to default lib folder, some types fall back to other
formats if folder does not exist):
- "default": load the original files for experiment, usually bam.
- "ofst": loads ofst files from the ofst folder, relative to lib folder (falls back to
default)
- "pshifted": loads ofst, wig or bigwig from pshifted folder (falls back to ofst,
then default)
- "cov": Load covRle objects from cov_RLE folder (fail if not found)
- "covl": Load covRleList objects, from cov_RLE_List folder (fail if not found)
- "bed": Load bed files, from bed folder (falls back to default)
- Other formats must be loaded directly with fimport
basename logical, default (FALSE). Get relative paths instead of full. Only use for inspec-
tion!
fallback logical, default: type If TRUE, will use type fallback, see above for info.
suffix_stem character, default "AUTO". Which is "" for all except type = "pshifted". Then
it is "_pshifted" appended to end of names before format. Can be vector, then it
searches suffixes in priority: so if you insert c("_pshifted", ""), it will look for
suffix _pshifted, then the empty suffix.
138 filterCage
base_folders character vector, default libFolder(df), path to base folder to search for library
variant directories. If single path (length == 1), it will apply to all libraries
in df. If df is a collection, an experiment where libraries are put in different
folders and library variants like pshifted are put inside those respective folders,
set base_folders = libFolder(df, mode = "all")
Details
For pshifted libraries, if "pshifted" is specified as type: if if multiple formats exist it will use a
priority: ofst -> bigwig -> wig -> bed. For formats outside default, all files must be stored in the
directory of the first file: base_folder <- libFolder(df)
Value
a character vector of paths, or a list of character with 2 paths per, if paired libraries exists
See Also
Other ORFik_experiment: ORFik.template.experiment(), ORFik.template.experiment.zf(),
bamVarName(), create.experiment(), experiment-class, libraryTypes(), organism,experiment-method,
outputLibs(), read.experiment(), save.experiment(), validateExperiments()
Examples
df <- ORFik.template.experiment()
filepath(df, "default")
# Subset
filepath(df[9,], "default")
# Other format path
filepath(df[9,], "ofst")
## If you have pshifted files, see shiftFootprintsByExperiment()
filepath(df[9,], "pshifted") # <- falls back to ofst
filterCage Filter peak of cage-data by value
Description
Filter peak of cage-data by value
Usage
filterCage(cage, filterValue = 1, fiveUTRs = NULL, preCleanup = TRUE)
filterExtremePeakGenes 139
Arguments
cage Either a filePath for the CageSeq file as .bed .bam or .wig, with possible com-
pressions (".gzip", ".gz", ".bgz"), or already loaded CageSeq peak data as GRanges
or GAlignment. NOTE: If it is a .bam file, it will add a score column by run-
ning: convertToOneBasedRanges(cage, method = "5prime", addScoreColumn =
TRUE) The score column is then number of replicates of read, if score column
is something else, like read length, set the score column to NULL first.
filterValue The minimum number of reads on cage position, for it to be counted as possible
new tss. (represented in score column in CageSeq data) If you already filtered,
set it to 0.
fiveUTRs a GRangesList (NULL), if added will filter out cage reads by these following
rules: all reads in region (-5:-1, 1:5) for each tss will be removed, removes
noise.
preCleanup logical (TRUE), if TRUE, remove all reads in region (-5:-1, 1:5) of all original
tss in leaders. This is to keep original TSS if it is only +/- 5 bases from the
original.
Value
the filtered GRanges object
filterExtremePeakGenes
Filter out transcript by a median filter
Description
For removing very extreme peaks in coverage plots, use high quantiles, like 99. Used to make your
plots look better, by removing extreme peaks.
Usage
filterExtremePeakGenes(
tx,
reads,
upstream = NULL,
downstream = NULL,
multiplier = "0.99",
min_cutoff = "0.999",
pre_filter_minimum = 0,
average = "median"
)
140 filterTranscripts
Arguments
tx a GRangesList
reads a GAlignments or GRanges
upstream numeric or NULL, default NULL. if you want window of tx, instead of whole,
specify how much upstream from start of tx, 10 is include 10 bases before start
downstream numeric or NULL, default NULL. if you want window of tx, instead of whole,
specify how much downstream from start of tx, 10 is go 10 bases into tx from
start.
multiplier a character or numeric, default "0.99", either a quantile if input is string[0-1],
like "0.99", or numeric value if input is numeric. How much bigger than median
/ mean counts per gene, must a value be to be defined as extreme ?
min_cutoff a character or numeric, default "0.999", either a quantile if input is string[0-1],
like "0.999", or numeric value if input is numeric. Lowest allowed value
pre_filter_minimum
numeric, default 0. If value is x, will remove all positions in all genes with
coverage < x, before median filter is applied. Set to 1 to remove all 0 positions.
average character, default "median". Alternative: "mean". How to scale the multiplier
argument, from median or mean of gene coverage.
Value
GRangesList (filtered)
filterTranscripts Filter transcripts by lengths
Description
Filter transcripts to those who have leaders, CDS, trailers of some lengths, you can also pick the
longest per gene.
Usage
filterTranscripts(
txdb,
minFiveUTR = 30L,
minCDS = 150L,
minThreeUTR = 30L,
longestPerGene = TRUE,
stopOnEmpty = TRUE,
by = "tx",
create.fst.version = FALSE
)
filterTranscripts 141
Arguments
txdb a TxDb file or a path to one of: (.gtf ,.gff, .gff2, .gff2, .db or .sqlite), if it is a
GRangesList, it will return it self.
minFiveUTR (integer) minimum bp for 5’ UTR during filtering for the transcripts. Set to
NULL if no 5’ UTRs exists for annotation.
minCDS (integer) minimum bp for CDS during filtering for the transcripts
minThreeUTR (integer) minimum bp for 3’ UTR during filtering for the transcripts. Set to
NULL if no 3’ UTRs exists for annotation.
longestPerGene logical (TRUE), return only longest valid transcript per gene. NOTE: This is
by priority longest cds isoform, if equal then pick longest total transcript. So if
transcript is shorter but cds is longer, it will still be the one returned.
stopOnEmpty logical TRUE, stop if no valid transcripts are found ?
by a character, default "tx" Either "tx" or "gene". What names to output region by,
the transcript name "tx" or gene names "gene". NOTE: this is not the same as
cdsBy(txdb, by = "gene"), cdsBy would then only give 1 cds per Gene, loadRe-
gion gives all isoforms, but with gene names.
create.fst.version
logical, FALSE. If TRUE, creates a .fst version of the transcript length table (if
it not already exists), reducing load time from ~ 15 seconds to ~ 0.01 second
next time you run filterTranscripts with this txdb object. The file is stored in the
same folder as the genome this txdb is created from, with the name:
paste0(ORFik:::remove.file_ext(metadata(txdb)[3,2]), "_", gsub(" \(.*|
|:", "", metadata(txdb)[metadata(txdb)[,1] == "Creation time",2]), "_txLengths.fst")
Some error checks are done to see this is a valid location, if the txdb data source
is a repository like UCSC and not a local folder, it will not be made.
Details
If a transcript does not have a trailer, then the length is 0, so they will be filtered out if you set
minThreeUTR to 1. So only transcripts with leaders, cds and trailers will be returned. You can set
the integer to 0, that will return all within that group.
If your annotation does not have leaders or trailers, set them to NULL, since 0 means there must
exist a column called utr3_len etc. Genes with gene_id = NA will be be removed.
Value
a character vector of valid transcript names
Examples
gtf_file <- system.file("extdata/Danio_rerio_sample", "annotations.gtf", package = "ORFik")
txdb <- loadTxdb(gtf_file)
txNames <- filterTranscripts(txdb, minFiveUTR = 1, minCDS = 30,
minThreeUTR = 1)
loadRegion(txdb, "mrna")[txNames]
loadRegion(txdb, "5utr")[txNames]
142 fimport
filterUORFs Remove uORFs that are false CDS hits
Description
This is a strong filtering, so that even if the cds is on another transcript , the uORF is filtered out,
this is because there is no way of knowing by current ribo-seq, rna-seq experiments.
Usage
filterUORFs(uorfs, cds)
Arguments
uorfs (GRangesList), the uORFs to filter
cds (GRangesList), the coding sequences (main ORFs on transcripts), to filter against.
Value
(GRangesList) of filtered uORFs
See Also
Other uorfs: addCdsOnLeaderEnds(), removeORFsWithSameStartAsCDS(), removeORFsWithSameStopAsCDS(),
removeORFsWithStartInsideCDS(), removeORFsWithinCDS(), uORFSearchSpace()
fimport Load any type of sequencing reads
Description
Wraps around ORFik file format loaders and rtracklayer::import and tries to speed up loading with
the use of data.table. Supports gzip, gz, bgz compression formats. Also safer chromosome naming
with the argument chrStyle
Usage
fimport(path, chrStyle = NULL, param = NULL, strandMode = 0)
fimport 143
Arguments
path a character path to file (1 or 2 files), or data.table with 2 colums(forward&reverse)
or a GRanges/Galignment/GAlignmentPairs object etc. If it is ranged object it
will presume to be already loaded, so will return the object as it is, updating the
seqlevelsStyle if given.
chrStyle a GRanges object, TxDb, FaFile, , a seqlevelsStyle or Seqinfo. (Default:
NULL) to get seqlevelsStyle from. In addition if it is a Seqinfo object, seqinfo
will be updated. Example of seqlevelsStyle update: Is chromosome 1 called
chr1 or 1, is mitocondrial chromosome called MT or chrM etc. Will use 1st
seqlevel-style if more are present. Like: c("NCBI", "UCSC") -> pick "NCBI"
param NULL or a ScanBamParam object. Like for scanBam, this influences what fields
and which records are imported. However, note that the fields specified thru this
ScanBamParam object will be loaded in addition to any field required for gen-
erating the returned object (GAlignments, GAlignmentPairs, or GappedReads
object), but only the fields requested by the user will actually be kept as meta-
data columns of the object.
By default (i.e. param=NULL or param=ScanBamParam()), no additional field is
loaded. The flag used is scanBamFlag(isUnmappedQuery=FALSE) for readGAlignments,
readGAlignmentsList, and readGappedReads. (i.e. only records correspond-
ing to mapped reads are loaded), and scanBamFlag(isUnmappedQuery=FALSE,
isPaired=TRUE, hasUnmappedMate=FALSE) for readGAlignmentPairs (i.e. only
records corresponding to paired-end reads with both ends mapped are loaded).
strandMode numeric, default 0. Only used for paired end bam files. One of (0: strand
= *, 1: first read of pair is +, 2: first read of pair is -). See ?strandMode.
Note: Sets default to 0 instead of 1, as readGAlignmentPairs uses 1. This is
to guarantee hits, but will also make mismatches of overlapping transcripts in
opposite directions.
Details
NOTE: For wig/bigWig files you can send in 2 files, so that it automatically merges forward and
reverse stranded objects. You can also just send 1 wig/bigWig file, it will then have "*" as strand.
Value
a GAlignments/GRanges object, depending on input.
See Also
Other utils: bedToGR(), convertToOneBasedRanges(), export.bed12(), export.bigWig(), export.fstwig(),
export.wiggle(), findFa(), fread.bed(), optimizeReads(), readBam(), readBigWig(), readWig()
Examples
bam_file <- system.file("extdata/Danio_rerio_sample", "ribo-seq.bam", package = "ORFik")
fimport(bam_file)
# Certain chromosome naming
fimport(bam_file, "NCBI")
144 findFa
# Paired end bam strandMode 1:
fimport(bam_file, strandMode = 1)
# (will have no effect in this case, since it is not paired end)
findFa Convenience wrapper for Rsamtools FaFile
Description
Get fasta file object, to find sequences in file.
Will load and import file if necessarry.
Usage
findFa(faFile)
Arguments
faFile FaFile, BSgenome, fasta/index file path or an ORFik experiment. This file is
usually used to find the transcript sequences from some GRangesList.
Value
a FaFile or BSgenome
See Also
Other utils: bedToGR(), convertToOneBasedRanges(), export.bed12(), export.bigWig(), export.fstwig(),
export.wiggle(), fimport(), fread.bed(), optimizeReads(), readBam(), readBigWig(), readWig()
Examples
# Some fasta genome with existing fasta index in same folder
path <- system.file("extdata/Danio_rerio_sample", "genome_dummy.fasta", package = "ORFik")
findFa(path)
findFromPath 145
findFromPath Find all candidate library types filenames
Description
From the given experiment
Usage
findFromPath(filepaths, candidates, slot = "auto")
Arguments
filepaths path to all files
candidates a data.table with 2 columns, Possible names to search for, see experiment_naming
family for candidates.
slot character, default "auto". If auto, use auto guessing of slot, else must be a char-
acter vector of length 1 or equal length as filepaths.
Value
a candidate library types (character vector)
findLibrariesInFolder Get all library files in folder/folders of given types
Description
Will try to guess paired / unpaired wig, bed, bam files.
Usage
findLibrariesInFolder(dir, types, pairedEndBam = FALSE)
Arguments
dir Which directory / directories to create experiment from, must be a directory
with NGS data from your experiment. Will include all files of file type specified
by "types" argument. So do not mix files from other experiments in the same
folder!
types Default c("bam", "bed", "wig", "ofst"), which types of libraries to allow as
NGS data.
pairedEndBam logical FALSE, else TRUE, or a logical list of TRUE/FALSE per library you see
will be included (run first without and check what order the files will come in)
1 paired end file, then two single will be c(T, F, F). If you have a SRA metadata
csv file, you can set this argument to study$LibraryLayout == "PAIRED", where
study is the SRA metadata for all files that was aligned.
146 findMapORFs
Details
Set pairedEndBam if you have paired end reads as a single bam file.
Value
(data.table) All files found from types parameter. With 2 extra column (logical), is it wig pairs, and
paired bam files.
findMapORFs Find ORFs and immediately map them to their genomic positions.
Description
This function can map spliced ORFs. It finds ORFs on the sequences of interest, but returns relative
positions to the positions of ‘grl‘ argument. For example, ‘grl‘ can be exons of known transcripts
(with genomic coordinates), and ‘seq‘ sequences of those transcripts, in that case, this function will
return genomic coordinates of ORFs found on transcript sequences.
Usage
findMapORFs(
grl,
seqs,
startCodon = startDefinition(1),
stopCodon = stopDefinition(1),
longestORF = TRUE,
minimumLength = 0,
groupByTx = FALSE
)
Arguments
grl (GRangesList) of sequences to search for ORFs, probably in genomic coordi-
nates
seqs (DNAStringSet or character vector) - DNA/RNA sequences to search for Open
Reading Frames. Can be both uppercase or lowercase. Easiest call to get
seqs if you want only regions from a fasta/fasta index pair is: seqs = OR-
Fik:::txSeqsFromFa(grl, faFile), where grl is a GRanges/List of search regions
and faFile is a FaFile.
startCodon (character vector) Possible START codons to search for. Check startDefinition
for helper function. Note that it is case sensitive, so "atg" would give 0 hits for
a sequence with only capital "ATG" ORFs.
stopCodon (character vector) Possible STOP codons to search for. Check stopDefinition
for helper function. Note that it is case sensitive, so "tga" would give 0 hits for
a sequence with only capital "TGA" ORFs.
findMapORFs 147
longestORF (logical) Default TRUE. Keep only the longest ORF per unique stopcodon: (se-
qname, strand, stopcodon) combination, Note: Not longest per transcript! You
can also use function longestORFs after creation of ORFs for same result.
minimumLength (integer) Default is 0. Which is START + STOP = 6 bp. Minimum length
of ORF, without counting 3bps for START and STOP codons. For example
minimumLength = 8 will result in size of ORFs to be at least START + 8*3 (bp)
+ STOP = 30 bases. Use this param to restrict search.
groupByTx logical (default: FALSE), should output GRangesList be grouped by exons per
ORF (TRUE) or by orfs per transcript (FALSE)?
Details
This function assumes that ‘seq‘ is in widths relative to ‘grl‘, and that their orders match. 1st seq is
1st grl object, etc.
See vignette for real life example.
Value
A GRangesList of ORFs.
See Also
Other findORFs: findORFs(), findORFsFasta(), findUORFs(), startDefinition(), stopDefinition()
Examples
# First show simple example using findORFs
# This sequence has ORFs at 1-9 and 4-9
seqs <- DNAStringSet("ATGATGTAA") # the dna transcript sequence
findORFs(seqs)
# lets assume that this sequence comes from two exons as follows
# Then we need to use findMapORFs instead of findORFs,
# for splicing information
gr <- GRanges(seqnames = "1", # chromosome 1
ranges = IRanges(start = c(21, 10), end = c(23, 15)),
strand = "-", #
names = "tx1") #From transcript 1 on chr 1
grl <- GRangesList(tx1 = gr) # 1 transcript with 2 exons
findMapORFs(grl, seqs) # ORFs are properly mapped to its genomic coordinates
grl <- c(grl, grl)
names(grl) <- c("tx1", "tx2")
findMapORFs(grl, c(seqs, seqs))
# More advanced example and how to save sequences found in vignette
148 findNewTSS
findMaxPeaks Find max peak for each transcript, returns as data.table, without
names, but with index
Description
Find max peak for each transcript, returns as data.table, without names, but with index
Usage
findMaxPeaks(cageOverlaps, filteredCage)
Arguments
cageOverlaps The cageOverlaps between cage and extended 5’ leaders
filteredCage The filtered raw cage-data used to reassign 5’ leaders
Value
a data.table of max peaks
findNewTSS Finds max peaks per trancsript from reads in the cagefile
Description
Finds max peaks per trancsript from reads in the cagefile
Usage
findNewTSS(fiveUTRs, cageData, extension, restrictUpstreamToTx)
Arguments
fiveUTRs The 5’ leader sequences as GRangesList
cageData The CAGE as GRanges object
extension The number of basses to extends transcripts upstream.
restrictUpstreamToTx
a logical (FALSE), if you want to restrict leaders to not extend closer than 5
bases from closest upstream leader, set this to TRUE.
Value
a Hits object
findNGSPairs 149
findNGSPairs Find pair of forward and reverse strand wig / bed files and paired end
bam files split in two
Description
Find pair of forward and reverse strand wig / bed files and paired end bam files split in two
Usage
findNGSPairs(
paths,
f = c("forward", "fwd"),
r = c("reverse", "rev"),
format = "wig"
)
Arguments
paths a character path at least one .wig / .bed file
f Default (c("forward", "fwd") a character vector for forward direction regex.
r Default (c("reverse", "rev") a character vector for reverse direction regex.
format default "wig", for bed do "bed". Also searches compressions of these variants.
Value
if not all are paired, return original list, if they are all paired, return a data.table with matches as 2
columns
findORFs Find Open Reading Frames.
Description
Find all Open Reading Frames (ORFs) on the simple input sequences in ONLY 5’- 3’ direction (+),
but within all three possible reading frames. Do not use findORFs for mapping to full chromosomes,
then use findMapORFs! For each sequence of the input vector IRanges with START and STOP
positions (inclusive) will be returned as IRangesList. Returned coordinates are relative to the
input sequences.
150 findORFs
Usage
findORFs(
seqs,
startCodon = startDefinition(1),
stopCodon = stopDefinition(1),
longestORF = TRUE,
minimumLength = 0
)
Arguments
seqs (DNAStringSet or character vector) - DNA/RNA sequences to search for Open
Reading Frames. Can be both uppercase or lowercase. Easiest call to get
seqs if you want only regions from a fasta/fasta index pair is: seqs = OR-
Fik:::txSeqsFromFa(grl, faFile), where grl is a GRanges/List of search regions
and faFile is a FaFile.
startCodon (character vector) Possible START codons to search for. Check startDefinition
for helper function. Note that it is case sensitive, so "atg" would give 0 hits for
a sequence with only capital "ATG" ORFs.
stopCodon (character vector) Possible STOP codons to search for. Check stopDefinition
for helper function. Note that it is case sensitive, so "tga" would give 0 hits for
a sequence with only capital "TGA" ORFs.
longestORF (logical) Default TRUE. Keep only the longest ORF per unique stopcodon: (se-
qname, strand, stopcodon) combination, Note: Not longest per transcript! You
can also use function longestORFs after creation of ORFs for same result.
minimumLength (integer) Default is 0. Which is START + STOP = 6 bp. Minimum length
of ORF, without counting 3bps for START and STOP codons. For example
minimumLength = 8 will result in size of ORFs to be at least START + 8*3 (bp)
+ STOP = 30 bases. Use this param to restrict search.
Details
If you want antisence strand too, do: #positive strands pos <- findORFs(seqs) #negative
strands (DNAStringSet only if character) neg <- findORFs(reverseComplement(DNAStringSet(seqs)))
relist(c(GRanges(pos, strand = "+"), GRanges(neg, strand = "-")), skeleton = merge(pos,
neg))
Value
(IRangesList) of ORFs locations by START and STOP sites grouped by input sequences. In a list
of sequences, only the indices of the sequences that had ORFs will be returned, e.g. 3 sequences
where only 1 and 3 has ORFs, will return size 2 IRangesList with names c("1", "3"). If there are a
total of 0 ORFs, an empty IRangesList will be returned.
See Also
Other findORFs: findMapORFs(), findORFsFasta(), findUORFs(), startDefinition(), stopDefinition()
findORFsFasta 151
Examples
## Simple examples
findORFs("ATGTAA")
findORFs("ATGTTAA") # not in frame anymore
findORFs("ATGATGTAA") # only longest of two above
findORFs("ATGATGTAA", longestORF = FALSE) # two ORFs
findORFs(c("ATGTAA", "ATGATGTAA")) # 1 ORF per transcript
## Get DNA sequences from ORFs
seq <- DNAStringSet(c("ATGTAA", "AAA", "ATGATGTAA"))
names(seq) <- c("tx1", "tx2", "tx3")
orfs <- findORFs(seq, longestORF = FALSE)
# you can get sequences like this:
gr <- unlist(orfs, use.names = TRUE)
gr <- GRanges(seqnames = names(seq)[as.integer(names(gr))],
ranges(gr), strand = "+")
# Give them some proper names:
names(gr) <- paste0("ORF_", seq.int(length(gr)), "_", seqnames(gr))
orf_seqs <- getSeq(seq, gr)
orf_seqs
# Save as .fasta (orf_seqs must be of type DNAStringSet)
# writeXStringSet(orf_seqs, "orfs.fasta")
## Reading from file and find ORFs
#findORFs(readDNAStringSet("path/to/transcripts.fasta"))
findORFsFasta Finds Open Reading Frames in fasta files.
Description
Should be used for procaryote genomes or transcript sequences as fasta. Makes no sence for eukary-
ote whole genomes, since those contains splicing (use findMapORFs for spliced ranges). Searches
through each fasta header and reports all ORFs found for BOTH sense (+) and antisense strand (-)
in all frames. Name of the header will be used as seqnames of reported ORFs. Each fasta header is
treated separately, and name of the sequence will be used as seqname in returned GRanges object.
This supports circular genomes.
Usage
findORFsFasta(
filePath,
startCodon = startDefinition(1),
stopCodon = stopDefinition(1),
longestORF = TRUE,
minimumLength = 0,
is.circular = FALSE
)
152 findORFsFasta
Arguments
filePath (character) Path to the fasta file. Can be both uppercase or lowercase. Or a
already loaded R object of either types: "BSgenome" or "DNAStringSet" with
named sequences
startCodon (character vector) Possible START codons to search for. Check startDefinition
for helper function. Note that it is case sensitive, so "atg" would give 0 hits for
a sequence with only capital "ATG" ORFs.
stopCodon (character vector) Possible STOP codons to search for. Check stopDefinition
for helper function. Note that it is case sensitive, so "tga" would give 0 hits for
a sequence with only capital "TGA" ORFs.
longestORF (logical) Default TRUE. Keep only the longest ORF per unique stopcodon: (se-
qname, strand, stopcodon) combination, Note: Not longest per transcript! You
can also use function longestORFs after creation of ORFs for same result.
minimumLength (integer) Default is 0. Which is START + STOP = 6 bp. Minimum length
of ORF, without counting 3bps for START and STOP codons. For example
minimumLength = 8 will result in size of ORFs to be at least START + 8*3 (bp)
+ STOP = 30 bases. Use this param to restrict search.
is.circular (logical) Whether the genome in filePath is circular. Prokaryotic genomes are
usually circular. Be carefull if you want to extract sequences, remember that
seqlengths must be set, else it does not know what last base in sequence is before
loop ends!
Details
Remember if you have a fasta file of transcripts (transcript coordinates), delete all negative stranded
ORFs afterwards by: orfs <- orfs[strandBool(orfs)] # negative strand orfs make no sense then.
Seqnames are created from header by format: >name info, so name must be first after "biggern
than" and space between name and info. Also make sure your fasta file is valid (no hidden spaces
etc), as this might break the coordinate system!
Value
(GRanges) object of ORFs mapped from fasta file. Positions are relative to the fasta file.
See Also
Other findORFs: findMapORFs(), findORFs(), findUORFs(), startDefinition(), stopDefinition()
Examples
# location of the example fasta file
example_genome <- system.file("extdata/Danio_rerio_sample", "genome_dummy.fasta",
package = "ORFik")
orfs <- findORFsFasta(example_genome)
# To store ORF sequences (you need indexed genome .fai file):
fa <- FaFile(example_genome)
names(orfs) <- paste0("ORF_", seq.int(length(orfs)), "_", seqnames(orfs))
orf_seqs <- getSeq(fa, orfs)
findPeaksPerGene 153
# You sequences (fa), needs to have isCircular(fa) == TRUE for it to work
# on circular wrapping ranges!
# writeXStringSet(DNAStringSet(orf_seqs), "orfs.fasta")
findPeaksPerGene Find peaks per gene
Description
For finding the peaks (stall sites) per gene, with some default filters. A peak is basically a position
of very high coverage compared to its surrounding area, as measured using zscore.
Usage
findPeaksPerGene(
tx,
reads,
top_tx = 0.5,
min_reads_per_tx = 20,
min_reads_per_peak = 10,
type = "max"
)
Arguments
tx a GRangesList
reads a GAlignments or GRanges, must be 1 width reads like p-shifts, or other reads
that is single positioned. It will work with non 1 width bases, but you then get
larger areas for peaks.
top_tx numeric, default 0.50 (only use 50% top transcripts by read counts).
min_reads_per_tx
numeric, default 20. Gene must have at least 20 reads, applied before type filter.
min_reads_per_peak
numeric, default 10. Peak must have at least 10 reads.
type character, default "max". Get only max peak per gene. Alternatives: "all", all
peaks passing the input filter will be returned. "median", only peaks that is
higher than the median of all peaks. "maxmedian": get first "max", then median
of those.
Details
For more details see reference, which uses a slightly different method by zscore of a sliding window
instead of over the whole tx.
154 findUORFs
Value
a data.table of gene_id, position, counts of the peak, zscore and standard deviation of the peak
compared to rest of gene area.
References
doi: 10.1261/rna.065235.117
Examples
df <- ORFik.template.experiment()
cds <- loadRegion(df, "cds")
# Load ribo seq from ORFik
rfp <- fimport(df[3,]$filepath)
# All transcripts passing filter
findPeaksPerGene(cds, rfp, top_tx = 0)
# Top 50% of genes
findPeaksPerGene(cds, rfp)
findUORFs Find upstream ORFs from transcript annotation
Description
Procedure: 1. Create a new search space starting with the 5’ UTRs. 2. Redefine TSS with CAGE
if wanted. 3. Add the whole of CDS to search space to allow uORFs going into cds. 4. find ORFs
on that search space. 5. Filter out wrongly found uORFs, if CDS is included. The CDS, alternative
CDS, uORFs starting within the CDS etc.
Usage
findUORFs(
fiveUTRs,
fa,
startCodon = startDefinition(1),
stopCodon = stopDefinition(1),
longestORF = TRUE,
minimumLength = 0,
cds = NULL,
cage = NULL,
extension = 1000,
filterValue = 1,
restrictUpstreamToTx = FALSE,
removeUnused = FALSE
)
findUORFs 155
Arguments
fiveUTRs (GRangesList) The 5’ leaders or full transcript sequences
fa a FaFile. With fasta sequences corresponding to fiveUTR annotation. Usually
loaded from the genome of an organism with fa = ORFik:::findFa("path/to/fasta/genome")
startCodon (character vector) Possible START codons to search for. Check startDefinition
for helper function. Note that it is case sensitive, so "atg" would give 0 hits for
a sequence with only capital "ATG" ORFs.
stopCodon (character vector) Possible STOP codons to search for. Check stopDefinition
for helper function. Note that it is case sensitive, so "tga" would give 0 hits for
a sequence with only capital "TGA" ORFs.
longestORF (logical) Default TRUE. Keep only the longest ORF per unique stopcodon: (se-
qname, strand, stopcodon) combination, Note: Not longest per transcript! You
can also use function longestORFs after creation of ORFs for same result.
minimumLength (integer) Default is 0. Which is START + STOP = 6 bp. Minimum length
of ORF, without counting 3bps for START and STOP codons. For example
minimumLength = 8 will result in size of ORFs to be at least START + 8*3 (bp)
+ STOP = 30 bases. Use this param to restrict search.
cds (GRangesList) CDS of relative fiveUTRs, applicable only if you want to extend
5’ leaders downstream of CDS’s, to allow upstream ORFs that can overlap into
CDS’s.
cage Either a filePath for the CageSeq file as .bed .bam or .wig, with possible com-
pressions (".gzip", ".gz", ".bgz"), or already loaded CageSeq peak data as GRanges
or GAlignment. NOTE: If it is a .bam file, it will add a score column by run-
ning: convertToOneBasedRanges(cage, method = "5prime", addScoreColumn =
TRUE) The score column is then number of replicates of read, if score column
is something else, like read length, set the score column to NULL first.
extension The maximum number of basses upstream of the TSS to search for CageSeq
peak.
filterValue The minimum number of reads on cage position, for it to be counted as possible
new tss. (represented in score column in CageSeq data) If you already filtered,
set it to 0.
restrictUpstreamToTx
a logical (FALSE). If TRUE: restrict leaders to not extend closer than 5 bases
from closest upstream leader, set this to TRUE.
removeUnused logical (FALSE), if False: (standard is to set them to original annotation), If
TRUE: remove leaders that did not have any cage support.
Details
From default a filtering process is done to remove "fake" uORFs, but only if cds is included, since
uORFs that stop on the stop codon on the CDS is not a uORF, but an alternative cds by definition,
etc.
Value
A GRangesList of uORFs, 1 granges list element per uORF.
156 findUORFs_exp
See Also
Other findORFs: findMapORFs(), findORFs(), findORFsFasta(), startDefinition(), stopDefinition()
Examples
# Load annotation
txdbFile <- system.file("extdata", "hg19_knownGene_sample.sqlite",
package = "GenomicFeatures")
## Not run:
txdb <- loadTxdb(txdbFile)
fiveUTRs <- loadRegion(txdb, "leaders")
cds <- loadRegion(txdb, "cds")
if (requireNamespace("BSgenome.Hsapiens.UCSC.hg19")) {
# Normally you would not use a BSgenome, but some custom fasta-
# annotation you have for your species
findUORFs(fiveUTRs, BSgenome.Hsapiens.UCSC.hg19::Hsapiens, "ATG",
cds = cds)
}
## End(Not run)
findUORFs_exp Find upstream ORFs from transcript annotation
Description
Procedure: 1. Create a new search space starting with the 5’ UTRs. 2. Redefine TSS with CAGE
if wanted. 3. Add the whole of CDS to search space to allow uORFs going into cds. 4. find ORFs
on that search space. 5. Filter out wrongly found uORFs, if CDS is included. The CDS, alternative
CDS, uORFs starting within the CDS etc.
Usage
findUORFs_exp(
df,
faFile = findFa(df),
leaders = loadRegion(txdb, "leaders"),
startCodon = startDefinition(1),
stopCodon = stopDefinition(1),
longestORF = TRUE,
minimumLength = 0,
overlappingCDS = FALSE,
cage = NULL,
extension = 1000,
filterValue = 1,
restrictUpstreamToTx = FALSE,
removeUnused = FALSE,
save_optimized = FALSE
)
findUORFs_exp 157
Arguments
df a txdb or experiment
faFile FaFile of genome, default findFa(df). Default only works for ORFik experi-
ments, if TxDb, input manually like: findFa(genome_path)
leaders GRangesList, default: loadRegion(txdb, "leaders"). If you do not have any good
leader annotation, a hack is to use ORFik:::groupGRangesBy(startSites(loadRegion(txdb,
"cds"), asGR = TRUE, keep.names = TRUE, is.sorted = TRUE))
startCodon (character vector) Possible START codons to search for. Check startDefinition
for helper function. Note that it is case sensitive, so "atg" would give 0 hits for
a sequence with only capital "ATG" ORFs.
stopCodon (character vector) Possible STOP codons to search for. Check stopDefinition
for helper function. Note that it is case sensitive, so "tga" would give 0 hits for
a sequence with only capital "TGA" ORFs.
longestORF (logical) Default TRUE. Keep only the longest ORF per unique stopcodon: (se-
qname, strand, stopcodon) combination, Note: Not longest per transcript! You
can also use function longestORFs after creation of ORFs for same result.
minimumLength (integer) Default is 0. Which is START + STOP = 6 bp. Minimum length
of ORF, without counting 3bps for START and STOP codons. For example
minimumLength = 8 will result in size of ORFs to be at least START + 8*3 (bp)
+ STOP = 30 bases. Use this param to restrict search.
overlappingCDS logical, default FALSE. Include uORFs that overlap CDS.
cage Either a filePath for the CageSeq file as .bed .bam or .wig, with possible com-
pressions (".gzip", ".gz", ".bgz"), or already loaded CageSeq peak data as GRanges
or GAlignment. NOTE: If it is a .bam file, it will add a score column by run-
ning: convertToOneBasedRanges(cage, method = "5prime", addScoreColumn =
TRUE) The score column is then number of replicates of read, if score column
is something else, like read length, set the score column to NULL first.
extension The maximum number of basses upstream of the TSS to search for CageSeq
peak.
filterValue The minimum number of reads on cage position, for it to be counted as possible
new tss. (represented in score column in CageSeq data) If you already filtered,
set it to 0.
restrictUpstreamToTx
a logical (FALSE). If TRUE: restrict leaders to not extend closer than 5 bases
from closest upstream leader, set this to TRUE.
removeUnused logical (FALSE), if False: (standard is to set them to original annotation), If
TRUE: remove leaders that did not have any cage support.
save_optimized logical, default FALSE. If TRUE, save in the optimized folder for the exper-
iment. You must have made this directory before running this function (call
makeTxdbFromGenome first if not).
Details
From default a filtering process is done to remove "fake" uORFs, but only if cds is included, since
uORFs that stop on the stop codon on the CDS is not a uORF, but an alternative cds by definition,
etc.
158 find_url_ebi
Value
A GRangesList of uORFs, 1 granges list element per uORF.
See Also
Other findORFs: findMapORFs(), findORFs(), findORFsFasta(), startDefinition(), stopDefinition()
Examples
df <- ORFik.template.experiment()
# Without cds overlapping, no 5' leader extension
findUORFs_exp(df, extension = 0)
# Without cds overlapping, extends 5' leaders by 1000 (good for yeast etc)
findUORFs_exp(df)
# Include cds overlapping uorfs
findUORFs_exp(df, overlappingCDS = TRUE)
find_url_ebi Locates and check if fastq files exists in ebi
Description
Look for files in ebi following url: ftp://ftp.sra.ebi.ac.uk/vol1/fastq Paired end and single end fastq
files.
EBI uses 3 ways to organize data inside vol1/fastq:
- 1: Most common: SRR(3 first)/0(2 last)/whole
- 2: less common: SRR(3 first)/00(1 last)/whole
- 3: least common SRR(3 first)/whole
Usage
find_url_ebi(SRR, stop.on.error = FALSE, study = NULL)
Arguments
SRR character, SRR, ERR or DRR numbers.
stop.on.error logical FALSE, if TRUE will stop if all files are not found. If FALSE returns
empty character vector if error is catched.
study default NULL, optional PRJ (study id) to speed up search for URLs.
Value
full url to fastq files, same length as input (2 urls for paired end data). Returns empty character() if
all files not found.
find_url_ebi_safe 159
Examples
# Test the 3 ways to get fastq files from EBI
# Both single end and paired end data
# Most common: SRR(3 first)/0(2 last)/whole
# Single
ORFik:::find_url_ebi("SRR10503056")
# Paired
ORFik:::find_url_ebi("SRR10500056")
# less common: SRR(3 first)/00(1 last)/whole
# Single
#ORFik:::find_url_ebi("SRR1562873")
# Paired
#ORFik:::find_url_ebi("SRR1560083")
# least common SRR(3 first)/whole
# Single
#ORFik:::find_url_ebi("SRR105687")
# Paired
#ORFik:::find_url_ebi("SRR105788")
find_url_ebi_safe Find URL for EBI fastq files
Description
Safer version
Usage
find_url_ebi_safe(accession, SRR = NULL, stop.on.error = FALSE)
Arguments
accession character: (PRJ, SRP, ERP, DRP, SRX, SRR, ERR,..). For studies or samples, it
returns all runs per study or sample.
SRR character, which SRR numbers to subset by (can also be ERR or DRR numbers)
stop.on.error logical FALSE, if TRUE will stop if all files are not found. If FALSE returns
empty character vector if error is catched.
Value
character (1 element per SRR number)
160 firstExonPerGroup
firstEndPerGroup Get first end per granges group
Description
grl must be sorted, call ORFik:::sortPerGroup if needed
Usage
firstEndPerGroup(grl, keep.names = TRUE)
Arguments
grl a GRangesList
keep.names a boolean, keep names or not, default: (TRUE)
Value
a Rle(keep.names = T), or integer vector(F)
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
firstEndPerGroup(grl)
firstExonPerGroup Get first exon per GRangesList group
Description
grl must be sorted, call ORFik:::sortPerGroup if needed
Usage
firstExonPerGroup(grl)
Arguments
grl a GRangesList
firstStartPerGroup 161
Value
a GRangesList of the first exon per group
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
firstExonPerGroup(grl)
firstStartPerGroup Get first start per granges group
Description
grl must be sorted, call ORFik:::sortPerGroup if needed
Usage
firstStartPerGroup(grl, keep.names = TRUE)
Arguments
grl a GRangesList
keep.names a boolean, keep names or not, default: (TRUE)
Value
a Rle(keep.names = TRUE), or integer vector(FALSE)
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
firstStartPerGroup(grl)
162 flankPerGroup
fix_malformed_gff Fix a malformed gff file
Description
Basically removes all info lines with character length > 32768 and save that new file.
Usage
fix_malformed_gff(gff)
Arguments
gff character, path to gtf, can not be gzipped!
Value
path of fixed gtf
Examples
# fix_malformed_gff("my_bad_gff.gff")
flankPerGroup Get flanks per group
Description
For a GRangesList, get start and end site, return back as GRL.
Usage
flankPerGroup(grl)
Arguments
grl a GRangesList
Value
a GRangesList, 1 GRanges per group with: start as minimum start of group and end as maximum
per group.
Examples
grl <- GRangesList(tx1 = GRanges("1", IRanges(c(1,5), width = 2), "+"),
tx2 = GRanges("2", IRanges(c(10,15), width = 2), "+"))
flankPerGroup(grl)
floss 163
floss Fragment Length Organization Similarity Score
Description
This feature is usually calcualted only for RiboSeq reads. For reads of width between ‘start‘ and
‘end‘, sum the fraction of RiboSeq reads (per read widths) that overlap ORFs and normalize by
CDS read width fractions. So if all read length are width 34 in ORFs and CDS, value is 1. If width
is 33 in ORFs and 34 in CDS, value is 0. If width is 33 in ORFs and 50/50 (33 and 34) in CDS,
values will be 0.5 (for 33).
Usage
floss(grl, RFP, cds, start = 26, end = 34, weight = 1L)
Arguments
grl a GRangesList object can be either transcripts, 5’ utrs, cds’, 3’ utrs or ORFs as
a special case (uORFs, potential new cds’ etc). If regions are not spliced you
can send a GRanges object.
RFP ribosomal footprints, given as GAlignments or GRanges object, must be already
shifted and resized to the p-site. Requires a $size column with original read
lengths.
cds a GRangesList of coding sequences, cds has to have names as grl so that they
can be matched
start usually 26, the start of the floss interval (inclusive)
end usually 34, the end of the floss interval (inclusive)
weight a vector (default: 1L, if 1L it is identical to countOverlaps()), if single number
(!= 1), it applies for all, if more than one must be equal size of ’reads’. else it
must be the string name of a defined meta column in subject "reads", that gives
number of times a read was found. GRanges("chr1", 1, "+", score = 5), would
mean "score" column tells that this alignment region was found 5 times.
Details
Pseudo explanation of the function:
SUM[start to stop]((grl[start:end][name]/grl) / (cds[start:end][name]/cds))
Where ’name’ is transcript names.
Please read more in the article.
Value
a vector of FLOSS of length same as grl, 0 means no RFP reads in range, 1 is perfect match.
164 footprints.analysis
References
doi: 10.1016/j.celrep.2014.07.045
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), fpkm(), fpkm_calc(), fractionLength(), initiationScore(),
insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(), orfScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
ORF1 <- GRanges(seqnames = "1",
ranges = IRanges(start = c(1, 12, 22),
end = c(10, 20, 32)),
strand = "+")
grl <- GRangesList(tx1_1 = ORF1)
# RFP is 1 width position based GRanges
RFP <- GRanges("1", IRanges(c(1, 25, 35, 38), width = 1), "+")
RFP$size <- c(28, 28, 28, 29) # original width in size col
cds <- GRangesList(tx1 = GRanges("1", IRanges(35, 44), "+"))
# grl must have same names as cds + _1 etc, so that they can be matched.
floss(grl, RFP, cds)
# or change ribosome start/stop, more strict
floss(grl, RFP, cds, 28, 28)
# With repeated alignments in score column
ORF2 <- GRanges(seqnames = "1",
ranges = IRanges(start = c(12, 22, 36),
end = c(20, 32, 38)),
strand = "+")
grl <- GRangesList(tx1_1 = ORF1, tx1_2 = ORF2)
score(RFP) <- c(5, 10, 5, 10)
floss(grl, RFP, cds, weight = "score")
footprints.analysis Pre shifting plot analysis
Description
For internal use only!
Usage
footprints.analysis(rw, heatmap, region = "start of CDS")
fpkm 165
Arguments
rw a data.table of position, score and fraction (read length) of either TIS or TES
(translation end site, around 3’ UTR)
heatmap a logical or character string, default FALSE. If TRUE, will plot heatmap of raw
reads before p-shifting to console, to see if shifts given make sense. You can
also set a filepath to save the file there.
region a character string, default "start of CDS"
Value
invisible(NULL)
fpkm Create normalizations of overlapping read counts.
Description
FPKM is short for "Fragments Per Kilobase of transcript per Million fragments in library". When
calculating RiboSeq data FPKM over ORFs, use ORFs as ‘grl‘. When calculating RNASeq data
FPKM, use full transcripts as ‘grl‘. It is equal to RPKM given that you do not have paired end reads.
Usage
fpkm(grl, reads, pseudoCount = 0, librarySize = "full", weight = 1L)
Arguments
grl a GRangesList object can be either transcripts, 5’ utrs, cds’, 3’ utrs or ORFs as
a special case (uORFs, potential new cds’ etc). If regions are not spliced you
can send a GRanges object.
reads a GAlignments, GRanges or GRangesList object, usually of RiboSeq, RnaSeq,
CageSeq, etc.
pseudoCount an integer, by default is 0, set it to 1 if you want to avoid NA and inf values.
librarySize either numeric value or character vector. Default ("full"), number of alignments
in library (reads). If you just have a subset, you can give the value by library-
Size = length(wholeLib), if you want lib size to be only number of reads over-
lapping grl, do: librarySize = "overlapping" sum(countOverlaps(reads, grl) >
0), if reads[1] has 3 hits in grl, and reads[2] has 2 hits, librarySize will be 2,
not 5. You can also get the inverse overlap, if you want lib size to be total
number of overlaps, do: librarySize = "DESeq" This is standard fpkm way of
DESeq2::fpkm(robust = FALSE) sum(countOverlaps(grl, reads)) if grl[1] has 3
reads and grl[2] has 2 reads, librarySize is 5, not 2.
weight a vector (default: 1L, if 1L it is identical to countOverlaps()), if single number
(!= 1), it applies for all, if more than one must be equal size of ’reads’. else it
must be the string name of a defined meta column in subject "reads", that gives
number of times a read was found. GRanges("chr1", 1, "+", score = 5), would
mean "score" column tells that this alignment region was found 5 times.
166 fpkm_calc
Details
Note also that you must consider if you will use the whole read library or just the reads overlapping
‘grl‘ for library size. A normal question here is, does it make sense to include rRNA in library size
? If you only want overlapping grl, do: librarySize = "overlapping"
Value
a numeric vector with the fpkm values
References
doi: 10.1038/nbt.1621
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss(), fpkm_calc(), fractionLength(), initiationScore(),
insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(), orfScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
ORF <- GRanges(seqnames = "1",
ranges = IRanges(start = c(1, 10, 20),
end = c(5, 15, 25)),
strand = "+")
grl <- GRangesList(tx1_1 = ORF)
RFP <- GRanges("1", IRanges(25, 25),"+")
fpkm(grl, RFP)
# With weights (10 reads at position 25)
RFP <- GRanges("1", IRanges(25, 25),"+", score = 10)
fpkm(grl, RFP, weight = "score")
fpkm_calc Create normalizations of read counts
Description
A helper for [fpkm()] Normally use function [fpkm()], if you want unusual normalization , you can
use this. Short for: Fragments per kilobase of transcript per million fragments Normally used in
Translations efficiency calculations
Usage
fpkm_calc(counts, lengthSize, librarySize)
fractionLength 167
Arguments
counts a list, # of read hits per group
lengthSize a list of lengths per group
librarySize a numeric of size 1, the # of reads in library
Value
a numeric vector
References
doi: 10.1038/nbt.1621
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss(), fpkm(), fractionLength(), initiationScore(),
insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(), orfScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
fractionLength Fraction Length
Description
Fraction Length is defined as
(widths of grl)/tx_len
so that each group in the grl is divided by the corresponding transcript.
Usage
fractionLength(grl, tx_len = widthPerGroup(tx, TRUE), tx = NULL)
Arguments
grl a GRangesList object with usually either leaders, cds’, 3’ utrs or ORFs. ORFs
are a special case, see argument tx_len
tx_len the transcript lengths of the transcripts, a named (tx names) vector of integers.
If you have the transcripts as GRangesList, call ‘ORFik:::widthPerGroup(tx,
TRUE)‘.
If you used CageSeq to reannotate leaders, then the tss for the the leaders have
changed, therefore the tx lengths have changed. To account for that call: ‘tx_len
<- widthPerGroup(extendLeaders(tx, cageFiveUTRs))‘ and calculate fraction
length using ‘fractionLength(grl, tx_len)‘.
tx default NULL, a GRangesList object of transcript to get lengths from. Pass in
for wrapping to widths inside the function.
168 fractionNames
Value
a numeric vector of ratios
References
doi: 10.1242/dev.098343
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss(), fpkm(), fpkm_calc(), initiationScore(),
insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(), orfScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
ORF <- GRanges(seqnames = "1",
ranges = IRanges(start = c(1, 10, 20), end = c(5, 15, 25)),
strand = "+")
grl <- GRangesList(tx1_1 = ORF)
# grl must have same names as cds + _1 etc, so that they can be matched.
tx <- GRangesList(tx1 = GRanges("1", IRanges(1, 50), "+"))
fractionLength(grl, tx = tx)
fractionNames Get cell fraction name variants
Description
Used to standardize nomeclature for experiments.
Example: cytosolic, mitochondrial, specific gene knock down
Usage
fractionNames()
Value
a data.table with 2 columns, the main name, and all name variants of the main name in second
column as a list.
See Also
Other experiment_naming: batchNames(), cellLineNames(), cellTypeNames(), conditionNames(),
inhibitorNames(), libNames(), mainNames(), repNames(), stageNames(), tissueNames()
fread.bed 169
fread.bed Load bed file as GRanges
Description
Wraps around import.bed and tries to speed up loading with the use of data.table. Supports gzip,
gz, bgz and bed formats. Also safer chromosome naming with the argument chrStyle
Usage
fread.bed(filePath, chrStyle = NULL)
Arguments
filePath The location of the bed file
chrStyle a GRanges object, TxDb, FaFile, , a seqlevelsStyle or Seqinfo. (Default:
NULL) to get seqlevelsStyle from. In addition if it is a Seqinfo object, seqinfo
will be updated. Example of seqlevelsStyle update: Is chromosome 1 called
chr1 or 1, is mitocondrial chromosome called MT or chrM etc. Will use 1st
seqlevel-style if more are present. Like: c("NCBI", "UCSC") -> pick "NCBI"
Value
a GRanges object
See Also
Other utils: bedToGR(), convertToOneBasedRanges(), export.bed12(), export.bigWig(), export.fstwig(),
export.wiggle(), fimport(), findFa(), optimizeReads(), readBam(), readBigWig(), readWig()
Examples
# path to example CageSeq data from hg19 heart sample
cageData <- system.file("extdata", "cage-seq-heart.bed.bgz",
package = "ORFik")
fread.bed(cageData)
170 geneToSymbol
gcContent Get GC content
Description
0.5 means 50
Usage
gcContent(seqs, fa = NULL)
Arguments
seqs a character vector of sequences, or ranges as GRangesList
fa fasta index file .fai file, either path to it, or the loaded FaFile, default (NULL),
only set if you give ranges as GRangesList
Value
a numeric vector of gc content scores
Examples
# Here we make an example from scratch
seqName <- "Chromosome"
ORF1 <- GRanges(seqnames = seqName,
ranges = IRanges(c(1007, 1096), width = 60),
strand = c("+", "+"))
ORF2 <- GRanges(seqnames = seqName,
ranges = IRanges(c(400, 100), width = 30),
strand = c("-", "-"))
ORFs <- GRangesList(tx1 = ORF1, tx2 = ORF2)
# get path to FaFile for sequences
faFile <- system.file("extdata/Danio_rerio_sample", "genome_dummy.fasta", package = "ORFik")
gcContent(ORFs, faFile)
geneToSymbol Get gene symbols from Ensembl gene ids
geneToSymbol 171
Description
If your organism is not in this list of supported organisms, manually assign the input arguments.
There are 2 main fetch modes:
By gene ids (Single accession per gene)
By tx ids (Multiple accessions per gene)
Run the mode you need depending on your required attributes.
Will check for already existing table of all genes, and use that instead of re-downloading every
time (If you input valid experiment or txdb and have run makeTxdbFromGenome with symbols =
TRUE, you have a file called gene_symbol_tx_table.fst) will load instantly. If df = NULL, it can
still search cache to load a bit slower.
Usage
geneToSymbol(
df,
organism_name = organism(df),
gene_ids = filterTranscripts(df, by = "gene", 0, 0, 0),
org.dataset = paste0(tolower(substr(organism_name, 1, 1)), gsub(".* ", replacement =
"", organism_name), "_gene_ensembl"),
ensembl = biomaRt::useEnsembl("ensembl", dataset = org.dataset),
attribute = "external_gene_name",
include_tx_ids = FALSE,
uniprot_id = FALSE,
force = FALSE,
verbose = TRUE
)
Arguments
df an ORFik experiment or TxDb object with defined organism slot. If set will
look for file at path of txdb / experiment reference path named: ’gene_symbol_tx_table.fst’
relative to the txdb/genome directory. Can be set to NULL if gene_ids and or-
ganism is defined manually.
organism_name default, organism(df). Scientific name of organism, like ("Homo sapiens"),
remember capital letter for first name only!
gene_ids default, filterTranscripts(df, by = "gene", 0, 0, 0). Ensembl gene IDs
to search for (default all transcripts coding and noncoding) To only get coding
do: filterTranscripts(df, by = "gene", 0, 1, 0)
org.dataset default, paste0(tolower(substr(organism_name, 1, 1)), gsub(".* ", replacement
= "", organism_name), "_gene_ensembl") the ensembl dataset to use. For
Homo sapiens, this converts to default as: hsapiens_gene_ensembl
ensembl default, useEnsembl("ensembl",dataset=org.dataset) .The mart connec-
tion.
attribute default, "external_gene_name", the biomaRt column / columns default(primary
gene symbol names). These are always from specific database, like hgnc symbol
for human, and mgi symbol for mouse and rat, sgd for yeast etc.
172 getGAlignments
include_tx_ids logical, default FALSE, also match tx ids, which then returns as the 3rd column.
Only allowed when ’df’ is defined. If
uniprot_id logical, default FALSE. Include uniprotsptrembl and/or uniprotswissprot. If in-
clude_tx_ids you will get per isoform if available, else you get canonical uniprot
id per gene. If both uniprotsptrembl and uniprotswissprot exists, it will make a
merged uniprot id column with rule: if id exists in uniprotswissprot, keep. If
not, use uniprotsptrembl column id.
force logical FALSE, if TRUE will not look for existing file made through makeTxdbFromGenome
corresponding to this txdb / ORFik experiment stored with name "gene_symbol_tx_table.fst".
verbose logical TRUE, if FALSE, do not output messages.
Value
data.table with 2, 3 or 4 columns: gene_id, gene_symbol, tx_id and uniprot_id named after attribute,
sorted in order of gene_ids input. (example: returns 3 columns if include_tx_ids is TRUE), and
more if additional columns are specified in ’attribute’ argument.
Examples
## Without ORFik experiment input
gene_id_ATF4 <- "ENSG00000128272"
#geneToSymbol(NULL, organism_name = "Homo sapiens", gene_ids = gene_id_ATF4)
# With uniprot canonical isoform id:
#geneToSymbol(NULL, organism_name = "Homo sapiens", gene_ids = gene_id_ATF4, uniprot_id = TRUE)
## All genes from Organism using ORFik experiment
# df <- read.experiment("some_experiment)
# geneToSymbol(df)
## Non vertebrate species (the ones not in ensembl, but in ensemblGenomes mart)
#txdb_ylipolytica <- loadTxdb("txdb_path")
#dt2 <- geneToSymbol(txdb_ylipolytica, include_tx_ids = TRUE,
# ensembl = useEnsemblGenomes(biomart = "fungi_mart", dataset = "ylipolytica_eg_gene"))
getGAlignments Internal GAlignments loader from fst data.frame
Description
Internal GAlignments loader from fst data.frame
Usage
getGAlignments(df, seqinfo = NULL)
getGAlignmentsPairs 173
Arguments
df a data.frame with columns minimum 4 columns: seqnames, start ("pos" in final
GA object), cigar and strand.
Additional columns will be assigned as meta columns
seqinfo Seqinfo object, defaul NULL (created from ranges). Add to avoid warnings later
on differences in seqinfo.
Value
GAlignments object
getGAlignmentsPairs Internal GAlignmentPairs loader from fst data.frame
Description
Internal GAlignmentPairs loader from fst data.frame
Usage
getGAlignmentsPairs(df, strandMode = 0, seqinfo = NULL)
Arguments
df a data.frame with columns minimum 6 columns: seqnames, start1/start2 (inte-
gers), cigar1/cigar2 and strand
Additional columns will be assigned as meta columns
strandMode numeric, default 0. Only used for paired end bam files. One of (0: strand
= *, 1: first read of pair is +, 2: first read of pair is -). See ?strandMode.
Note: Sets default to 0 instead of 1, as readGAlignmentPairs uses 1. This is
to guarantee hits, but will also make mismatches of overlapping transcripts in
opposite directions.
seqinfo Seqinfo object, defaul NULL (created from ranges). Add to avoid warnings later
on differences in seqinfo.
Value
GAlignmentPairs object
174 getGenomeAndAnnotation
getGenomeAndAnnotation
Download genome (fasta), annotation (GTF) and contaminants
Description
This function automatically downloads (if files not already exists) genomes and contaminants speci-
fied for genome alignment. By default, it will use ensembl reference, upon completion, the function
will store a file called file.path(output.dir, "outputs.rds") with the output paths of your
completed genome/annotation downloads. For most non-model nonvertebrate organisms, you need
my fork of biomartr for it to work: devtools::install_github("Roleren/biomartr) If you misspelled
something or crashed, delete wrong files and run again.
Do remake = TRUE, to do it all over again.
Usage
getGenomeAndAnnotation(
organism,
output.dir,
db = "ensembl",
GTF = TRUE,
genome = TRUE,
merge_contaminants = TRUE,
phix = FALSE,
ncRNA = FALSE,
tRNA = FALSE,
rRNA = FALSE,
gunzip = TRUE,
remake = FALSE,
assembly_type = c("primary_assembly", "toplevel"),
optimize = FALSE,
gene_symbols = FALSE,
uniprot_id = FALSE,
pseudo_5UTRS_if_needed = NULL,
remove_annotation_outliers = TRUE,
notify_load_existing = TRUE,
assembly = organism
)
Arguments
organism scientific name of organism, Homo sapiens, Danio rerio, Mus musculus, etc.
See biomartr:::get.ensembl.info() for full list of supported organisms.
output.dir directory to save downloaded data
getGenomeAndAnnotation 175
db database to use for genome and GTF, default adviced: "ensembl" (remember to
set assembly_type to "primary_assembly", else it will contain haplotypes, very
large file!). Alternatives: "refseq" (reference assemblies) and "genbank" (all
assemblies)
GTF logical, default: TRUE, download gtf of organism specified in "organism" argu-
ment. If FALSE, check if the downloaded file already exist. If you want to use
a custom gtf from you hard drive, set GTF = FALSE, and assign:
annotation <- getGenomeAndAnnotation(gtf = FALSE)
annotation["gtf"] = "path/to/gtf.gtf".
If db is not "ensembl", you will instead get a gff file.
genome logical, default: TRUE, download genome of organism specified in "organism"
argument. If FALSE, check if the downloaded file already exist. If you want to
use a custom gtf from you hard drive, set GTF = FALSE, and assign:
annotation <- getGenomeAndAnnotation(genome = FALSE)
annotation["genome"] = "path/to/genome.fasta".
Will download the primary assembly from Ensembl.
merge_contaminants
logical, default TRUE. Will merge the contaminants specified into one fasta file,
this considerably saves space and is much quicker to align with STAR than each
contaminant on it’s own. If no contaminants are specified, this is ignored.
phix logical, default FALSE, download phiX sequence to filter out Illumina control
reads. ORFik defines Phix as a contaminant genome. Phix is used in Illu-
mina sequencers for sequencing quality control. Genome is: refseq, Escherichia
phage phiX174. If sequencing facility created fastq files with the command
bcl2fastq, then there should be very few phix reads left in the fastq files re-
cieved.
ncRNA logical or character, default FALSE (not used, no download), if TRUE or defned
path, ncRNA is used as a contaminant reference. If TRUE, will try to find
ncRNA sequences from the gtf file, usually represented as lncRNA (long non-
coding RNA’s). Will let you know if no ncRNA sequences were found in gtf.
If not found try character input:
Alternatives; "auto": Will try to find ncRNA file on NONCODE from organ-
ism, Homo sapiens -> human etc. "auto" will not work for all, then you must
specify the name used by NONCODE, go to the link below and find it. If not
"auto" / "" it must be a character vector of species common name (not scientific
name) Homo sapiens is human, Rattus norwegicus is rat etc, download ncRNA
sequence to filter out with. From NONCODE online server, if you cant find
common name see: http://www.noncode.org/download.php/
tRNA logical or character, default FALSE (not used, no download), tRNA is used as
a contaminant genome. If TRUE, will try to find tRNA sequences from the gtf
file, usually represented as Mt_tRNA (mature tRNA’s). Will let you know if no
tRNA sequences were found in gtf. If not found try character input:
if not "" it must be a character vector to valid path of mature tRNAs fasta file to
remove as contaminants on your disc. Find and download your wanted mtRNA
at: http://gtrnadb.ucsc.edu/, or run trna-scan on you genome.
rRNA logical or character, default FALSE (not used, no download), rRNA is used as
a contaminant reference If TRUE, will try to find rRNA sequences from the gtf
176 getGenomeAndAnnotation
file, usually represented as rRNA (ribosomal RNA’s). Will let you know if no
rRNA sequences were found in gtf. If not found you can try character input:
If "silva" will download silva SSU & LSU sequences for all species (250MB
file) and use that. If you want a smaller file go to https://www.arb-silva.de/
If not "" or "silva" it must be a character vector to valid path of mature rRNA
fasta file to remove as contaminants on your disc.
gunzip logical, default TRUE, uncompress downloaded files that are zipped when down-
loaded, should be TRUE!
remake logical, default: FALSE, if TRUE remake everything specified
assembly_type character, default c("primary_assembly", "toplevel"). Used for ensembl only,
specifies the genome assembly type. Searches for both primary and toplevel,
and if both are found, uses the first by order (so primary is prioritized by de-
fault). The Primary assembly should usually be used if it exists. The "primary
assembly" contains all the top-level sequence regions, excluding alternative hap-
lotypes and patches. If the primary assembly file is not present for a species
(only defined for standard model organisms), that indicates that there were no
haplotype/patch regions, and in such cases, the ’toplevel file is used. For more
details see: ensembl tutorial
optimize logical, default FALSE. Create a folder within the folder of the gtf, that in-
cludes optimized objects to speed up loading of annotation regions from up to
15 seconds on human genome down to 0.1 second. ORFik will then load these
optimized objects instead. Currently optimizes filterTranscript() function and
loadRegion() function for 5’ UTRs, 3’ UTRs, CDS, mRNA (all transcript with
CDS) and tx (all transcripts).
gene_symbols logical default FALSE. If TRUE, will download and store all gene symbols for
all transcripts (coding and noncoding)- In a file called: "gene_symbol_tx_table.fst"
in same folder as txdb. hgcn for human, mouse symbols for mouse and rat, more
to be added.
uniprot_id logical default FALSE. If TRUE, will download and store all uniprot id for all
transcripts (coding and noncoding)- In a file called: "gene_symbol_tx_table.fst"
in same folder as txdb.
pseudo_5UTRS_if_needed
integer, default NULL. If defined > 0, will add pseudo 5’ UTRs if 30 a leader.
remove_annotation_outliers
logical, default TRUE. Only for refseq. shall outlier lines be removed from the
input annotation_file? If yes, then the initial annotation_file will be overwritten
and the removed outlier lines will be stored at tempdir for further exploration.
Among others Aridopsis refseq contains malformed lines, where this is needed
notify_load_existing
logical, default TRUE. If annotation exists (defined as: locally (a file called
outputs.rds) exists in outputdir), print a small message notifying the user it is
not redownloading. Set to FALSE, if this is not wanted
assembly character, default is assembly = organism, which means getting the first assem-
bly in list, otherwise the name of the assembly wanted, like "GCA_000005845"
will get ecoli substrain k12, which is the most used ones for references. Usually
ignore this for non bacterial species.
getGenomeAndAnnotation 177
Details
Some files that are made after download:
- A fasta index for the genome
- A TxDb to speed up GTF/GFF reading
- Seperat of merged contaminant files
Files that can be made:
- Gene symbols (hgnc, etc)
- Uniprot ids (For name of protein structures)
If you want custom genome or gtf from you hard drive, assign existing paths like this:
annotation <- getGenomeAndAnnotation(GTF = "path/to/gtf.gtf", genome = "path/to/genome.fasta")
Value
a named character vector of path to genomes and gtf downloaded, and additional contaminants if
used. If merge_contaminants is TRUE, will not give individual fasta files to contaminants, but only
the merged one.
See Also
Other STAR: STAR.align.folder(), STAR.align.single(), STAR.allsteps.multiQC(), STAR.index(),
STAR.install(), STAR.multiQC(), STAR.remove.crashed.genome(), install.fastp()
Examples
## Get Saccharomyces cerevisiae genome and gtf (create txdb for R)
#getGenomeAndAnnotation("Saccharomyces cerevisiae", tempdir(), assembly_type = "toplevel")
## Download and add pseudo 5' UTRs
#getGenomeAndAnnotation("Saccharomyces cerevisiae", tempdir(), assembly_type = "toplevel",
# pseudo_5UTRS_if_needed = 100)
## Get Danio rerio genome and gtf (create txdb for R)
#getGenomeAndAnnotation("Danio rerio", tempdir())
output.dir <- "/Bio_data/references/zebrafish"
## Get Danio rerio and Phix contamints to deplete during alignment
#getGenomeAndAnnotation("Danio rerio", output.dir, phix = TRUE)
## Optimize for ORFik (speed up for large annotations like human or zebrafish)
#getGenomeAndAnnotation("Danio rerio", tempdir(), optimize = TRUE)
# Drosophila melanogaster (toplevel exists only)
#getGenomeAndAnnotation("drosophila melanogaster", output.dir = file.path(config["ref"],
# "Drosophila_melanogaster_BDGP6"), assembly_type = "toplevel")
## How to save malformed refseq gffs:
## First run function and let it crash:
#annotation <- getGenomeAndAnnotation(organism = "Arabidopsis thaliana",
# output.dir = "~/Desktop/test_plant/",
# assembly_type = "primary_assembly", db = "refseq")
## Then apply a fix (example for linux, too long rows):
# fixed_gff <- fix_malformed_gff("~/Desktop/test_plant/Arabidopsis_thaliana_genomic_refseq.gff")
## Then updated arguments:
178 getGtfPathFromTxdb
# annotation <- c(fixed_gff, "~/Desktop/test_plant/Arabidopsis_thaliana_genomic_refseq.fna")
# names(annotation) <- c("gtf", "genome")
# Then make the txdb (for faster R use)
# makeTxdbFromGenome(annotation["gtf"], annotation["genome"], organism = "Arabidopsis thaliana")
getGRanges Internal GRanges loader from fst data.frame
Description
Internal GRanges loader from fst data.frame
Usage
getGRanges(df, seqinfo = NULL)
Arguments
df a data.frame with columns minimum 4 columns: seqnames, start, strand
Additional specific columns are:
- width (if not set, width is set to 1 for all reads)
Additional columns will be assigned as meta columns
seqinfo Seqinfo object, defaul NULL (created from ranges). Add to avoid warnings later
on differences in seqinfo.
Value
GRanges object
getGtfPathFromTxdb Get path of GTF that created txdb
Description
Will crash and report proper error if no gtf is found
Usage
getGtfPathFromTxdb(txdb, stop.error = TRUE)
Arguments
txdb a loaded TxDb object
stop.error logical TRUE, stop if Txdb does not have a gtf. If FALSE, return NULL.
Value
a character file path, returns NULL if not valid and stop.error is FALSE.
getNGenesCoverage 179
getNGenesCoverage Get number of genes per coverage table
Description
Used to count genes in ORFik meta plots
Usage
getNGenesCoverage(coverage)
Arguments
coverage a data.table with coverage
Value
number of genes in coverage
getWeights Get weights from a subject GenomicRanges object
Description
Get weights from a subject GenomicRanges object
Usage
getWeights(subject, weight = 1L)
Arguments
subject a GRanges, IRanges or GAlignment object
weight a vector (default: 1L, if 1L it is identical to countOverlaps()), if single number
(!= 1), it applies for all, if more than one must be equal size of ’reads’. else it
must be the string name of a defined meta column in subject "reads", that gives
number of times a read was found. GRanges("chr1", 1, "+", score = 5), would
mean "score" column tells that this alignment region was found 5 times.
Value
a numeric vector of weights of equal size to subject
180 get_bioproject_candidates
get_bioproject_candidates
Query eutils for bioproject IDs
Description
The default query of Ribosome Profiling human, will result in internal entrez search of: Ribo-
some[All Fields] AND Profiling[All Fields] AND ("Homo sapiens"[Organism] OR human[All
Fields])
Usage
get_bioproject_candidates(
term = "Ribosome Profiling human",
as_accession = TRUE,
add_study_title = FALSE,
RetMax = 10000
)
Arguments
term character, default "Ribosome Profiling human". A space is translated into AND,
that means "Ribosome AND Profiling AND human", will give same as above.
To do OR operation, do: "Ribosome OR profiling OR human".
as_accession logical, default TRUE. Get bioproject accessions: PRJNA, PRJEB, PRJDB val-
ues, or IDs (FALSE), numbers only. Accessions are usually the thing needed for
most tools.
add_study_title
logical, default FALSE. If TRUE, return as data table with 2 columns: id: ID or
accessions. title: The title of the study.
RetMax integer, default 10000. How many IDs to return maximum
Value
character vector of Accessions or IDs. If add_study_title is TRUE, returns a data.table.
References
https://www.ncbi.nlm.nih.gov/books/NBK25501/
See Also
Other sra: browseSRA(), download.SRA(), download.SRA.metadata(), download.ebi(), install.sratoolkit(),
rename.SRA.files()
get_genome_fasta 181
Examples
term <- "Ribosome Profiling Saccharomyces cerevisiae"
# get_bioproject_candidates(term)
get_genome_fasta Download genome (fasta), annotation (GTF) and contaminants
Description
This function automatically downloads (if files not already exists) genomes and contaminants speci-
fied for genome alignment. By default, it will use ensembl reference, upon completion, the function
will store a file called file.path(output.dir, "outputs.rds") with the output paths of your
completed genome/annotation downloads. For most non-model nonvertebrate organisms, you need
my fork of biomartr for it to work: devtools::install_github("Roleren/biomartr) If you misspelled
something or crashed, delete wrong files and run again.
Do remake = TRUE, to do it all over again.
Usage
get_genome_fasta(
genome,
output.dir,
organism,
assembly,
assembly_type,
db,
gunzip
)
Arguments
genome logical, default: TRUE, download genome of organism specified in "organism"
argument. If FALSE, check if the downloaded file already exist. If you want to
use a custom gtf from you hard drive, set GTF = FALSE, and assign:
annotation <- getGenomeAndAnnotation(genome = FALSE)
annotation["genome"] = "path/to/genome.fasta".
Will download the primary assembly from Ensembl.
output.dir directory to save downloaded data
organism scientific name of organism, Homo sapiens, Danio rerio, Mus musculus, etc.
See biomartr:::get.ensembl.info() for full list of supported organisms.
assembly character, default is assembly = organism, which means getting the first assem-
bly in list, otherwise the name of the assembly wanted, like "GCA_000005845"
will get ecoli substrain k12, which is the most used ones for references. Usually
ignore this for non bacterial species.
182 get_genome_fasta
assembly_type character, default c("primary_assembly", "toplevel"). Used for ensembl only,
specifies the genome assembly type. Searches for both primary and toplevel,
and if both are found, uses the first by order (so primary is prioritized by de-
fault). The Primary assembly should usually be used if it exists. The "primary
assembly" contains all the top-level sequence regions, excluding alternative hap-
lotypes and patches. If the primary assembly file is not present for a species
(only defined for standard model organisms), that indicates that there were no
haplotype/patch regions, and in such cases, the ’toplevel file is used. For more
details see: ensembl tutorial
db database to use for genome and GTF, default adviced: "ensembl" (remember to
set assembly_type to "primary_assembly", else it will contain haplotypes, very
large file!). Alternatives: "refseq" (reference assemblies) and "genbank" (all
assemblies)
gunzip logical, default TRUE, uncompress downloaded files that are zipped when down-
loaded, should be TRUE!
Details
Some files that are made after download:
- A fasta index for the genome
- A TxDb to speed up GTF/GFF reading
- Seperat of merged contaminant files
Files that can be made:
- Gene symbols (hgnc, etc)
- Uniprot ids (For name of protein structures)
If you want custom genome or gtf from you hard drive, assign existing paths like this:
annotation <- getGenomeAndAnnotation(GTF = "path/to/gtf.gtf", genome = "path/to/genome.fasta")
Value
a named character vector of path to genomes and gtf downloaded, and additional contaminants if
used. If merge_contaminants is TRUE, will not give individual fasta files to contaminants, but only
the merged one.
See Also
Other STAR: STAR.align.folder(), STAR.align.single(), STAR.allsteps.multiQC(), STAR.index(),
STAR.install(), STAR.multiQC(), STAR.remove.crashed.genome(), install.fastp()
Examples
## Get Saccharomyces cerevisiae genome and gtf (create txdb for R)
#getGenomeAndAnnotation("Saccharomyces cerevisiae", tempdir(), assembly_type = "toplevel")
## Download and add pseudo 5' UTRs
#getGenomeAndAnnotation("Saccharomyces cerevisiae", tempdir(), assembly_type = "toplevel",
# pseudo_5UTRS_if_needed = 100)
## Get Danio rerio genome and gtf (create txdb for R)
#getGenomeAndAnnotation("Danio rerio", tempdir())
get_genome_gtf 183
output.dir <- "/Bio_data/references/zebrafish"
## Get Danio rerio and Phix contamints to deplete during alignment
#getGenomeAndAnnotation("Danio rerio", output.dir, phix = TRUE)
## Optimize for ORFik (speed up for large annotations like human or zebrafish)
#getGenomeAndAnnotation("Danio rerio", tempdir(), optimize = TRUE)
# Drosophila melanogaster (toplevel exists only)
#getGenomeAndAnnotation("drosophila melanogaster", output.dir = file.path(config["ref"],
# "Drosophila_melanogaster_BDGP6"), assembly_type = "toplevel")
## How to save malformed refseq gffs:
## First run function and let it crash:
#annotation <- getGenomeAndAnnotation(organism = "Arabidopsis thaliana",
# output.dir = "~/Desktop/test_plant/",
# assembly_type = "primary_assembly", db = "refseq")
## Then apply a fix (example for linux, too long rows):
# fixed_gff <- fix_malformed_gff("~/Desktop/test_plant/Arabidopsis_thaliana_genomic_refseq.gff")
## Then updated arguments:
# annotation <- c(fixed_gff, "~/Desktop/test_plant/Arabidopsis_thaliana_genomic_refseq.fna")
# names(annotation) <- c("gtf", "genome")
# Then make the txdb (for faster R use)
# makeTxdbFromGenome(annotation["gtf"], annotation["genome"], organism = "Arabidopsis thaliana")
get_genome_gtf Download genome (fasta), annotation (GTF) and contaminants
Description
This function automatically downloads (if files not already exists) genomes and contaminants speci-
fied for genome alignment. By default, it will use ensembl reference, upon completion, the function
will store a file called file.path(output.dir, "outputs.rds") with the output paths of your
completed genome/annotation downloads. For most non-model nonvertebrate organisms, you need
my fork of biomartr for it to work: devtools::install_github("Roleren/biomartr) If you misspelled
something or crashed, delete wrong files and run again.
Do remake = TRUE, to do it all over again.
Usage
get_genome_gtf(
GTF,
output.dir,
organism,
assembly,
db,
gunzip,
genome,
optimize = FALSE,
184 get_genome_gtf
uniprot_id = FALSE,
gene_symbols = FALSE,
pseudo_5UTRS_if_needed = NULL,
remove_annotation_outliers = TRUE
)
Arguments
GTF logical, default: TRUE, download gtf of organism specified in "organism" argu-
ment. If FALSE, check if the downloaded file already exist. If you want to use
a custom gtf from you hard drive, set GTF = FALSE, and assign:
annotation <- getGenomeAndAnnotation(gtf = FALSE)
annotation["gtf"] = "path/to/gtf.gtf".
If db is not "ensembl", you will instead get a gff file.
output.dir directory to save downloaded data
organism scientific name of organism, Homo sapiens, Danio rerio, Mus musculus, etc.
See biomartr:::get.ensembl.info() for full list of supported organisms.
assembly character, default is assembly = organism, which means getting the first assem-
bly in list, otherwise the name of the assembly wanted, like "GCA_000005845"
will get ecoli substrain k12, which is the most used ones for references. Usually
ignore this for non bacterial species.
db database to use for genome and GTF, default adviced: "ensembl" (remember to
set assembly_type to "primary_assembly", else it will contain haplotypes, very
large file!). Alternatives: "refseq" (reference assemblies) and "genbank" (all
assemblies)
gunzip logical, default TRUE, uncompress downloaded files that are zipped when down-
loaded, should be TRUE!
genome character path, default NULL. Path to fasta genome, corresponding to the gtf.
must be indexed (.fai file must exist there). If you want to make sure chromo-
some naming of the GTF matches the genome and correct seqlengths. If value
is NULL or FALSE, it will be ignored.
optimize logical, default FALSE. Create a folder within the folder of the gtf, that in-
cludes optimized objects to speed up loading of annotation regions from up to
15 seconds on human genome down to 0.1 second. ORFik will then load these
optimized objects instead. Currently optimizes filterTranscript() function and
loadRegion() function for 5’ UTRs, 3’ UTRs, CDS, mRNA (all transcript with
CDS) and tx (all transcripts).
uniprot_id logical default FALSE. If TRUE, will download and store all uniprot id for all
transcripts (coding and noncoding)- In a file called: "gene_symbol_tx_table.fst"
in same folder as txdb.
gene_symbols logical default FALSE. If TRUE, will download and store all gene symbols for
all transcripts (coding and noncoding)- In a file called: "gene_symbol_tx_table.fst"
in same folder as txdb. hgcn for human, mouse symbols for mouse and rat, more
to be added.
pseudo_5UTRS_if_needed
integer, default NULL. If defined > 0, will add pseudo 5’ UTRs if 30 a leader.
get_genome_gtf 185
remove_annotation_outliers
logical, default TRUE. Only for refseq. shall outlier lines be removed from the
input annotation_file? If yes, then the initial annotation_file will be overwritten
and the removed outlier lines will be stored at tempdir for further exploration.
Among others Aridopsis refseq contains malformed lines, where this is needed
Details
Some files that are made after download:
- A fasta index for the genome
- A TxDb to speed up GTF/GFF reading
- Seperat of merged contaminant files
Files that can be made:
- Gene symbols (hgnc, etc)
- Uniprot ids (For name of protein structures)
If you want custom genome or gtf from you hard drive, assign existing paths like this:
annotation <- getGenomeAndAnnotation(GTF = "path/to/gtf.gtf", genome = "path/to/genome.fasta")
Value
a named character vector of path to genomes and gtf downloaded, and additional contaminants if
used. If merge_contaminants is TRUE, will not give individual fasta files to contaminants, but only
the merged one.
See Also
Other STAR: STAR.align.folder(), STAR.align.single(), STAR.allsteps.multiQC(), STAR.index(),
STAR.install(), STAR.multiQC(), STAR.remove.crashed.genome(), install.fastp()
Examples
## Get Saccharomyces cerevisiae genome and gtf (create txdb for R)
#getGenomeAndAnnotation("Saccharomyces cerevisiae", tempdir(), assembly_type = "toplevel")
## Download and add pseudo 5' UTRs
#getGenomeAndAnnotation("Saccharomyces cerevisiae", tempdir(), assembly_type = "toplevel",
# pseudo_5UTRS_if_needed = 100)
## Get Danio rerio genome and gtf (create txdb for R)
#getGenomeAndAnnotation("Danio rerio", tempdir())
output.dir <- "/Bio_data/references/zebrafish"
## Get Danio rerio and Phix contamints to deplete during alignment
#getGenomeAndAnnotation("Danio rerio", output.dir, phix = TRUE)
## Optimize for ORFik (speed up for large annotations like human or zebrafish)
#getGenomeAndAnnotation("Danio rerio", tempdir(), optimize = TRUE)
# Drosophila melanogaster (toplevel exists only)
#getGenomeAndAnnotation("drosophila melanogaster", output.dir = file.path(config["ref"],
# "Drosophila_melanogaster_BDGP6"), assembly_type = "toplevel")
## How to save malformed refseq gffs:
186 get_noncoding_rna
## First run function and let it crash:
#annotation <- getGenomeAndAnnotation(organism = "Arabidopsis thaliana",
# output.dir = "~/Desktop/test_plant/",
# assembly_type = "primary_assembly", db = "refseq")
## Then apply a fix (example for linux, too long rows):
# fixed_gff <- fix_malformed_gff("~/Desktop/test_plant/Arabidopsis_thaliana_genomic_refseq.gff")
## Then updated arguments:
# annotation <- c(fixed_gff, "~/Desktop/test_plant/Arabidopsis_thaliana_genomic_refseq.fna")
# names(annotation) <- c("gtf", "genome")
# Then make the txdb (for faster R use)
# makeTxdbFromGenome(annotation["gtf"], annotation["genome"], organism = "Arabidopsis thaliana")
get_noncoding_rna Download genome (fasta), annotation (GTF) and contaminants
Description
This function automatically downloads (if files not already exists) genomes and contaminants speci-
fied for genome alignment. By default, it will use ensembl reference, upon completion, the function
will store a file called file.path(output.dir, "outputs.rds") with the output paths of your
completed genome/annotation downloads. For most non-model nonvertebrate organisms, you need
my fork of biomartr for it to work: devtools::install_github("Roleren/biomartr) If you misspelled
something or crashed, delete wrong files and run again.
Do remake = TRUE, to do it all over again.
Usage
get_noncoding_rna(ncRNA, output.dir, organism, gunzip)
Arguments
ncRNA logical or character, default FALSE (not used, no download), if TRUE or defned
path, ncRNA is used as a contaminant reference. If TRUE, will try to find
ncRNA sequences from the gtf file, usually represented as lncRNA (long non-
coding RNA’s). Will let you know if no ncRNA sequences were found in gtf.
If not found try character input:
Alternatives; "auto": Will try to find ncRNA file on NONCODE from organ-
ism, Homo sapiens -> human etc. "auto" will not work for all, then you must
specify the name used by NONCODE, go to the link below and find it. If not
"auto" / "" it must be a character vector of species common name (not scientific
name) Homo sapiens is human, Rattus norwegicus is rat etc, download ncRNA
sequence to filter out with. From NONCODE online server, if you cant find
common name see: http://www.noncode.org/download.php/
output.dir directory to save downloaded data
organism scientific name of organism, Homo sapiens, Danio rerio, Mus musculus, etc.
See biomartr:::get.ensembl.info() for full list of supported organisms.
gunzip logical, default TRUE, uncompress downloaded files that are zipped when down-
loaded, should be TRUE!
get_noncoding_rna 187
Details
Some files that are made after download:
- A fasta index for the genome
- A TxDb to speed up GTF/GFF reading
- Seperat of merged contaminant files
Files that can be made:
- Gene symbols (hgnc, etc)
- Uniprot ids (For name of protein structures)
If you want custom genome or gtf from you hard drive, assign existing paths like this:
annotation <- getGenomeAndAnnotation(GTF = "path/to/gtf.gtf", genome = "path/to/genome.fasta")
Value
a named character vector of path to genomes and gtf downloaded, and additional contaminants if
used. If merge_contaminants is TRUE, will not give individual fasta files to contaminants, but only
the merged one.
See Also
Other STAR: STAR.align.folder(), STAR.align.single(), STAR.allsteps.multiQC(), STAR.index(),
STAR.install(), STAR.multiQC(), STAR.remove.crashed.genome(), install.fastp()
Examples
## Get Saccharomyces cerevisiae genome and gtf (create txdb for R)
#getGenomeAndAnnotation("Saccharomyces cerevisiae", tempdir(), assembly_type = "toplevel")
## Download and add pseudo 5' UTRs
#getGenomeAndAnnotation("Saccharomyces cerevisiae", tempdir(), assembly_type = "toplevel",
# pseudo_5UTRS_if_needed = 100)
## Get Danio rerio genome and gtf (create txdb for R)
#getGenomeAndAnnotation("Danio rerio", tempdir())
output.dir <- "/Bio_data/references/zebrafish"
## Get Danio rerio and Phix contamints to deplete during alignment
#getGenomeAndAnnotation("Danio rerio", output.dir, phix = TRUE)
## Optimize for ORFik (speed up for large annotations like human or zebrafish)
#getGenomeAndAnnotation("Danio rerio", tempdir(), optimize = TRUE)
# Drosophila melanogaster (toplevel exists only)
#getGenomeAndAnnotation("drosophila melanogaster", output.dir = file.path(config["ref"],
# "Drosophila_melanogaster_BDGP6"), assembly_type = "toplevel")
## How to save malformed refseq gffs:
## First run function and let it crash:
#annotation <- getGenomeAndAnnotation(organism = "Arabidopsis thaliana",
# output.dir = "~/Desktop/test_plant/",
# assembly_type = "primary_assembly", db = "refseq")
## Then apply a fix (example for linux, too long rows):
# fixed_gff <- fix_malformed_gff("~/Desktop/test_plant/Arabidopsis_thaliana_genomic_refseq.gff")
## Then updated arguments:
188 get_phix_genome
# annotation <- c(fixed_gff, "~/Desktop/test_plant/Arabidopsis_thaliana_genomic_refseq.fna")
# names(annotation) <- c("gtf", "genome")
# Then make the txdb (for faster R use)
# makeTxdbFromGenome(annotation["gtf"], annotation["genome"], organism = "Arabidopsis thaliana")
get_phix_genome Download genome (fasta), annotation (GTF) and contaminants
Description
This function automatically downloads (if files not already exists) genomes and contaminants speci-
fied for genome alignment. By default, it will use ensembl reference, upon completion, the function
will store a file called file.path(output.dir, "outputs.rds") with the output paths of your
completed genome/annotation downloads. For most non-model nonvertebrate organisms, you need
my fork of biomartr for it to work: devtools::install_github("Roleren/biomartr) If you misspelled
something or crashed, delete wrong files and run again.
Do remake = TRUE, to do it all over again.
Usage
get_phix_genome(phix, output.dir, gunzip)
Arguments
phix logical, default FALSE, download phiX sequence to filter out Illumina control
reads. ORFik defines Phix as a contaminant genome. Phix is used in Illu-
mina sequencers for sequencing quality control. Genome is: refseq, Escherichia
phage phiX174. If sequencing facility created fastq files with the command
bcl2fastq, then there should be very few phix reads left in the fastq files re-
cieved.
output.dir directory to save downloaded data
gunzip logical, default TRUE, uncompress downloaded files that are zipped when down-
loaded, should be TRUE!
Details
Some files that are made after download:
- A fasta index for the genome
- A TxDb to speed up GTF/GFF reading
- Seperat of merged contaminant files
Files that can be made:
- Gene symbols (hgnc, etc)
- Uniprot ids (For name of protein structures)
If you want custom genome or gtf from you hard drive, assign existing paths like this:
annotation <- getGenomeAndAnnotation(GTF = "path/to/gtf.gtf", genome = "path/to/genome.fasta")
get_silva_rRNA 189
Value
a named character vector of path to genomes and gtf downloaded, and additional contaminants if
used. If merge_contaminants is TRUE, will not give individual fasta files to contaminants, but only
the merged one.
See Also
Other STAR: STAR.align.folder(), STAR.align.single(), STAR.allsteps.multiQC(), STAR.index(),
STAR.install(), STAR.multiQC(), STAR.remove.crashed.genome(), install.fastp()
Examples
## Get Saccharomyces cerevisiae genome and gtf (create txdb for R)
#getGenomeAndAnnotation("Saccharomyces cerevisiae", tempdir(), assembly_type = "toplevel")
## Download and add pseudo 5' UTRs
#getGenomeAndAnnotation("Saccharomyces cerevisiae", tempdir(), assembly_type = "toplevel",
# pseudo_5UTRS_if_needed = 100)
## Get Danio rerio genome and gtf (create txdb for R)
#getGenomeAndAnnotation("Danio rerio", tempdir())
output.dir <- "/Bio_data/references/zebrafish"
## Get Danio rerio and Phix contamints to deplete during alignment
#getGenomeAndAnnotation("Danio rerio", output.dir, phix = TRUE)
## Optimize for ORFik (speed up for large annotations like human or zebrafish)
#getGenomeAndAnnotation("Danio rerio", tempdir(), optimize = TRUE)
# Drosophila melanogaster (toplevel exists only)
#getGenomeAndAnnotation("drosophila melanogaster", output.dir = file.path(config["ref"],
# "Drosophila_melanogaster_BDGP6"), assembly_type = "toplevel")
## How to save malformed refseq gffs:
## First run function and let it crash:
#annotation <- getGenomeAndAnnotation(organism = "Arabidopsis thaliana",
# output.dir = "~/Desktop/test_plant/",
# assembly_type = "primary_assembly", db = "refseq")
## Then apply a fix (example for linux, too long rows):
# fixed_gff <- fix_malformed_gff("~/Desktop/test_plant/Arabidopsis_thaliana_genomic_refseq.gff")
## Then updated arguments:
# annotation <- c(fixed_gff, "~/Desktop/test_plant/Arabidopsis_thaliana_genomic_refseq.fna")
# names(annotation) <- c("gtf", "genome")
# Then make the txdb (for faster R use)
# makeTxdbFromGenome(annotation["gtf"], annotation["genome"], organism = "Arabidopsis thaliana")
get_silva_rRNA Download Silva SSU & LSU sequences
Description
Version downloaded is 138.1. NR99_tax (non redundant)
190 groupGRangesBy
Usage
get_silva_rRNA(output.dir)
Arguments
output.dir directory to save downloaded data
Details
If it fails from timeout, set higher timeout: options(timeout = 200)
Value
filepath to downloaded file
Examples
output.dir <- tempdir()
# get_silva_rRNA(output.dir)
groupGRangesBy Group GRanges
Description
It will group / split the GRanges object by the argument ‘other‘. For example if you would like to
to group GRanges object by gene, set other to gene names.
If ‘other‘ is not specified function will try to use the names of the GRanges object. It will then be
similar to ‘split(gr, names(gr))‘.
Usage
groupGRangesBy(gr, other = NULL)
Arguments
gr a GRanges object
other a vector of unique names to group by (default: NULL)
Details
It is important that all intended groups in ‘other‘ are uniquely named, otherwise duplicated group
names will be grouped together.
Value
a GRangesList named after names(GRanges) if other is NULL, else names are from unique(other)
groupings 191
Examples
ORFranges <- GRanges(seqnames = Rle(rep("1", 3)),
ranges = IRanges(start = c(1, 10, 20),
end = c(5, 15, 25)),
strand = "+")
ORFranges2 <- GRanges("1",
ranges = IRanges(start = c(20, 30, 40),
end = c(25, 35, 45)),
strand = "+")
names(ORFranges) = rep("tx1_1", 3)
names(ORFranges2) = rep("tx1_2", 3)
grl <- GRangesList(tx1_1 = ORFranges, tx1_2 = ORFranges2)
gr <- unlist(grl, use.names = FALSE)
## now recreate the grl
## group by orf
grltest <- groupGRangesBy(gr) # using the names to group
identical(grl, grltest) ## they are identical
## group by transcript
names(gr) <- txNames(gr)
grltest <- groupGRangesBy(gr)
identical(grl, grltest) ## they are not identical
groupings Get number of ranges per group as an iteration
Description
Get number of ranges per group as an iteration
Usage
groupings(grl)
Arguments
grl GRangesList
Value
an integer vector
Examples
grl <- GRangesList(GRanges("1", c(1, 3, 5), "+"),
GRanges("1", c(19, 21, 23), "+"))
ORFik::groupings(grl)
192 hasHits
gSort Sort a GRangesList, helper.
Description
A helper for [sortPerGroup()]. A faster, more versatile reimplementation of GenomicRanges::sort()
Normally not used directly. Groups first each group, then either decreasing or increasing (on starts
if byStarts == T, on ends if byStarts == F)
Usage
gSort(grl, decreasing = FALSE, byStarts = TRUE)
Arguments
grl a GRangesList
decreasing should the first in each group have max(start(group)) ->T or min-> default(F) ?
byStarts a logical T, should it order by starts or ends F.
Value
an equally named GRangesList, where each group is sorted within group.
hasHits Hits from reads
Description
Finding GRanges groups that have overlap hits with reads Similar to
Usage
hasHits(grl, reads, keep.names = FALSE, overlaps = NULL)
Arguments
grl a GRangesList or GRanges object
reads a GRanges, GAlignment or GAlignmentPairs object
keep.names logical (F), keep names or not
overlaps default NULL, if not null must be countOverlaps(grl, reads), input if you have
it already.
Value
a list of logicals, T == hit, F == no hit
heatMapL 193
heatMapL Coverage heatmap of multiple libraries
Description
Coverage heatmap of multiple libraries
Usage
heatMapL(
region,
tx,
df,
outdir,
scores = "sum",
upstream,
downstream,
zeroPosition = upstream,
acceptedLengths = NULL,
type = "ofst",
legendPos = "right",
colors = "default",
addFracPlot = TRUE,
location = "TIS",
shifting = NULL,
skip.last = FALSE,
plot.ext = ".pdf",
plot.together = TRUE,
title = TRUE,
scale_x = 5.5,
scale_y = 15.5,
gradient.max = "default",
BPPARAM = BiocParallel::SerialParam()
)
Arguments
region #’ a GRangesList object of region, usually either leaders, cds’, 3’ utrs or ORFs,
start region, stop regions etc. This is the region that will be mapped in heatmap
tx default NULL, a GRangesList of transcripts or (container region), names of tx
must contain all grl names. The names of grl can also be the ORFik orf names.
that is "txName_id"
df an ORFik experiment
outdir a character path to directory to save plot, will be named from ORFik experiment
columns
194 heatMapL
scores character vector, default c("transcriptNormalized", "sum"), either of zs-
core, transcriptNormalized, sum, mean, median, .. see ?coverageScorings for
info and more alternatives.
upstream 1 or 2 integers, default c(50, 30), how long upstream from 0 should window
extend (first index is 5’ end extension, second is 3’ end extension). If only 1
shifting, only 1 value should be given, if two are given will use first.
downstream 1 or 2 integers, default c(29, 69), how long upstream from 0 should window
extend (first index is 5’ end extension, second is 3’ end extension). If only 1
shifting, only 1 value should be given, if two are given will use first.
zeroPosition an integer DEFAULT (upstream), what is the center point? Like leaders and cds
combination, then 0 is the TIS and -1 is last base in leader. NOTE!: if windows
have different widths, this will be ignored.
acceptedLengths
an integer vector (NULL), the read lengths accepted. Default NULL, means all
lengths accepted.
type character, default: "ofst". Type of library: either "default", usually bam format
(the one you gave to experiment), "pshifted" pshifted reads, "ofst", "bed", "bedo"
optimized bed, or "wig"
legendPos a character, Default "right". Where should the fill legend be ? ("top", "bottom",
"right", "left")
colors character vector, default: "default", this gives you: c("white", "yellow2", "yel-
low3", "lightblue", "blue", "navy"), do "high" for more high contrasts, or specify
your own colors.
addFracPlot Add margin histogram plot on top of heatmap with fractions per positions
location a character, default "start site", will make xlabel of heatmap be Position relative
to "start site" or alternative given.
shifting a character, default c("5prime", "3prime"), can also be NULL (no shifting of
reads). If NULL, will use first index of ’upstream’ and ’downstream’ argument.
skip.last skip top(highest) read length, default FALSE
plot.ext a character, default ".pdf", alternative ".png"
plot.together logical (default: FALSE), plot all in 1 plot (if TRUE)
title a character, default NULL (no title), what is the top title of plot?
scale_x numeric, how should the width of the single plots be scaled, bigger the number,
the bigger the plot
scale_y numeric, how should the height of the plots be scaled, bigger the number, the
bigger the plot
gradient.max numeric or character, default: "default", which is: max(coverage$score), the
max coverage over all readlengths. If you want all plots to use same reference
point for max scaling, then first detect this point, look at max in plot etc, and use
that value, to get all plots to have same max point.
BPPARAM a core param, default: single thread: BiocParallel::SerialParam(). Set to
BiocParallel::bpparam() to use multicore. Be aware, this uses a lot of extra
ram (40GB+) for larger human samples!
heatMapRegion 195
Value
invisible(NULL), plots are saved
See Also
Other heatmaps: coverageHeatMap(), heatMapRegion(), heatMap_single()
heatMapRegion Create coverage heatmaps of specified region
Description
Simplified input space for easier abstraction of coverage heatmaps
Pick your transcript region and plot directly
Input CAGE file if you use TSS and want improved 5’ annotation.
Usage
heatMapRegion(
df,
region = "TIS",
outdir = "default",
scores = c("transcriptNormalized", "sum"),
type = "ofst",
cage = NULL,
plot.ext = ".pdf",
acceptedLengths = 21:75,
upstream = c(50, 30),
downstream = c(29, 69),
shifting = c("5prime", "3prime"),
longestPerGene = TRUE,
colors = "default",
scale_x = 5.5,
scale_y = 15.5,
gradient.max = "default",
BPPARAM = BiocParallel::SerialParam()
)
Arguments
df an ORFik experiment
region a character, default "TIS". The centering point for the heatmap (what is position
0, beween -50 and 50 etc), can be any combination of the set: c("TSS", "TIS",
"TTS", "TES"), which are: - Transcription start site (5’ end of mrna)
- Translation initation site (5’ end of CDS)
- Translation termination site (5’ end of 3’ UTRs)
- Transcription end site (3’ end of 3’ UTRs)
196 heatMapRegion
outdir a character path, default: "default", saves to: file.path(QCfolder(df), "heatmaps/"),
a created folder within the ORFik experiment data folder for plots. Change if
you want custom location.
scores character vector, default c("transcriptNormalized", "sum"), either of zs-
core, transcriptNormalized, sum, mean, median, .. see ?coverageScorings for
info and more alternatives.
type character, default: "ofst". Type of library: either "default", usually bam format
(the one you gave to experiment), "pshifted" pshifted reads, "ofst", "bed", "bedo"
optimized bed, or "wig"
cage a character path to library file or a GRanges, GAlignments preloaded file of
CAGE data. Only used if "TSS" is defined as region, to redefine 5’ leaders.
plot.ext a character, default ".pdf", alternative ".png"
acceptedLengths
an integer vector (NULL), the read lengths accepted. Default NULL, means all
lengths accepted.
upstream 1 or 2 integers, default c(50, 30), how long upstream from 0 should window
extend (first index is 5’ end extension, second is 3’ end extension). If only 1
shifting, only 1 value should be given, if two are given will use first.
downstream 1 or 2 integers, default c(29, 69), how long upstream from 0 should window
extend (first index is 5’ end extension, second is 3’ end extension). If only 1
shifting, only 1 value should be given, if two are given will use first.
shifting a character, default c("5prime", "3prime"), can also be NULL (no shifting of
reads). If NULL, will use first index of ’upstream’ and ’downstream’ argument.
longestPerGene logical, default TRUE. Use only longest transcript isoform per gene. This will
speed up your computation.
colors character vector, default: "default", this gives you: c("white", "yellow2", "yel-
low3", "lightblue", "blue", "navy"), do "high" for more high contrasts, or specify
your own colors.
scale_x numeric, how should the width of the single plots be scaled, bigger the number,
the bigger the plot
scale_y numeric, how should the height of the plots be scaled, bigger the number, the
bigger the plot
gradient.max numeric or character, default: "default", which is: max(coverage$score), the
max coverage over all readlengths. If you want all plots to use same reference
point for max scaling, then first detect this point, look at max in plot etc, and use
that value, to get all plots to have same max point.
BPPARAM a core param, default: single thread: BiocParallel::SerialParam(). Set to
BiocParallel::bpparam() to use multicore. Be aware, this uses a lot of extra
ram (40GB+) for larger human samples!
Value
invisible(NULL), plots are saved
heatMap_single 197
See Also
Other heatmaps: coverageHeatMap(), heatMapL(), heatMap_single()
Examples
# Toy example, will not give logical output, but shows how it works
df <- ORFik.template.experiment()[9:10,] # Subset to 2 Ribo-seq libs
#heatMapRegion(df, "TIS", outdir = "default")
#
# Do also TSS, add cage for specific TSS
# heatMapRegion(df, c("TSS", "TIS"), cage = "path/to/cage.bed")
# Do on pshifted reads instead of original files
remove.experiments(df) # Remove loaded experiment first
# heatMapRegion(df, "TIS", type = "pshifted")
heatMap_single Coverage heatmap of single libraries
Description
Coverage heatmap of single libraries
Usage
heatMap_single(
region,
tx,
reads,
outdir,
scores = "sum",
upstream,
downstream,
zeroPosition = upstream,
returnCoverage = FALSE,
acceptedLengths = NULL,
legendPos = "right",
colors = "default",
addFracPlot = TRUE,
location = "start site",
shifting = NULL,
skip.last = FALSE,
title = NULL,
gradient.max = "default"
)
198 heatMap_single
Arguments
region #’ a GRangesList object of region, usually either leaders, cds’, 3’ utrs or ORFs,
start region, stop regions etc. This is the region that will be mapped in heatmap
tx default NULL, a GRangesList of transcripts or (container region), names of tx
must contain all grl names. The names of grl can also be the ORFik orf names.
that is "txName_id"
reads a GAlignments, GRanges, or precomputed coverage as covRleList (where
names of covRle objects are readlengths) of RiboSeq, RnaSeq etc.
Weigths for scoring is default the ’score’ column in ’reads’. Can also be random
access paths to bigWig or fstwig file. Do not use random access for more than a
few genes, then loading the entire files is usually better.
outdir a character path to save file as: not just directory, but full name.
scores character vector, default "sum", either of zscore, transcriptNormalized, sum,
mean, median, .. see ?coverageScorings for info and more alternatives.
upstream an integer, relative region to get upstream from.
downstream an integer, relative region to get downstream from
zeroPosition an integer DEFAULT (upstream), what is the center point? Like leaders and cds
combination, then 0 is the TIS and -1 is last base in leader. NOTE!: if windows
have different widths, this will be ignored.
returnCoverage logical, default: FALSE, return coverage, if FALSE returns plot instead.
acceptedLengths
an integer vector (NULL), the read lengths accepted. Default NULL, means all
lengths accepted.
legendPos a character, Default "right". Where should the fill legend be ? ("top", "bottom",
"right", "left")
colors character vector, default: "default", this gives you: c("white", "yellow2", "yel-
low3", "lightblue", "blue", "navy"), do "high" for more high contrasts, or specify
your own colors.
addFracPlot Add margin histogram plot on top of heatmap with fractions per positions
location a character, default "start site", will make xlabel of heatmap be Position relative
to "start site" or alternative given.
shifting a character, default NULL (no shifting), can also be either of c("5prime", "3prime")
skip.last skip top(highest) read length, default FALSE
title a character, default NULL (no title), what is the top title of plot?
gradient.max numeric or character, default: "default", which is: max(coverage$score), the
max coverage over all readlengths. If you want all plots to use same reference
point for max scaling, then first detect this point, look at max in plot etc, and use
that value, to get all plots to have same max point.
Value
ggplot2 grob (default), data.table (if returnCoverage is TRUE)
import.bedo 199
See Also
Other heatmaps: coverageHeatMap(), heatMapL(), heatMapRegion()
import.bedo Load GRanges object from .bedo
Description
.bedo is .bed ORFik, an optimized bed format for coverage reads with read lengths .bedo is a text
based format with columns (6 maximum):
1. chromosome
2. start
3. end
4. strand
5. ref width (cigar # M’s, match/mismatch total)
6. duplicates of that read
Usage
import.bedo(path)
Arguments
path a character, location on disc (full path)
Details
Positions are 1-based, not 0-based as .bed. export with export.bedo
Value
GRanges object
import.bedoc Load GAlignments object from .bedoc
Description
A much faster way to store, load and use bam files.
.bedoc is .bed ORFik, an optimized bed format for coverage reads with cigar and replicate number.
.bedoc is a text based format with columns (5 maximum):
1. chromosome
2. cigar: (cigar # M’s, match/mismatch total)
3. start (left most position)
4. strand (+, -, *)
5. score: duplicates of that read
200 import.fstwig
Usage
import.bedoc(path)
Arguments
path a character, location on disc (full path)
Details
Positions are 1-based, not 0-based as .bed. export with export.bedo
Value
GAlignments object
import.fstwig Import region from fastwig
Description
Import region from fastwig
Usage
import.fstwig(gr, dir, id = "", readlengths = "all")
Arguments
gr a GRanges object of exons
dir prefix to filepath for file strand and chromosome will be added
id id to column type, not used currently!
readlengths integer / character vector, default "all". Or a subset of readlengths.
Value
a data.table with columns specified by readlengths
import.ofst 201
import.ofst Load GRanges / GAlignments object from .ofst
Description
A much faster way to store, load and use bam files.
.ofst is ORFik fast serialized object, an optimized format for coverage reads with cigar and replicate
number. It uses the fst format as back-end: fst-package.
A .ofst ribo seq file can compress the information in a bam file from 5GB down to a few MB. This
new files has super fast reading time, only a few seconds, instead of minutes. It also has random
index access possibility of the file.
.ofst is represented as a data.frane format with minimum 4 columns:
1. chromosome
2. start (left most position)
3. strand (+, -, *)
4. width (not added if cigar exists)
5. cigar (not needed if width exists): (cigar # M’s, match/mismatch total)
5. score: duplicates of that read
6. size: qwidth according to reference of read
If file is from GAlignmentPairs, it will contain a cigar1, cigar2 instead of cigar and start1 and
start2 instead of start
Usage
import.ofst(file, strandMode = 0, seqinfo = NULL)
Arguments
file a path to a .ofst file
strandMode numeric, default 0. Only used for paired end bam files. One of (0: strand
= *, 1: first read of pair is +, 2: first read of pair is -). See ?strandMode.
Note: Sets default to 0 instead of 1, as readGAlignmentPairs uses 1. This is
to guarantee hits, but will also make mismatches of overlapping transcripts in
opposite directions.
seqinfo Seqinfo object, defaul NULL (created from ranges). Add to avoid warnings later
on differences in seqinfo.
Details
Other columns can be named whatever you want and added to meta columns. Positions are 1-based,
not 0-based as .bed. Import with import.ofst
Value
a GAlignment, GAlignmentPairs or GRanges object, dependent of if cigar/cigar1 is defined in .ofst
file.
202 inhibitorNames
Examples
## GRanges
gr <- GRanges("1:1-3:-")
tmp <- file.path(tempdir(), "path.ofst")
# export.ofst(gr, file = tmp)
# import.ofst(tmp)
## GAlignment
# Make input data.frame
df <- data.frame(seqnames = "1", cigar = "3M", start = 1L, strand = "+")
ga <- ORFik:::getGAlignments(df)
# export.ofst(ga, file = tmp)
# import.ofst(tmp)
importGtfFromTxdb Import the GTF / GFF that made the txdb
Description
Import the GTF / GFF that made the txdb
Usage
importGtfFromTxdb(txdb, stop.error = TRUE)
Arguments
txdb a TxDb, path to txdb / gff or ORFik experiment object
stop.error logical TRUE, stop if Txdb does not have a gtf. If FALSE, return NULL.
Value
data.frame, the gtf/gff object imported with rtracklayer::import. Or NULL, if stop.error is FALSE,
and no GTF file found.
inhibitorNames Get translocation inhibitor name variants
Description
Used to standardize nomeclature for experiments.
Example: cycloheximide, lactimidomycin, harringtonine
Usage
inhibitorNames()
initiationScore 203
Value
a data.table with 2 columns, the main name, and all name variants of the main name in second
column as a list.
See Also
Other experiment_naming: batchNames(), cellLineNames(), cellTypeNames(), conditionNames(),
fractionNames(), libNames(), mainNames(), repNames(), stageNames(), tissueNames()
initiationScore Get initiation score for a GRangesList of ORFs
Description
initiationScore tries to check how much each TIS region resembles, the average of the CDS TIS
regions.
Usage
initiationScore(grl, cds, tx, reads, pShifted = TRUE, weight = "score")
Arguments
grl a GRangesList object with ORFs
cds a GRangesList object with coding sequences
tx a GRangesList of transcripts covering grl.
reads ribo seq reads as GAlignments, GRanges or GRangesList object
pShifted a logical (TRUE), are riboseq reads p-shifted?
weight a vector (default: 1L, if 1L it is identical to countOverlaps()), if single number
(!= 1), it applies for all, if more than one must be equal size of ’reads’. else it
must be the string name of a defined meta column in subject "reads", that gives
number of times a read was found. GRanges("chr1", 1, "+", score = 5), would
mean "score" column tells that this alignment region was found 5 times.
Details
Since this features uses a distance matrix for scoring, values are distributed like this:
As result there is one value per ORF:
0.000: means that ORF had no reads
-1.000: means that ORF is identical to average of CDS
1.000: means that orf is maximum different than average of CDS
If a score column is defined, it will use it as weights, see getWeights
204 insideOutsideORF
Value
an integer vector, 1 score per ORF, with names of grl
References
doi: 10.1186/s12915-017-0416-0
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss() , fpkm(), fpkm_calc(), fractionLength(),
insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(), orfScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
# Good hiting ORF
ORF <- GRanges(seqnames = "1",
ranges = IRanges(21, 40),
strand = "+")
names(ORF) <- c("tx1")
grl <- GRangesList(tx1 = ORF)
# 1 width p-shifted reads
reads <- GRanges("1", IRanges(c(21, 23, 50, 50, 50, 53, 53, 56, 59),
width = 1), "+")
score(reads) <- 28 # original width
cds <- GRanges(seqnames = "1",
ranges = IRanges(50, 80),
strand = "+")
cds <- GRangesList(tx1 = cds)
tx <- GRanges(seqnames = "1",
ranges = IRanges(1, 85),
strand = "+")
tx <- GRangesList(tx1 = tx)
initiationScore(grl, cds, tx, reads, pShifted = TRUE)
insideOutsideORF Inside/Outside score (IO)
Description
Inside/Outside score is defined as
(reads over ORF)/(reads outside ORF and within transcript)
A pseudo-count of one is added to both the ORF and outside sums.
insideOutsideORF 205
Usage
insideOutsideORF(
grl,
RFP,
GtfOrTx,
ds = NULL,
RFP.sorted = FALSE,
weight = 1L,
overlapGrl = NULL
)
Arguments
grl a GRangesList object with usually either leaders, cds’, 3’ utrs or ORFs.
RFP RiboSeq reads as GAlignments, GRanges or GRangesList object
GtfOrTx If it is TxDb object transcripts will be extracted using exonsBy(Gtf, by = "tx",
use.names = TRUE). Else it must be GRangesList
ds numeric vector (NULL), disengagement score. If you have already calculated
disengagementScore, input here to save time.
RFP.sorted logical (FALSE), an optimizer, have you ran this line: RFP <- sort(RFP[countOverlaps(RFP,
tx, type = "within") > 0]) Normally not touched, for internal optimization
purposes.
weight a vector (default: 1L, if 1L it is identical to countOverlaps()), if single number
(!= 1), it applies for all, if more than one must be equal size of ’reads’. else it
must be the string name of a defined meta column in subject "reads", that gives
number of times a read was found. GRanges("chr1", 1, "+", score = 5), would
mean "score" column tells that this alignment region was found 5 times.
overlapGrl an integer, (default: NULL), if defined must be countOverlaps(grl, RFP), added
for speed if you already have it
Value
a named vector of numeric values of scores
References
doi: 10.1242/dev.098345
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss() , fpkm(), fpkm_calc(), fractionLength(),
initiationScore(), isInFrame(), isOverlapping(), kozakSequenceScore(), orfScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
206 install.fastp
Examples
# Check inside outside score of a ORF within a transcript
ORF <- GRanges("1",
ranges = IRanges(start = c(20, 30, 40),
end = c(25, 35, 45)),
strand = "+")
grl <- GRangesList(tx1_1 = ORF)
tx1 <- GRanges(seqnames = "1",
ranges = IRanges(start = c(1, 10, 20, 30, 40, 50),
end = c(5, 15, 25, 35, 45, 200)),
strand = "+")
tx <- GRangesList(tx1 = tx1)
RFP <- GRanges(seqnames = "1",
ranges = IRanges(start = c(1, 4, 30, 60, 80, 90),
end = c(30, 33, 63, 90, 110, 120)),
strand = "+")
insideOutsideORF(grl, RFP, tx)
install.fastp Download and prepare fastp trimmer
Description
On Linux, will not run "make", only use precompiled fastp file.
On Mac OS it will use precompiled binaries.
For windows must be installed through WSL (Windows Subsystem Linux)
Usage
install.fastp(folder = "~/bin")
Arguments
folder path to folder for download, file will be named "fastp", this should be most
recent version. On mac it will search for a folder called fastp-master inside
folder given. Since there is no precompiled version of fastp for Mac OS.
Value
path to runnable fastp
References
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6129281/
install.sratoolkit 207
See Also
Other STAR: STAR.align.folder(), STAR.align.single(), STAR.allsteps.multiQC(), STAR.index(),
STAR.install(), STAR.multiQC(), STAR.remove.crashed.genome(), getGenomeAndAnnotation()
Examples
## With default folder:
#install.fastp()
## Or set manual folder:
folder <- "~/I/WANT/IT/HERE/"
#install.fastp(folder)
install.sratoolkit Download sra toolkit
Description
Currently supported for Linux (64 bit centos and ubunutu is tested to work) and Mac-OS(64 bit). If
other linux distro, centos binaries will be used.
Usage
install.sratoolkit(folder = "~/bin", version = "2.11.3")
Arguments
folder default folder, "~/bin"
version a string, default "2.11.3"
Value
path to fastq-dump in sratoolkit
References
https://ncbi.github.io/sra-tools/fastq-dump.html
See Also
Other sra: browseSRA(), download.SRA(), download.SRA.metadata(), download.ebi(), get_bioproject_candidates(),
rename.SRA.files()
Examples
# install.sratoolkit()
## Custom folder and version (not adviced)
folder <- "/I/WANT/IT/HERE/"
# install.sratoolkit(folder, version = "2.10.9")
208 is.gr_or_grl
is.grl Helper function to check for GRangesList
Description
Helper function to check for GRangesList
Usage
is.grl(class)
Arguments
class the class you want to check if is GRL, either a character from class or the object
itself.
Value
a boolean
See Also
Other validity: checkRFP(), checkRNA(), is.ORF(), is.gr_or_grl(), is.range(), validGRL(),
validSeqlevels()
is.gr_or_grl Helper function to check for GRangesList or GRanges class
Description
Helper function to check for GRangesList or GRanges class
Usage
is.gr_or_grl(class)
Arguments
class the class you want to check if is GRL or GR, either a character from class or the
object itself.
Value
a boolean
See Also
Other validity: checkRFP(), checkRNA(), is.ORF(), is.grl(), is.range(), validGRL(), validSeqlevels()
is.ORF 209
is.ORF Check if all requirements for an ORFik ORF is accepted.
Description
Check if all requirements for an ORFik ORF is accepted.
Usage
is.ORF(grl)
Arguments
grl a GRangesList or GRanges to check
Value
a logical (TRUE/FALSE)
See Also
Other validity: checkRFP(), checkRNA(), is.gr_or_grl(), is.grl(), is.range(), validGRL(),
validSeqlevels()
is.range Helper function to check for ranged object
Description
Helper function to check for ranged object
Usage
is.range(x)
Arguments
x the object to check is a ranged object. Either GRangesList, GRanges, IRanges-
List, IRanges.
Value
a boolean
See Also
Other validity: checkRFP(), checkRNA(), is.ORF(), is.gr_or_grl(), is.grl(), validGRL(),
validSeqlevels()
210 isInFrame
isInFrame Find frame for each orf relative to cds
Description
Input of this function, is the output of the function [distToCds()], or any other relative ORF frame.
Usage
isInFrame(dists)
Arguments
dists a vector of integer distances between ORF and cds. 0 distance means equal
frame
Details
possible outputs: 0: orf is in frame with cds 1: 1 shifted from cds 2: 2 shifted from cds
Value
a logical vector
References
doi: 10.1074/jbc.R116.733899
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss() , fpkm(), fpkm_calc(), fractionLength(),
initiationScore(), insideOutsideORF(), isOverlapping(), kozakSequenceScore(), orfScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
# simple example
isInFrame(c(3,6,8,11,15))
# GRangesList example
grl <- GRangesList(tx1_1 = GRanges("1", IRanges(1,10), "+"))
fiveUTRs <- GRangesList(tx1 = GRanges("1", IRanges(1,20), "+"))
dist <- distToCds(grl, fiveUTRs)
isInFrame <- isInFrame(dist)
isOverlapping 211
isOverlapping Find frame for each orf relative to cds
Description
Input of this function, is the output of the function [distToCds()]
Usage
isOverlapping(dists)
Arguments
dists a vector of distances between ORF and cds
Value
a logical vector
References
doi: 10.1074/jbc.R116.733899
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss() , fpkm(), fpkm_calc(), fractionLength(),
initiationScore(), insideOutsideORF(), isInFrame(), kozakSequenceScore(), orfScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
# simple example
isOverlapping(c(-3,-6,8,11,15))
# GRangesList example
grl <- GRangesList(tx1_1 = GRanges("1", IRanges(1,10), "+"))
fiveUTRs <- GRangesList(tx1 = GRanges("1", IRanges(1,20), "+"))
dist <- distToCds(grl, fiveUTRs)
isOverlapping <- isOverlapping(dist)
212 isPeriodic
isPeriodic Find if there is a periodicity of 3 in the vector
Description
It uses Fourier transform + periodogram for finding periodic vectors on the transcript normalized
counts over all CDS regions from position 0 (TIS) to 149 (or other max position if increased by the
user.
Checks if there is a periodicity and if the periodicity is 3, more precisely between 2.9 and 3.1.
Usage
isPeriodic(x, info = NULL, verbose = FALSE, strict.fft = TRUE)
Arguments
x (numeric) Vector of values to detect periodicity of 3 like in RiboSeq data.
info specify read length if wanted for verbose output.
verbose logical, default FALSE. Report details of analysis/periodogram. Good if you are
not sure if the analysis was correct.
strict.fft logical, TRUE. Use a FFT without noise filter. This means keep only reads
lengths that are "periodic for the human eye". If you want more coverage, set to
FALSE, to also get read lengths that are "messy", but the noise filter detects the
periodicity of 3. This should only be done when you do not need high quality
periodic reads! Example would be differential translation analysis by counts
over each ORF.
Details
Input data:
Transcript normalized means per CDS TIS region, count reads per position, divide that number per
position by the total of that transcript, then sum up these numbers per position for all transcripts.
Detection method:
The maximum dominant Fourier frequencies is found by finding which period has the highest spec-
trum density (using a 10
Value
a logical, if it is periodic.
kozakHeatmap 213
kozakHeatmap Make sequence region heatmap relative to scoring
Description
Given sequences, DNA or RNA. And some score, ribo-seq fpkm, TE etc. Create a heatmap divided
per letter in seqs, by how strong the score is.
Usage
kozakHeatmap(
seqs,
rate,
start = 1,
stop = max(nchar(seqs)),
center = ceiling((stop - start + 1)/2),
min.observations = ">q1",
skip.startCodon = FALSE,
xlab = "TIS",
type = "ribo-seq"
)
Arguments
seqs the sequences (character vector, DNAStringSet)
rate a scoring vector (equal size to seqs)
start position in seqs to start at (first is 1), default 1.
stop position in seqs to stop at (first is 1), default max(nchar(seqs)), that is the longest
sequence length
center position in seqs to center at (first is 1), center will be +1 in heatmap
min.observations
How many observations per position per letter to accept? numeric or quantile,
default (">q1", bigger than quartile 1 (25 percentile)). You can do (10), to get
all with more than 10 observations.
skip.startCodon
startCodon is defined as after centering (position 1, 2 and 3). Should they be
skipped ? default (FALSE). Not relevant if you are not doing Translation initia-
tion sites (TIS).
xlab Region you are checking, default (TIS)
type What type is the rate scoring ? default (ribo-seq)
Details
It will create blocks around the highest rate per position
214 kozakSequenceScore
Value
a ggplot of the heatmap
Examples
## Not run:
if (requireNamespace("BSgenome.Hsapiens.UCSC.hg19")) {
txdbFile <- system.file("extdata", "hg19_knownGene_sample.sqlite",
package = "GenomicFeatures")
#Extract sequences of Coding sequences.
cds <- loadRegion(txdbFile, "cds")
tx <- loadRegion(txdbFile, "mrna")
# Get region to check
kozakRegions <- startRegionString(cds, tx, BSgenome.Hsapiens.UCSC.hg19::Hsapiens
, upstream = 4, 5)
# Some toy ribo-seq fpkm scores on cds
set.seed(3)
fpkm <- sample(1:115, length(cds), replace = TRUE)
kozakHeatmap(kozakRegions, fpkm, 1, 9, skip.startCodon = F)
}
## End(Not run)
kozakSequenceScore Make a score for each ORFs start region by proximity to Kozak
Description
The closer the sequence is to the Kozak sequence the higher the score, based on the experimental
pwms from article referenced. Minimum score is 0 (worst correlation), max is 1 (the best base per
column was chosen).
Usage
kozakSequenceScore(grl, tx, faFile, species = "human", include.N = FALSE)
Arguments
grl a GRangesList grouped by ORF
tx a GRangesList, the reference area for ORFs, each ORF must have a corespond-
ing tx.
faFile FaFile, BSgenome, fasta/index file path or an ORFik experiment. This file is
usually used to find the transcript sequences from some GRangesList.
kozakSequenceScore 215
species ("human"), which species to use, currently supports human (Homo sapiens),
zebrafish (Danio rerio) and mouse (Mus musculus). Both scientific or common
name for these species will work. You can also specify a pfm for your own
species. Syntax of pfm is an rectangular integer matrix, where all columns must
sum to the same value, normally 100. See example for more information. Rows
are in order: c("A", "C", "G", "T")
include.N logical (F), if TRUE, allow N bases to be counted as hits, score will be average
of the other bases. If True, N bases will be added to pfm, automaticly, so dont
include them if you make your own pfm.
Details
Ranges that does not have minimum 15 length (the kozak requirement as a sliding window of size
15 around grl start), will be set to score 0. Since they should not have the posibility to make an
efficient ribosome binding.
Value
a numeric vector with values between 0 and 1
an integer vector, one score per orf
References
doi: https://doi.org/10.1371/journal.pone.0108475
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss() , fpkm(), fpkm_calc(), fractionLength(),
initiationScore(), insideOutsideORF(), isInFrame(), isOverlapping(), orfScore(), rankOrder(),
ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
# Usually the ORFs are found in orfik, which makes names for you etc.
# Here we make an example from scratch
seqName <- "Chromosome"
ORF1 <- GRanges(seqnames = seqName,
ranges = IRanges(c(1007, 1096), width = 60),
strand = c("+", "+"))
ORF2 <- GRanges(seqnames = seqName,
ranges = IRanges(c(400, 100), width = 30),
strand = c("-", "-"))
ORFs <- GRangesList(tx1 = ORF1, tx2 = ORF2)
ORFs <- makeORFNames(ORFs) # need ORF names
tx <- extendLeaders(ORFs, 100)
# get faFile for sequences
faFile <- FaFile(system.file("extdata/Danio_rerio_sample", "genome_dummy.fasta", package = "ORFik"))
kozakSequenceScore(ORFs, tx, faFile)
# For more details see vignettes.
216 lastExonEndPerGroup
kozak_IR_ranking Rank kozak initiation sequences
Description
Defined as region (-4, -1) relative to TIS
Usage
kozak_IR_ranking(cds_k, mrna, dt.ir, faFile, group.min = 10, species = "human")
Arguments
cds_k cds ranges (GRangesList)
mrna mrna ranges (GRangesList)
dt.ir data.table with a column called IR, initiation rate
faFile FaFile, BSgenome, fasta/index file path or an ORFik experiment. This file is
usually used to find the transcript sequences from some GRangesList.
group.min numeric, default 10. Minimum transcripts per initation group to be included
species ("human"), which species to use, currently supports human (Homo sapiens),
zebrafish (Danio rerio) and mouse (Mus musculus). Both scientific or common
name for these species will work. You can also specify a pfm for your own
species. Syntax of pfm is an rectangular integer matrix, where all columns must
sum to the same value, normally 100. See example for more information. Rows
are in order: c("A", "C", "G", "T")
Value
a ggplot grid object
lastExonEndPerGroup Get last end per granges group
Description
Get last end per granges group
Usage
lastExonEndPerGroup(grl, keep.names = TRUE)
Arguments
grl a GRangesList
keep.names a boolean, keep names or not, default: (TRUE)
lastExonPerGroup 217
Value
a Rle(keep.names = T), or integer vector(F)
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
lastExonEndPerGroup(grl)
lastExonPerGroup Get last exon per GRangesList group
Description
grl must be sorted, call ORFik:::sortPerGroup if needed
Usage
lastExonPerGroup(grl)
Arguments
grl a GRangesList
Value
a GRangesList of the last exon per group
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
lastExonPerGroup(grl)
218 length,covRle-method
lastExonStartPerGroup Get last start per granges group
Description
Get last start per granges group
Usage
lastExonStartPerGroup(grl, keep.names = TRUE)
Arguments
grl a GRangesList
keep.names a boolean, keep names or not, default: (TRUE)
Value
a Rle(keep.names = T), or integer vector(F)
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
lastExonStartPerGroup(grl)
length,covRle-method length covRle
Description
Number of chromosomes
Usage
## S4 method for signature 'covRle'
length(x)
Arguments
x a covRle object
length,covRleList-method 219
Value
an integer, number of chromosomes in covRle object
length,covRleList-method
length covRleList
Description
Number of covRle objects
Usage
## S4 method for signature 'covRleList'
length(x)
Arguments
x a covRleList object
Value
an integer, number of covRle objects
lengths,covRle-method lengths covRle
Description
Lengths of each chromosome
Usage
## S4 method for signature 'covRle'
lengths(x)
Arguments
x a covRle object
Value
a named integer vector of chromosome lengths
220 libFolder
lengths,covRleList-method
lengths covRleList
Description
Lengths of each chromosome
Usage
## S4 method for signature 'covRleList'
lengths(x)
Arguments
x a covRle object
Value
a named integer vector of chromosome lengths
libFolder Get ORFik experiment library folder
Description
Get ORFik experiment library folder
Usage
libFolder(x, mode = "first")
Arguments
x an ORFik experiment
mode character, default "first". Alternatives: "unique", "all".
Value
a character path
libFolder,experiment-method 221
libFolder,experiment-method
Get ORFik experiment library folder
Description
Get ORFik experiment library folder
Usage
## S4 method for signature 'experiment'
libFolder(x, mode = "first")
Arguments
x an ORFik experiment
mode character, default "first". Alternatives: "unique", "all".
Value
a character path
libNames Get library name variants
Description
Used to standardize nomeclature for experiments.
Example: RFP is main naming, but a variant is ribo-seq ribo-seq will then be renamed to RFP
Usage
libNames()
Value
a data.table with 2 columns, the main name, and all name variants of the main name in second
column as a list.
See Also
Other experiment_naming: batchNames(), cellLineNames(), cellTypeNames(), conditionNames(),
fractionNames(), inhibitorNames(), mainNames(), repNames(), stageNames(), tissueNames()
222 list.experiments
libraryTypes Which type of library type in experiment?
Description
Which type of library type in experiment?
Usage
libraryTypes(df, uniqueTypes = TRUE)
Arguments
df an ORFik experiment
uniqueTypes logical, default TRUE. Only return unique lib types.
Value
library types (character vector)
See Also
Other ORFik_experiment: ORFik.template.experiment(), ORFik.template.experiment.zf(),
bamVarName(), create.experiment(), experiment-class, filepath(), organism,experiment-method,
outputLibs(), read.experiment(), save.experiment(), validateExperiments()
Examples
df <- ORFik.template.experiment()
libraryTypes(df)
libraryTypes(df, uniqueTypes = FALSE)
list.experiments List current experiment available
Description
Will only search .csv extension, also exclude any experiment with the word template.
Usage
list.experiments(
dir = ORFik::config()["exp"],
pattern = "*",
libtypeExclusive = NULL,
validate = TRUE,
BPPARAM = bpparam()
)
list.genomes 223
Arguments
dir directory for ORFik experiments: default: ORFik::config()["exp"], which by
default is: "~/Bio_data/ORFik_experiments/"
pattern allowed patterns in experiment file name: default ("*", all experiments)
libtypeExclusive
search for experiments with exclusivly this libtype, default (NULL, all)
validate logical, default TRUE. Abort if any library files does not exist. Do not set this
to FALSE, unless you know what you are doing!
BPPARAM how many cores/threads to use? default: bpparam()
Value
a data.table, 1 row per experiment with columns:
- experiment (name),
- organism
- author
- libtypes
- number of samples
Examples
## Make your experiments
df <- ORFik.template.experiment(TRUE)
df2 <- df[1:6,] # Only first 2 libs
## Save them
# save.experiment(df, "~/Bio_data/ORFik_experiments/exp1.csv")
# save.experiment(df2, "~/Bio_data/ORFik_experiments/exp1_subset.csv")
## List all experiment you have:
## Path above is default path, so no dir argument needed
#list.experiments()
#list.experiments(pattern = "subset")
## For non default directory experiments
#list.experiments(dir = "MY/CUSTOM/PATH)
list.genomes List genomes created with ORFik
Description
Given the reference.folder, list all valid references. An ORFik genome is defined as a folder with
a file called output.rds that is a named R vector with names gtf and genome, where the values are
character paths to those files inside that folder. This makes sure that this reference was made by
ORFik and not some other program.
Usage
list.genomes(reference.folder = ORFik::config()["ref"])
224 loadRegion
Arguments
reference.folder
character path, default: ORFik::config()["ref"].
Value
a data.table with 5 columns:
- character (name of folder)
- logical (does it have a gtf)
- logical (does it have a fasta genome)
- logical (does it have a STAR index)
- logical (only displayed if some are TRUE, does it have protein structure predictions of ORFs from
alphafold etc, in folder called ’protein_structure_predictions’)
- logical (only displayed if some are TRUE, does it have gene symbol fst file from bioMart etc, in
file called ’gene_symbol_tx_table.fst’)
Examples
## Run with default config path
#list.genomes()
## Run with custom config path
list.genomes(tempdir())
## Get the path to fasta genome of first organism in list
#readRDS(file.path(config()["ref"], list.genomes()$name, "outputs.rds")[1])["genome"]
loadRegion Load transcript region
Description
Usefull to simplify loading of standard regions, like cds’ and leaders. Adds another safety in that
seqlevels will be set
Usage
loadRegion(
txdb,
part = "tx",
names.keep = NULL,
by = "tx",
skip.optimized = FALSE
)
loadRegions 225
Arguments
txdb a TxDb file or a path to one of: (.gtf ,.gff, .gff2, .gff2, .db or .sqlite), if it is a
GRangesList, it will return it self.
part a character, one of: tx, ncRNA, mrna, leader, cds, trailer, intron, NOTE: dif-
ference between tx and mrna is that tx are all transcripts, while mrna are all
transcripts with a cds, respectivly ncRNA are all tx without a cds.
names.keep a character vector of subset of names to keep. Example: loadRegions(txdb,
names = "ENST1000005"), will return only that transcript. Remember if you
set by to "gene", then this list must be with gene names.
by a character, default "tx" Either "tx" or "gene". What names to output region by,
the transcript name "tx" or gene names "gene". NOTE: this is not the same as
cdsBy(txdb, by = "gene"), cdsBy would then only give 1 cds per Gene, loadRe-
gion gives all isoforms, but with gene names.
skip.optimized logical, default FALSE. If TRUE, will not search for optimized rds files to load
created from ORFik::makeTxdbFromGenome(..., optimize = TRUE). The opti-
mized files are ~ 100x faster to load for human genome.
Details
Load as GRangesList if input is not already GRangesList.
Value
a GRangesList of region
Examples
# GTF file is slow, but possible to use
gtf <- system.file("extdata", "hg19_knownGene_sample.sqlite",
package = "GenomicFeatures")
txdb <- loadTxdb(gtf)
loadRegion(txdb, "cds")
loadRegion(txdb, "intron")
# Use txdb from experiment
df <- ORFik.template.experiment()
txdb <- loadTxdb(df)
loadRegion(txdb, "leaders")
# Use ORFik experiment directly
loadRegion(df, "mrna")
loadRegions Get all regions of transcripts specified to environment
Description
By default loads all parts to .GlobalEnv (global environemnt) Useful to not spend time on finding
the functions to load regions.
226 loadRegions
Usage
loadRegions(
txdb,
parts = c("mrna", "leaders", "cds", "trailers"),
extension = "",
names.keep = NULL,
by = "tx",
skip.optimized = FALSE,
envir = .GlobalEnv
)
Arguments
txdb a TxDb file, a path to one of: (.gtf ,.gff, .gff2, .gff2, .db or .sqlite) or an ORFik
experiment
parts the transcript parts you want, default: c("mrna", "leaders", "cds", "trailers").
See ?loadRegion for more info on this argument.
extension What to add on the name after leader, like: B -> leadersB
names.keep a character vector of subset of names to keep. Example: loadRegions(txdb,
names = "ENST1000005"), will return only that transcript. Remember if you
set by to "gene", then this list must be with gene names.
by a character, default "tx" Either "tx" or "gene". What names to output region by,
the transcript name "tx" or gene names "gene". NOTE: this is not the same as
cdsBy(txdb, by = "gene"), cdsBy would then only give 1 cds per Gene, loadRe-
gion gives all isoforms, but with gene names.
skip.optimized logical, default FALSE. If TRUE, will not search for optimized rds files to load
created from ORFik::makeTxdbFromGenome(..., optimize = TRUE). The opti-
mized files are ~ 100x faster to load for human genome.
envir Which environment to save to, default: .GlobalEnv
Value
invisible(NULL) (regions saved in envir)
Examples
# Load all mrna regions to Global environment
gtf <- system.file("extdata", "hg19_knownGene_sample.sqlite",
package = "GenomicFeatures")
loadRegions(gtf, parts = c("mrna", "leaders", "cds", "trailers"))
loadTranscriptType 227
loadTranscriptType Load transcripts of given biotype
Description
Like rRNA, snoRNA etc. NOTE: Only works on gtf/gff, not .db object for now. Also note that
these anotations are not perfect, some rRNA annotations only contain 5S rRNA etc. If your
gtf does not contain evertyhing you need, use a resource like repeatmasker and download a gtf:
https://genome.ucsc.edu/cgi-bin/hgTables
Usage
loadTranscriptType(object, part = "rRNA", tx = NULL)
Arguments
object a TxDb, ORFik experiment or path to gtf/gff,
part a character, default rRNA. Can also be: snoRNA, tRNA etc. As long as that
biotype is defined in the gtf.
tx a GRangesList of transcripts (Optional, default NULL, all transcript of that
type), else it must be names a list to subset on.
Value
a GRangesList of transcript of that type
References
doi: 10.1002/0471250953.bi0410s25
Examples
gtf <- "path/to.gtf"
#loadTranscriptType(gtf, part = "rRNA")
#loadTranscriptType(gtf, part = "miRNA")
loadTxdb General loader for txdb
Description
Useful to allow fast TxDb loader like .db
Usage
loadTxdb(txdb, chrStyle = NULL, organism = NA, chrominfo = NULL)
228 longestORFs
Arguments
txdb a TxDb file, a path to one of: (.gtf ,.gff, .gff2, .gff2, .db or .sqlite) or an ORFik
experiment
chrStyle a GRanges object, TxDb, FaFile, , a seqlevelsStyle or Seqinfo. (Default:
NULL) to get seqlevelsStyle from. In addition if it is a Seqinfo object, seqinfo
will be updated. Example of seqlevelsStyle update: Is chromosome 1 called
chr1 or 1, is mitocondrial chromosome called MT or chrM etc. Will use 1st
seqlevel-style if more are present. Like: c("NCBI", "UCSC") -> pick "NCBI"
organism character, default NA. Scientific name of organism. Only used if input is path to
gff.
chrominfo Seqinfo object, default NULL. Only used if input is path to gff.
Value
a TxDb object
Examples
library(GenomicFeatures)
# Get the gtf txdb file
txdbFile <- system.file("extdata", "hg19_knownGene_sample.sqlite",
package = "GenomicFeatures")
txdb <- loadTxdb(txdbFile)
longestORFs Get longest ORF per stop site
Description
Rule: if seqname, strand and stop site is equal, take longest one. Else keep. If IRangesList or
IRanges, seqnames are groups, if GRanges or GRangesList seqnames are the seqlevels (e.g. chro-
mosomes/transcripts)
Usage
longestORFs(grl)
Arguments
grl a GRangesList/IRangesList, GRanges/IRanges of ORFs
Value
a GRangesList/IRangesList, GRanges/IRanges (same as input)
mainNames 229
See Also
Other ORFHelpers: defineTrailer(), mapToGRanges(), orfID(), startCodons(), startSites(),
stopCodons(), stopSites(), txNames(), uniqueGroups(), uniqueOrder()
Examples
ORF1 = GRanges("1", IRanges(10,21), "+")
ORF2 = GRanges("1", IRanges(1,21), "+") # <- longest
grl <- GRangesList(ORF1 = ORF1, ORF2 = ORF2)
longestORFs(grl) # get only longest
mainNames Get main name from variant name
Description
Used to standardize nomeclature for experiments.
Example: RFP is main naming, but a variant is ribo-seq ribo-seq will then be renamed to RFP
Usage
mainNames(names, dt)
Arguments
names a character vector of names that must exist in dt$allNames
dt a data.table with 2 columns (mainName, allNames)
Value
a data.table with 2 columns, the main name, and all name variants of the main name in second
column as a list.
See Also
Other experiment_naming: batchNames(), cellLineNames(), cellTypeNames(), conditionNames(),
fractionNames(), inhibitorNames(), libNames(), repNames(), stageNames(), tissueNames()
230 makeORFNames
makeExonRanks Make grouping by exons ranks
Description
There are two ways to make vector of exon ranking: 1. Iterate per exon in ORF, byTranscript =
FALSE 2. Iterate per ORF in transcript, byTranscript = TRUE.
Usage
makeExonRanks(grl, byTranscript = FALSE)
Arguments
grl a GRangesList
byTranscript logical (default: FALSE), groups orfs by transcript name or ORF name, if ORfs
are by transcript, check duplicates.
Details
Either by transcript or by original groupings. Must be ordered, so that same transcripts are ordered
together.
Value
an integer vector of indices for exon ranks
makeORFNames Make ORF names per orf
Description
grl must be grouped by transcript If a list of orfs are grouped by transcripts, but does not have ORF
names, then create them and return the new GRangesList
Usage
makeORFNames(grl, groupByTx = TRUE)
Arguments
grl a GRangesList
groupByTx logical (T), should output GRangesList be grouped by transcripts (T) or by
ORFs (F)?
makeSummarizedExperimentFromBam 231
Value
(GRangesList) with ORF names, grouped by transcripts, sorted.
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
makeORFNames(grl)
makeSummarizedExperimentFromBam
Make a count matrix from a library or experiment
Description
Make a summerizedExperiment / matrix object from bam files or other library formats sepcified by
lib.type argument. Works like HTSeq, to give you count tables per library.
Usage
makeSummarizedExperimentFromBam(
df,
saveName = NULL,
longestPerGene = FALSE,
geneOrTxNames = "tx",
region = "mrna",
type = "count",
lib.type = "ofst",
weight = "score",
forceRemake = FALSE,
force = TRUE,
BPPARAM = BiocParallel::SerialParam()
)
Arguments
df an ORFik experiment
saveName a character (default NULL), if set save experiment to path given. Always saved
as .rds., it is optional to add .rds, it will be added for you if not present. Also
used to load existing file with that name.
232 makeSummarizedExperimentFromBam
longestPerGene a logical (default FALSE), if FALSE all transcript isoforms per gene. Ignored if
"region" is not a character of either: "mRNA","tx", "cds", "leaders" or "trailers".
geneOrTxNames a character vector (default "tx"), should row names keep trancript names ("tx")
or change to gene names ("gene")
region a character vector (default: "mrna"), make raw count matrices of whole mrnas
or one of (leaders, cds, trailers). Can also be a GRangesList, then it uses this
region directly. Can then be uORFs or a subset of CDS etc.
type default: "count" (raw counts matrix), alternative is "fpkm", "log2fpkm" or "log10fpkm"
lib.type a character(default: "default"), load files in experiment or some precomputed
variant, either "ofst", "bedo", "bedoc" or "pshifted". These are made with OR-
Fik:::convertLibs() or shiftFootprintsByExperiment(). Can also be custom user
made folders inside the experiments bam folder.
weight numeric or character, a column to score overlaps by. Default "score", will check
for a metacolumn called "score" in libraries. If not found, will not use weights.
forceRemake logical, default FALSE. If TRUE, will not look for existing file count table files.
force logical, default TRUE. IF TRUE, will not use existing libraries found in envi-
ronment of experiment. See argument ’force’ in link{outputLibs}
BPPARAM how many cores/threads to use? default: BiocParallel::SerialParam()
Details
If txdb or gtf path is added, it is a rangedSummerizedExperiment NOTE: If the file called saveName
exists, it will then load file, not remake it!
There are different ways of counting hits on transcripts, ORFik does it as pure coverage (if a single
read aligns to a region with 2 genes, both gets a count of 1 from that read). This is the safest way to
avoid false negatives (genes with no assigned hits that actually have true hits).
Value
a SummarizedExperiment object or data.table if "type" is not "count, with rownames as transcript
/ gene names.
Examples
##Make experiment
df <- ORFik.template.experiment()
# makeSummarizedExperimentFromBam(df)
## Only cds (coding sequences):
# makeSummarizedExperimentFromBam(df, region = "cds")
## FPKM instead of raw counts on whole mrna regions
# makeSummarizedExperimentFromBam(df, type = "fpkm")
## Make count tables of pshifted libraries over uORFs
uorfs <- GRangesList(uorf1 = GRanges("chr23", 17599129:17599156, "-"))
#saveName <- file.path(dirname(df$filepath[1]), "uORFs", "countTable_uORFs")
#makeSummarizedExperimentFromBam(df, saveName, region = uorfs)
## To load the uORFs later
# countTable(df, region = "uORFs", count.folder = "uORFs")
makeTxdbFromGenome 233
makeTxdbFromGenome Make txdb from genome
Description
Make a Txdb with defined seqlevels and seqlevelsstyle from the fasta genome. This makes it more
fail safe than standard Txdb creation. Example is that you can not create a coverage window outside
the chromosome boundary, this is only possible if you have set the seqlengths.
Usage
makeTxdbFromGenome(
gtf,
genome = NULL,
organism,
optimize = FALSE,
gene_symbols = FALSE,
uniprot_id = FALSE,
pseudo_5UTRS_if_needed = NULL,
return = FALSE
)
Arguments
gtf path to gtf file
genome character, default NULL. Path to fasta genome corresponding to the gtf. If
NULL, can not set seqlevels. If value is NULL or FALSE, it will be ignored.
organism Scientific name of organism, first letter must be capital! Example: Homo sapi-
ens. Will force first letter to capital and convert any "_" (underscore) to " "
(space)
optimize logical, default FALSE. Create a folder within the folder of the gtf, that in-
cludes optimized objects to speed up loading of annotation regions from up to
15 seconds on human genome down to 0.1 second. ORFik will then load these
optimized objects instead. Currently optimizes filterTranscript() function and
loadRegion() function for 5’ UTRs, 3’ UTRs, CDS, mRNA (all transcript with
CDS) and tx (all transcripts).
gene_symbols logical default FALSE. If TRUE, will download and store all gene symbols for
all transcripts (coding and noncoding)- In a file called: "gene_symbol_tx_table.fst"
in same folder as txdb. hgcn for human, mouse symbols for mouse and rat, more
to be added.
uniprot_id logical default FALSE. If TRUE, will download and store all uniprot id for all
transcripts (coding and noncoding)- In a file called: "gene_symbol_tx_table.fst"
in same folder as txdb.
pseudo_5UTRS_if_needed
integer, default NULL. If defined > 0, will add pseudo 5’ UTRs if 30 a leader.
return logical, default FALSE. If TRUE, return TXDB object, else NULL.
234 mapToGRanges
Value
NULL, Txdb saved to disc named paste0(gtf, ".db"). Set ’return’ argument to TRUE, to get txdb
back
Examples
gtf <- "/path/to/local/annotation.gtf"
genome <- "/path/to/local/genome.fasta"
#makeTxdbFromGenome(gtf, genome, organism = "Saccharomyces cerevisiae")
## Add pseudo UTRs if needed (< 30% of cds have a defined 5'UTR)
mapToGRanges Map orfs to genomic coordinates
Description
Creates GRangesList from the results of ORFs_as_List and the GRangesList used to find the ORFs
Usage
mapToGRanges(grl, result, groupByTx = TRUE)
Arguments
grl A GRangesList of the original sequences that gave the orfs in Genomic coordi-
nates.
result IRangesList A list of the results of finding uorfs list syntax is: Per list group in
IRangesList is per grl index. In transcript coordinates. The names are grl index
as character.
groupByTx logical (T), should output GRangesList be grouped by transcripts (T) or by
ORFs (F)?
Details
There is no check on invalid matches, so be carefull if you use this function directly.
Value
A GRangesList of ORFs.
See Also
Other ORFHelpers: defineTrailer(), longestORFs(), orfID(), startCodons(), startSites(),
stopCodons(), stopSites(), txNames(), uniqueGroups(), uniqueOrder()
matchColors 235
matchColors Match coloring of coverage plot
Description
Check that colors match with the number of fractions.
Usage
matchColors(coverage, colors)
Arguments
coverage a data.table with coverage
colors a character vector of colors
Value
number of genes in coverage
matchNaming Match naming of GRangesList
Description
Given a GRangesList and a reference, make the naming convention and the number of metacolumns
equal to reference
Usage
matchNaming(gr, reference)
Arguments
gr a GRangesList or GRanges object
reference a GRangesList of a reference
Value
a GRangesList
236 mergeFastq
matchSeqStyle A wrapper for seqlevelsStyle
Description
To make sure chromosome naming is correct (chr1 vs 1 vs I etc)
Usage
matchSeqStyle(range, chrStyle = NULL)
Arguments
range a ranged object, (GRanges, GAlignment etc)
chrStyle a GRanges object, TxDb, FaFile, , a seqlevelsStyle or Seqinfo. (Default:
NULL) to get seqlevelsStyle from. In addition if it is a Seqinfo object, seqinfo
will be updated. Example of seqlevelsStyle update: Is chromosome 1 called
chr1 or 1, is mitocondrial chromosome called MT or chrM etc. Will use 1st
seqlevel-style if more are present. Like: c("NCBI", "UCSC") -> pick "NCBI"
Value
a GAlignment/GRanges object depending on input.
mergeFastq Merge groups of Fastq /Fasta files
Description
Will use multithreading to speed up process. Only works for Unix OS (Linux and Mac)
Usage
mergeFastq(in_files, out_files, BPPARAM = bpparam())
Arguments
in_files character specify the full path to the individual fastq.gz files. Seperated by
space per file in group: For 2 output files from 4 input files: in_files <- c("file1.fastq
file2.fastq". "file3.fastq file4.fastq")
out_files character specify the path to the FASTQ directory For 2 output files: out_files
<- c("/merged/file1&2.fastq", "/merged/file3&4.fastq")
BPPARAM how many cores/threads to use? default: bpparam(). To see number of threads
used, do bpparam()$workers
mergeLibs 237
Value
invisible(NULL).
Examples
fastq.folder <- tempdir() # <- Your fastq files
infiles <- dir(fastq.folder, "*.fastq", full.names = TRUE)
## Not run:
# Seperate files into groups (here it is 4 output files from 12 input files)
in_files <- c(paste0(grep(infiles, pattern = paste0("ribopool-",
seq(11, 14), collapse = "|"), value = TRUE), collapse = " "),
paste0(grep(infiles, pattern = paste0("ribopool-",
seq(18, 19), collapse = "|"), value = TRUE), collapse = " "),
paste0(grep(infiles, pattern = paste0("C11-",
seq(11, 14), collapse = "|"), value = TRUE), collapse = " "),
paste0(grep(infiles, pattern = paste0("C11-",
seq(18, 19), collapse = "|"), value = TRUE), collapse = " "))
out_files <- paste0(c("SSU_ribopool", "LSU_ribopool", "SSU_WT", "LSU_WT"), ".fastq.gz")
merged.fastq.folder <- file.path(fastq.folder, "merged/")
out_files <- file.path(merged.fastq.folder, out_files)
mergeFastq(in_files, out_files)
## End(Not run)
mergeLibs Merge and save libraries of experiment
Description
Aggregate count of reads (from the "score" column) by making a merged library. Only allowed for
.ofst files!
Usage
mergeLibs(
df,
out_dir = file.path(libFolder(df), "ofst_merged"),
mode = "all",
type = "ofst",
keep_all_scores = TRUE
)
Arguments
df an ORFik experiment
238 mergeLibs
out_dir Ouput directory, default file.path(dirname(df$filepath[1]), "ofst_merged"),
saved as "all.ofst" in this folder if mode is "all". Use a folder called pshifted_merged,
for default Ribo-seq ofst files.
mode character, default "all". Merge all or "rep" for collapsing replicates only, or "lib"
for collapsing all per library type.
type a character(default: "default"), load files in experiment or some precomputed
variant, like "ofst" or "pshifted". These are made with ORFik:::convertLibs(),
shiftFootprintsByExperiment(), etc. Can also be custom user made folders in-
side the experiments bam folder. It acts in a recursive manner with priority: If
you state "pshifted", but it does not exist, it checks "ofst". If no .ofst files, it uses
"default", which always must exists.
Presets are (folder is relative to default lib folder, some types fall back to other
formats if folder does not exist):
- "default": load the original files for experiment, usually bam.
- "ofst": loads ofst files from the ofst folder, relative to lib folder (falls back to
default)
- "pshifted": loads ofst, wig or bigwig from pshifted folder (falls back to ofst,
then default)
- "cov": Load covRle objects from cov_RLE folder (fail if not found)
- "covl": Load covRleList objects, from cov_RLE_List folder (fail if not found)
- "bed": Load bed files, from bed folder (falls back to default)
- Other formats must be loaded directly with fimport
keep_all_scores
logical, default TRUE, keep all library scores in the merged file. These score
columns are named the libraries full name from bamVarName(df).
Value
NULL, files saved to disc. A data.table with a score column that now contains the sum of scores
per merge setting.
Examples
df2 <- ORFik.template.experiment()
df2 <- df2[df2$libtype == "RFP",]
# Merge all
#mergeLibs(df2, tempdir(), mode = "all", type = "default")
# Read as GRanges with mcols
#fimport(file.path(tempdir(), "all.ofst"))
# Read as direct fst data.table
#read_fst(file.path(tempdir(), "all.ofst"))
# Collapse replicates
#mergeLibs(df2, tempdir(), mode = "rep", type = "default")
# Collapse by lib types
#mergeLibs(df2, tempdir(), mode = "lib", type = "default")
metadata.autnaming 239
metadata.autnaming Guess SRA metadata columns
Description
Guess SRA metadata columns
Usage
metadata.autnaming(file)
Arguments
file a data.table of SRA metadata
Value
a data.table of SRA metadata with additional columns: LIBRARYTYPE, REPLICATE, STAGE,
CONDITION, INHIBITOR
metaWindow Calculate meta-coverage of reads around input GRanges/List object.
Description
Sums up coverage over set of GRanges objects as a meta representation.
Usage
metaWindow(
x,
windows,
scoring = "sum",
withFrames = FALSE,
zeroPosition = NULL,
scaleTo = 100,
fraction = NULL,
feature = NULL,
forceUniqueEven = !is.null(scoring),
forceRescale = TRUE,
weight = "score",
drop.zero.dt = FALSE,
append.zeroes = FALSE
)
240 metaWindow
Arguments
x GRanges/GAlignment object of your reads. Remember to resize them before-
hand to width of 1 to focus on 5’ ends of footprints etc, if that is wanted.
windows GRangesList or GRanges of your ranges
scoring a character, default: "sum", one of (zscore, transcriptNormalized, mean, median,
sum, sumLength, NULL), see ?coverageScorings for info and more alternatives.
withFrames a logical (TRUE), return positions with the 3 frames, relative to zeroPosition.
zeroPosition is frame 0.
zeroPosition an integer DEFAULT (NULL), the point if all windows are equal size, that
should be set to position 0. Like leaders and cds combination, then 0 is the
TIS and -1 is last base in leader. NOTE!: if not all windows have equal width,
this will be ignored. If all have equal width and zeroPosition is NULL, it is set
to as.integer(width / 2).
scaleTo an integer (100), if windows have different size, a meta window can not directly
be created, since a meta window must have equal size for all windows. Rescale
(bin) all windows to scaleTo. i.e c(1,2,3) -> size 2 -> coverage of position c(1,
mean(2,3)) etc.
fraction a character/integer (NULL), the fraction i.e (27) for read length 27, or ("LSU")
for large sub-unit TCP-seq.
feature a character string, info on region. Usually either gene name, transcript part like
cds, leader, or CpG motifs etc.
forceUniqueEven
a logical (TRUE), if TRUE; require that all windows are of same width and even.
To avoid bugs. FALSE if score is NULL.
forceRescale logical, default TRUE. If TRUE, if unique(widthPerGroup(windows)) has
length > 1, it will force all windows to width of the scaleTo argument, making
a binned meta coverage.
weight (default: ’score’), if defined a character name of valid meta column in subject.
GRanges("chr1", 1, "+", score = 5), would mean score column tells that this
alignment region was found 5 times. ORFik ofst, bedoc and .bedo files contains
a score column like this. As do CAGEr CAGE files and many other package
formats. You can also assign a score column manually.
drop.zero.dt logical FALSE, if TRUE and as.data.table is TRUE, remove all 0 count posi-
tions. This greatly speeds up and most importantly, greatly reduces memory
usage. Will not change any plots, unless 0 positions are used in some sense.
(mean, median, zscore coverage will only scale differently)
append.zeroes logical, default FALSE. If TRUE and drop.zero.dt is TRUE and all windows
have equal length, it will add back 0 values after transformation. Sometimes
needed for correct plots, if TRUE, will call abort if not all windows are equal
length!
Value
A data.table with scored counts (score) of reads mapped to positions (position) specified in windows
along with frame (frame) per gene (genes) per library (fraction) per transcript region (feature).
Column that does not apply is not given, but position and (score/count) is always returned.
model.matrix,experiment-method 241
See Also
Other coverage: coverageScorings(), regionPerReadLength(), scaledWindowPositions(),
windowPerReadLength()
Examples
library(GenomicRanges)
windows <- GRangesList(GRanges("chr1", IRanges(c(50, 100), c(80, 200)),
"-"))
x <- GenomicRanges::GRanges(
seqnames = "chr1",
ranges = IRanges::IRanges(c(100, 180), c(200, 300)),
strand = "-")
metaWindow(x, windows, withFrames = FALSE)
model.matrix,experiment-method
Get experiment design model matrix
Description
The function extends stats::model.matrix.
Usage
## S4 method for signature 'experiment'
model.matrix(object, design_formula = design(object, as.formula = TRUE))
Arguments
object an ORFik experiment
design_formula the experiment design, as formula, subset columns, to change the model.matrix,
default: design(object, as.formula = TRUE)
Value
a matrix with design and level attributes
Examples
df <- ORFik.template.experiment()
model.matrix(df)
242 name,experiment-method
name Get name of ORFik experiment
Description
Get name of ORFik experiment
Usage
name(x)
Arguments
x an ORFik experiment
Value
character, name of experiment
name,experiment-method
Get name of ORFik experiment
Description
Get name of ORFik experiment
Usage
## S4 method for signature 'experiment'
name(x)
Arguments
x an ORFik experiment
Value
character, name of experiment
nrow,experiment-method 243
nrow,experiment-method
Internal nrow function for ORFik experiment Number of runs in exper-
iment
Description
Internal nrow function for ORFik experiment Number of runs in experiment
Usage
## S4 method for signature 'experiment'
nrow(x)
Arguments
x an ORFik experiment
Value
number of rows in experiment (integer)
numCodons Get number of codons
Description
Length of object / 3. Choose either only whole codons, or with stubs. ORF stubs are not relevant,
since there are no correctly defined ORFs that are 17 bases long etc.
Usage
numCodons(grl, as.integer = TRUE, keep.names = FALSE)
Arguments
grl a GRangesList object
as.integer a logical (TRUE), remove stub codons
keep.names a logical (FALSE)
Value
an integer vector
244 ofst_merge
numExonsPerGroup Get list of the number of exons per group
Description
Can also be used generaly to get number of GRanges object per GRangesList group
Usage
numExonsPerGroup(grl, keep.names = TRUE)
Arguments
grl a GRangesList
keep.names a logical, keep names or not, default: (TRUE)
Value
an integer vector of counts
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
numExonsPerGroup(grl)
ofst_merge Merge multiple ofst file
Description
Collapses and sums the score column of each ofst file It is required that each file is of same ofst
type. That is if one file has cigar information, all must have it.
Usage
ofst_merge(
file_paths,
lib_names = sub(pattern = "\\.ofst$", replacement = "", basename(file_paths)),
keep_all_scores = TRUE,
keepCigar = TRUE,
sort = TRUE
)
optimizedTranscriptLengths 245
Arguments
file_paths Full path to .ofst files wanted to merge
lib_names the name to give the resulting score columns
keep_all_scores
logical, default TRUE, keep all library scores in the merged file. These score
columns are named the libraries full name from bamVarName(df).
keepCigar logical, default TRUE. If CIGAR is defined, keep column. Setting to FALSE
compresses the file much more usually.
sort logical, default TRUE. Sort the ranges. Will make the file smaller and faster to
load, but some additional merging time is added.
Value
a data.table of merged result, it is merged on all columns except "score". The returned file will
contain the scores of each file + the aggregate sum score.
optimizedTranscriptLengths
Load length and names of all transcripts
Description
A speedup wrapper around transcriptLengths, default load time of lengths is ~ 15 seconds, if
ORFik fst optimized lengths object has been made, load that file instead: load time reduced to ~ 0.1
second.
Usage
optimizedTranscriptLengths(
txdb,
with.utr5_len = TRUE,
with.utr3_len = TRUE,
create.fst.version = FALSE
)
Arguments
txdb a TxDb file or a path to one of: (.gtf ,.gff, .gff2, .gff2, .db or .sqlite), if it is a
GRangesList, it will return it self.
with.utr5_len logical TRUE, include length of 5’ UTRs, ignored if .fst exists
with.utr3_len logical TRUE, include length of 3’ UTRs, ignored if .fst exists
246 optimized_txdb_path
create.fst.version
logical, FALSE. If TRUE, creates a .fst version of the transcript length table (if
it not already exists), reducing load time from ~ 15 seconds to ~ 0.01 second
next time you run filterTranscripts with this txdb object. The file is stored in the
same folder as the genome this txdb is created from, with the name:
paste0(ORFik:::remove.file_ext(metadata(txdb)[3,2]), "_", gsub(" \(.*|
|:", "", metadata(txdb)[metadata(txdb)[,1] == "Creation time",2]), "_txLengths.fst")
Some error checks are done to see this is a valid location, if the txdb data source
is a repository like UCSC and not a local folder, it will not be made.
Value
a data.table of loaded lengths 8 columns, 1 row per transcript isoform.
Examples
dt <- optimizedTranscriptLengths(ORFik.template.experiment())
dt
dt[cds_len > 0,] # All mRNA
optimized_txdb_path Get path for optimization files for txdb
Description
Get path for optimization files for txdb
Usage
optimized_txdb_path(txdb, create.dir = FALSE, stop.error = TRUE)
Arguments
txdb a loaded TxDb object
create.dir logical FALSE, if TRUE create the optimization directory, this should only be
called first time used.
stop.error logical TRUE
Value
a character file path, returns NULL if not valid and stop.error is FALSE.
optimizeReads 247
optimizeReads Find optimized subset of valid reads
Description
Keep only the ones that overlap within the grl ranges. Also sort them in the end
Usage
optimizeReads(grl, reads)
Arguments
grl a GRangesList or GRanges object
reads a GRanges, GAlignment or GAlignmentPairs object
Value
the reads as GRanges, GAlignment or GAlignmentPairs
See Also
Other utils: bedToGR(), convertToOneBasedRanges(), export.bed12(), export.bigWig(), export.fstwig(),
export.wiggle(), fimport(), findFa(), fread.bed(), readBam(), readBigWig(), readWig()
orfFrameDistributions Find shifted Ribo-seq frame distributions
Description
Per library: get coverage over CDS per frame per readlength Return as data.datable with information
and best frame found. Can be used to automize re-shifting of read lengths (find read lengths where
frame 0 is not the best frame over the entire cds)
Usage
orfFrameDistributions(
df,
type = "pshifted",
weight = "score",
BPPARAM = BiocParallel::bpparam()
)
248 orfID
Arguments
df an ORFik experiment
type type of library loaded, default pshifted, warning if not pshifted might crash if
too many read lengths!
weight which column in reads describe duplicates, default "score".
BPPARAM how many cores/threads to use? default: bpparam(). To see number of threads
used, do bpparam()$workers. You can also add a time remaining bar, for a
more detailed pipeline.
Value
data.table with columns: fraction (library) frame (0, 1, 2) score (coverage) length (read length)
percent (coverage percentage of library) percent_length (coverage percentage of library and length)
best_frame (TRUE/FALSE, is this the best frame per length)
Examples
df <- ORFik.template.experiment()[3,]
dt <- orfFrameDistributions(df, BPPARAM = BiocParallel::SerialParam())
## Check that frame 0 is best frame for all
all(dt[frame == 0,]$best_frame)
orfID Get id’s for each orf
Description
These id’s can be uniqued by isoform etc, this is not supported by GenomicRanges.
Usage
orfID(grl, with.tx = FALSE)
Arguments
grl a GRangesList
with.tx a boolean, include transcript names, if you want unique orfs, so that they dont
have duplicates from different isoforms, set it to FALSE.
Value
a character vector of ids, 1 per orf
See Also
Other ORFHelpers: defineTrailer(), longestORFs(), mapToGRanges(), startCodons(), startSites(),
stopCodons(), stopSites(), txNames(), uniqueGroups(), uniqueOrder()
ORFik.template.experiment 249
ORFik.template.experiment
An ORFik experiment to see how it looks
Description
Toy-data created to resemble human genes:
Number of genes: 6
Ribo-seq: 2 libraries RNA-seq: 2 libraries CAGE: 1 library PAS (poly-A): 1 library
Usage
ORFik.template.experiment(as.temp = FALSE)
Arguments
as.temp logical, default FALSE, load as ORFik experiment. If TRUE, loads as data.frame
template of the experiment.
Value
an ORFik experiment
See Also
Other ORFik_experiment: ORFik.template.experiment.zf(), bamVarName(), create.experiment(),
experiment-class, filepath(), libraryTypes(), organism,experiment-method, outputLibs(),
read.experiment(), save.experiment(), validateExperiments()
Examples
ORFik.template.experiment()
ORFik.template.experiment.zf
An ORFik experiment to see how it looks
Description
Toy-data created to resemble Zebrafish genes:
Number of genes: 150
Ribo-seq: 1 library
Usage
ORFik.template.experiment.zf(as.temp = FALSE)
250 ORFikQC
Arguments
as.temp logical, default FALSE, load as ORFik experiment. If TRUE, loads as data.frame
template of the experiment.
Value
an ORFik experiment
See Also
Other ORFik_experiment: ORFik.template.experiment(), bamVarName(), create.experiment(),
experiment-class, filepath(), libraryTypes(), organism,experiment-method, outputLibs(),
read.experiment(), save.experiment(), validateExperiments()
Examples
ORFik.template.experiment.zf()
ORFikQC A post Alignment quality control of reads
Description
The ORFik QC uses the aligned files (usually bam files), fastp and STAR log files combined with
annotation to create relevant statistics.
This report consists of several steps:
1. Convert bam file / Input files to ".ofst" format, if not already done. This format is around 400x
faster to use in R than the bam format. Files are also outputted to R environment specified by
envExp(df)
2. From this report you will get a summary csv table, with distribution of aligned reads and overlap
counts over transcript regions like: leader, cds, trailer, lincRNAs, tRNAs, rRNAs, snoRNAs etc. It
will be called STATS.csv. And can be imported with QCstats function.
3. It will also make correlation plots and meta coverage plots, so you get a good understanding of
how good the quality of your NGS data production + aligner step were.
4. Count tables are produced, similar to HTseq count tables. Over mrna, leader, cds and trailer sep-
arately. This tables are stored as SummarizedExperiment, for easy loading into DEseq, conversion
to normalized fpkm values, or collapsing replicates in an experiment. And can be imported with
countTable function.
Everything will be outputed in the directory of your NGS data, inside the folder ./QC_STATS/,
relative to data location in ’df’. You can specify new out location with out.dir if you want.
To make a ORFik experiment, see ?ORFik::experiment
To see some normal mrna coverage profiles of different RNA-seq protocols: https://www.ncbi.
nlm.nih.gov/pmc/articles/PMC4310221/figure/F6/
ORFikQC 251
Usage
ORFikQC(
df,
out.dir = resFolder(df),
plot.ext = ".pdf",
create.ofst = TRUE,
complex.correlation.plots = TRUE,
BPPARAM = bpparam()
)
Arguments
df an ORFik experiment
out.dir optional output directory, default: resFolder(df). Will make a folder within
this called "QC_STATS" with all results in this directory. Warning: If you assign
not default path, you will have a hazzle to load files later. Much easier to load
count tables, statistics, ++ later with default. Update resFolder of df instead if
needed.
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg". Note that in pdf format
the complex correlation plots become very slow to load!
create.ofst logical, default TRUE. Create ".ofst" files from the input libraries, ofst is much
faster to load in R, for later use. Stored in ./ofst/ folder relative to experiment
main folder.
complex.correlation.plots
logical, default TRUE. Add in addition to simple correlation plot two compu-
tationally heavy dots + correlation plots. Useful for deeper analysis, but takes
longer time to run, especially on low-quality gpu computers. Set to FALSE to
skip these.
BPPARAM how many cores/threads to use? default: bpparam(). To see number of threads
used, do bpparam()$workers. You can also add a time remaining bar, for a
more detailed pipeline.
Value
invisible(NULL) (objects are stored to disc)
See Also
Other QC report: QCplots(), QCstats()
Examples
# Load an experiment
df <- ORFik.template.experiment()
# Run QC
# QCreport(df)
252 orfScore
orfScore Get ORFscore for a GRangesList of ORFs
Description
ORFscore tries to check whether the first frame of the 3 possible frames in an ORF has more reads
than second and third frame. IMPORTANT: Only use p-shifted libraries, see (detectRibosomeShifts).
Else this score makes no sense.
Usage
orfScore(
grl,
RFP,
is.sorted = FALSE,
weight = "score",
overlapGrl = NULL,
coverage = NULL,
stop3 = TRUE
)
Arguments
grl a GRangesList of 5’ utrs, CDS, transcripts, etc.
RFP ribosomal footprints, given as GAlignments or GRanges object, must be already
shifted and resized to the p-site. Requires a $size column with original read
lengths.
is.sorted logical (FALSE), is grl sorted. That is + strand groups in increasing ranges
(1,2,3), and - strand groups in decreasing ranges (3,2,1)
weight (default: ’score’), if defined a character name of valid meta column in subject.
GRanges("chr1", 1, "+", score = 5), would mean score column tells that this
alignment region was found 5 times. ORFik ofst, bedoc and .bedo files contains
a score column like this. As do CAGEr CAGE files and many other package
formats. You can also assign a score column manually.
overlapGrl an integer, (default: NULL), if defined must be countOverlaps(grl, RFP), added
for speed if you already have it
coverage a data.table from coveragePerTiling of length same as ’grl’ argument. Save time
if you have already computed it.
stop3 logical, default TRUE. Stop if any input is of width < 3.
Details
Pseudocode: assume rff - is reads fraction in specific frame
ORFScore = log(rff1 + rff2 + rff3)
orfScore 253
If rff2 or rff3 is bigger than rff1, negate the resulting value.
ORFScore[rff1Smaller] <- ORFScore[rff1Smaller] * -1
As result there is one value per ORF: - Positive values say that the first frame have the most reads,
- zero values means it is uniform: (ORFscore between -2.5 and 2.5 can be considered close to
uniform), - negative values say that the first frame does not have the most reads. NOTE non-
pshifted reads: If reads are not of width 1, then a read from 1-4 on range of 1-4, will get scores
frame1 = 2, frame2 = 1, frame3 = 1. What could be logical is that only the 5’ end is important, so
that only frame1 = 1, to get this, you first resize reads to 5’end only.
General NOTES: 1. p shifting is not exact, so some functional ORFs will get a bad ORF score.
2. If a score column is defined, it will use it as weights, set to weight = 1L if you don’t have weight,
and score column is something else. 3. If needed a test for significance and critical values, use
chi-squared. There are 3 degrees of freedom (3 frames), so critical 0.05 (3-1 degrees of freedm =
2), value is: log2(6) = 2.58 see getWeights
Value
a data.table with 4 columns, the orfscore (ORFScores) and score of each of the 3 tiles (frame_zero_RP,
frame_one_RP, frame_two_RP)
References
doi: 10.1002/embj.201488411
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss() , fpkm(), fpkm_calc(), fractionLength(),
initiationScore(), insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(),
rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
ORF <- GRanges(seqnames = "1",
ranges = IRanges(start = c(1, 10, 20), end = c(5, 15, 25)),
strand = "+")
names(ORF) <- c("tx1", "tx1", "tx1")
grl <- GRangesList(tx1_1 = ORF)
RFP <- GRanges("1", IRanges(25, 25), "+") # 1 width position based
score(RFP) <- 28 # original width
orfScore(grl, RFP) # negative because more hits on frames 1,2 than 0.
# example with positive result, more hits on frame 0 (in frame of ORF)
RFP <- GRanges("1", IRanges(c(1, 1, 1, 25), width = 1), "+")
score(RFP) <- c(28, 29, 31, 28) # original width
orfScore(grl, RFP)
254 organism,experiment-method
organism,experiment-method
Get ORFik experiment organism
Description
If not defined directly, checks the txdb / gtf organism information, if existing.
Usage
## S4 method for signature 'experiment'
organism(object)
Arguments
object an ORFik experiment
Value
character, name of organism
See Also
Other ORFik_experiment: ORFik.template.experiment(), ORFik.template.experiment.zf(),
bamVarName(), create.experiment(), experiment-class, filepath(), libraryTypes(), outputLibs(),
read.experiment(), save.experiment(), validateExperiments()
Examples
# if you have set organism in txdb of ORFik experiment:
df <- ORFik.template.experiment()
organism(df)
#' If you have not set the organism you can do:
#gtf <- "pat/to/gff_or_gff"
#txdb_path <- paste0(gtf, ".db") # This file is created in next step
#txdb <- makeTxdbFromGenome(gtf, genome, organism = "Homo sapiens",
# optimize = TRUE, return = TRUE)
# then use this txdb in you ORFik experiment and load:
# create.experiment(exper = "new_experiment",
# txdb = txdb_path) ...
# organism(read.experiment("new-experiment))
outputLibs 255
outputLibs Output NGS libraries to R as variables
Description
By default loads the original files of the experiment into the global environment, named by the rows
of the experiment required to make all libraries have unique names.
Uses multiple cores to load, defined by multicoreParam
Usage
outputLibs(
df,
type = "default",
paths = filepath(df, type),
param = NULL,
strandMode = 0,
naming = "minimum",
output.mode = "envir",
chrStyle = NULL,
envir = envExp(df),
verbose = TRUE,
force = TRUE,
BPPARAM = bpparam()
)
Arguments
df an ORFik experiment
type a character(default: "default"), load files in experiment or some precomputed
variant, like "ofst" or "pshifted". These are made with ORFik:::convertLibs(),
shiftFootprintsByExperiment(), etc. Can also be custom user made folders in-
side the experiments bam folder. It acts in a recursive manner with priority: If
you state "pshifted", but it does not exist, it checks "ofst". If no .ofst files, it uses
"default", which always must exists.
Presets are (folder is relative to default lib folder, some types fall back to other
formats if folder does not exist):
- "default": load the original files for experiment, usually bam.
- "ofst": loads ofst files from the ofst folder, relative to lib folder (falls back to
default)
- "pshifted": loads ofst, wig or bigwig from pshifted folder (falls back to ofst,
then default)
- "cov": Load covRle objects from cov_RLE folder (fail if not found)
- "covl": Load covRleList objects, from cov_RLE_List folder (fail if not found)
- "bed": Load bed files, from bed folder (falls back to default)
- Other formats must be loaded directly with fimport
256 outputLibs
paths character vector, the filpaths to use, default filepath(df, type). Change type
argument if not correct. If that is not enough, then you can also update this
argument. But be careful about using this directly.
param NULL or a ScanBamParam object. Like for scanBam, this influences what fields
and which records are imported. However, note that the fields specified thru this
ScanBamParam object will be loaded in addition to any field required for gen-
erating the returned object (GAlignments, GAlignmentPairs, or GappedReads
object), but only the fields requested by the user will actually be kept as meta-
data columns of the object.
By default (i.e. param=NULL or param=ScanBamParam()), no additional field is
loaded. The flag used is scanBamFlag(isUnmappedQuery=FALSE) for readGAlignments,
readGAlignmentsList, and readGappedReads. (i.e. only records correspond-
ing to mapped reads are loaded), and scanBamFlag(isUnmappedQuery=FALSE,
isPaired=TRUE, hasUnmappedMate=FALSE) for readGAlignmentPairs (i.e. only
records corresponding to paired-end reads with both ends mapped are loaded).
strandMode numeric, default 0. Only used for paired end bam files. One of (0: strand
= *, 1: first read of pair is +, 2: first read of pair is -). See ?strandMode.
Note: Sets default to 0 instead of 1, as readGAlignmentPairs uses 1. This is
to guarantee hits, but will also make mismatches of overlapping transcripts in
opposite directions.
naming a character (default: "minimum"). Name files as minimum information needed
to make all files unique. Set to "full" to get full names. Set to "fullexp", to get
full name with experiment name as prefix, the last one guarantees uniqueness.
output.mode character, default "envir". Output libraries to environment. Alternative: "list",
return as list. "envirlist", output to envir and return as list. If output is list for-
mat, the list elements are named from: bamVarName(df.rfp) (Full or minimum
naming based on ’naming’ argument)
chrStyle a GRanges object, TxDb, FaFile, , a seqlevelsStyle or Seqinfo. (Default:
NULL) to get seqlevelsStyle from. In addition if it is a Seqinfo object, seqinfo
will be updated. Example of seqlevelsStyle update: Is chromosome 1 called
chr1 or 1, is mitocondrial chromosome called MT or chrM etc. Will use 1st
seqlevel-style if more are present. Like: c("NCBI", "UCSC") -> pick "NCBI"
envir environment to save to, default envExp(df), which defaults to .GlobalEnv, but
can be set with envExp(df) <- new.env() etc.
verbose logical, default TRUE, message about library output status.
force logical, default TRUE If TRUE, reload files even if matching named variables
are found in environment used by experiment (see envExp) A simple way to
make sure correct libraries are always loaded. FALSE is faster if data is loaded
correctly already.
BPPARAM how many cores/threads to use? default: bpparam(). To see number of threads
used, do bpparam()$workers. You can also add a time remaining bar, for a
more detailed pipeline.
Value
NULL (libraries set by envir assignment), unless output.mode is "list" or "envirlist": Then you get
a list of the libraries.
pasteDir 257
See Also
Other ORFik_experiment: ORFik.template.experiment(), ORFik.template.experiment.zf(),
bamVarName(), create.experiment(), experiment-class, filepath(), libraryTypes(), organism,experiment-method,
read.experiment(), save.experiment(), validateExperiments()
Examples
## Load a template ORFik experiment
df <- ORFik.template.experiment()
## Default library type load, usually bam files
# outputLibs(df, type = "default")
## .ofst file load, if ofst files does not exists
## it will load default
# outputLibs(df, type = "ofst")
## .wig file load, if wiggle files does not exists
## it will load default
# outputLibs(df, type = "wig")
## Load as list
outputLibs(df, output.mode = "list")
## Load libs to new environment (called ORFik in Global)
# outputLibs(df, envir = assign(name(df), new.env(parent = .GlobalEnv)))
## Load to hidden environment given by experiment
# envExp(df) <- new.env()
# outputLibs(df)
pasteDir A paste function for directories Makes sure slashes are corrected, and
not doubled.
Description
A paste function for directories Makes sure slashes are corrected, and not doubled.
Usage
pasteDir(...)
Arguments
... any amount of arguments that are possible to convert to characters
Value
the pasted string
258 pcaExperiment
pcaExperiment Simple PCA analysis
Description
Detect outlier libraries with PCA analysis. Will output PCA plot of PCA component 1 (x-axis)
vs PCA component 2 (y-axis) for each library (colored by library), shape by replicate. Will be
extended to allow batch correction in the future.
Usage
pcaExperiment(
df,
output.dir = NULL,
table = countTable(df, "cds", type = "fpkm"),
title = "PCA analysis by CDS fpkm",
subtitle = paste("Numer of genes/regions:", nrow(table)),
plot.ext = ".pdf",
return.data = FALSE,
color.by.group = TRUE
)
Arguments
df an ORFik experiment
output.dir default NULL, else character path to directory. File saved as "PCAplot_(experiment
name)(plot.ext)"
table data.table, default countTable(df, "cds", type = "fpkm"), a data.table of counts
per column (default normalized fpkm values).
title character, default "CDS fpkm".
subtitle character, default: paste("Numer of genes:", nrow(table))
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg". Note that in pdf format
the complex correlation plots become very slow to load!
return.data logical, default FALSE. Return data instead of plot
color.by.group logical, default TRUE. Colors in PCA plot represent unique library groups, if
FALSE. Color each sample in seperate color (harder to distinguish for > 10
samples)
Value
ggplot or invisible(NULL) if output.dir is defined or < 3 samples. Returns data.table with PCA
analysis if return.data is TRUE.
percentage_to_ratio 259
Examples
df <- ORFik.template.experiment()
# Select only Ribo-seq and RNA-seq
pcaExperiment(df[df$libtype %in% c("RNA", "RFP"),])
percentage_to_ratio Convert percentage to ratio of 1
Description
50 -> 0.5 etc, if length cds > minimum.cds
Usage
percentage_to_ratio(top_tx, cds, minimum.cds = 1000)
Arguments
top_tx numeric
cds GRangesList object
minimum.cds numeric, default 1000
Value
numeric, as ratio of 1
plotHelper Helper function for coverage plots
Description
Should only be used internally
Usage
plotHelper(
coverage,
df,
outdir,
scores,
returnCoverage = FALSE,
title = "coverage metaplot",
plot.ext = ".pdf",
colors = c("skyblue4", "orange"),
plotFunction = "windowCoveragePlot"
)
260 pmapFromTranscriptF
Arguments
coverage a data.table containing at least columns (count/score, position), it is possible to
have additionals: (genes, fraction, feature)
df an ORFik experiment
outdir directory to save to (default: NULL, no saving)
scores scoring function (default: c("sum", "transcriptNormalized")), see ?coverageScor-
ings for possible scores.
returnCoverage (defualt: FALSE), return the ggplot object (TRUE) or NULL (FALSE).
title Title to give plot
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg".
colors Which colors to use, default auto color from function experiment.colors, new
color per library type. Else assign colors yourself.
plotFunction Which plot function, default: windowCoveragePlot
Value
NULL (or ggplot object if returnCoverage is TRUE)
pmapFromTranscriptF Faster pmapFromTranscript
Description
Map range coordinates between features in the transcriptome and genome (reference) space. The
length of x must be the same as length of transcripts. Only exception is if x have integer names like
(1, 3, 3, 5), so that x[1] maps to 1, x[2] maps to transcript 3 etc.
Usage
pmapFromTranscriptF(x, transcripts, removeEmpty = FALSE)
Arguments
x IRangesList/IRanges/GRanges to map to genomic coordinates
transcripts a GRangesList to map against (the genomic coordinates)
removeEmpty a logical, remove non hit exons, else they are set to 0. That is all exons in the
reference that the transcript coordinates do not span.
Details
This version tries to fix the short commings of GenomicFeature’s version. Much faster and uses
less memory. Implemented as dynamic program optimized c++ code.
pmapToTranscriptF 261
Value
a GRangesList of mapped reads, names from ranges are kept.
Examples
ranges <- IRanges(start = c( 5, 6), end = c(10, 10))
seqnames = rep("chr1", 2)
strands = rep("-", 2)
grl <- split(GRanges(seqnames, IRanges(c(85, 70), c(89, 82)), strands),
c(1, 1))
ranges <- split(ranges, c(1,1)) # both should be mapped to transcript 1
pmapFromTranscriptF(ranges, grl, TRUE)
pmapToTranscriptF Faster pmapToTranscript
Description
Map range coordinates between features in the transcriptome and genome (reference) space. The
length of x must be the same as length of transcripts. Only exception is if x have integer names like
(1, 3, 3, 5), so that x[1] maps to 1, x[2] maps to transcript 3 etc.
Usage
pmapToTranscriptF(
x,
transcripts,
ignore.strand = FALSE,
x.is.sorted = TRUE,
tx.is.sorted = TRUE
)
Arguments
x GRangesList/GRanges/IRangesList/IRanges to map to transcriptomic coordi-
nates
transcripts a GRangesList/GRanges/IRangesList/IRanges to map against (the genomic co-
ordinates). Must be of lower abstraction level than x. So if x is GRanges, tran-
scripts can not be IRanges etc.
ignore.strand When ignore.strand is TRUE, strand is ignored in overlaps operations (i.e., all
strands are considered "+") and the strand in the output is ’*’.
When ignore.strand is FALSE (default) strand in the output is taken from the
transcripts argument. When transcripts is a GRangesList, all inner list elements
of a common list element must have the same strand or an error is thrown.
Mapped position is computed by counting from the transcription start site (TSS)
and is not affected by the value of ignore.strand.
262 pmapToTranscriptF
x.is.sorted if x is a GRangesList object, are "-" strand groups pre-sorted in decreasing order
within group, default: TRUE
tx.is.sorted if transcripts is a GRangesList object, are "-" strand groups pre-sorted in de-
creasing order within group, default: TRUE
Details
This version tries to fix the shortcommings of GenomicFeature’s version. Much faster and uses less
memory. Implemented as dynamic program optimized c++ code.
Value
object of same class as input x, names from ranges are kept.
Examples
library(GenomicFeatures)
# Need 2 ranges object, the target region and whole transcript
# x is target region
x <- GRanges("chr1", IRanges(start = c(26, 29), end = c(27, 29)), "+")
names(x) <- rep("tx1_ORF1", length(x))
x <- groupGRangesBy(x)
# tx is the whole region
tx_gr <- GRanges("chr1", IRanges(c(5, 29), c(27, 30)), "+")
names(tx_gr) <- rep("tx1", length(tx_gr))
tx <- groupGRangesBy(tx_gr)
pmapToTranscriptF(x, tx)
pmapToTranscripts(x, tx)
# Reuse names for matching
x <- GRanges("chr1", IRanges(start = c(26, 29, 5), end = c(27, 29, 18)), "+")
names(x) <- c(rep("tx1_1", 2), "tx1_2")
x <- groupGRangesBy(x)
tx1_2 <- GRanges("chr1", IRanges(c(4, 28), c(26, 31)), "+")
names(tx1_2) <- rep("tx1", 2)
tx <- c(tx, groupGRangesBy(tx1_2))
a <- pmapToTranscriptF(x, tx[txNames(x)])
b <- pmapToTranscripts(x, tx[txNames(x)])
identical(a, b)
seqinfo(a)
# A note here, a & b only have 1 seqlength, even though the 2 "tx1"
# are different in size. This is an artifact of using duplicated names.
## Also look at the asTx for a similar useful function.
prettyScoring 263
prettyScoring Prettify scoring name
Description
Prettify scoring name
Usage
prettyScoring(scoring)
Arguments
scoring a character (the scoring)
Value
a new scoring name or the same if pretty
pseudo.transform Transform object
Description
Similar to normal transform like log2 or log10. But keep 0 values as 0, to avoid Inf values and
negtive values are made as -scale(abs(x)), to avoid NaN values.
Usage
pseudo.transform(x, scale = log2, by.reference = FALSE)
Arguments
x a numeric vector or data.frame/data.table of numeric columns
scale a function, default log2, which function to transform with.
by.reference logical, FALSE. if TRUE, update object by reference if it is data.table.
Value
same object class as x, with transformed values
264 pSitePlot
pSitePlot Plot area around TIS as histogram
Description
Usefull to validate p-shifting is correct Can be used for any coverage of region around a point, like
TIS, TSS, stop site etc.
Usage
pSitePlot(
hitMap,
length = unique(hitMap$fraction),
region = "start",
output = NULL,
type = "canonical CDS",
scoring = "Averaged counts",
forHeatmap = FALSE,
title = "auto",
facet = FALSE,
frameSum = FALSE
)
Arguments
hitMap a data.frame/data.table, given from metaWindow (must have columns: position,
(score or count) and frame)
length an integer (29), which read length is this for?
region a character (start), either "start or "stop"
output character (NULL), if set, saves the plot as pdf or png to path given. If no format
is given, is save as pdf.
type character (canonical CDS), type for plot
scoring character, default: (Averaged counts), which scoring did you use ? see ?cover-
ageScorings for info and more alternatives.
forHeatmap a logical (FALSE), should the plot be part of a heatmap? It will scale it differ-
ently. Removing title, x and y labels, and truncate spaces between bars.
title character, title of plot. Default "auto", will make it: paste("Length", length,
"over", region, "of", type). Else set your own (set to NULL to remove all to-
gether).
facet logical, default FALSE. If you input multiple read lengths, specified by fraction
column of hitMap, it will split the plots for each read length, putting them under
each other. Ignored if forHeatmap is TRUE.
frameSum logical default FALSE. If TRUE, add an addition plot to the right, sum per frame
over all positions per length.
QCfolder 265
Details
The region is represented as a histogram with different colors for the 3 frames. To make it easy
to see patterns in the reads. Remember if you want to change anything like colors, just return the
ggplot object, and reassign like: obj + scale_color_brewer() etc.
Value
a ggplot object of the coverage plot, NULL if output is set, then the plot will only be saved to
location.
See Also
Other coveragePlot: coverageHeatMap(), savePlot(), windowCoveragePlot()
Examples
# An ORF
grl <- GRangesList(tx1 = GRanges("1", IRanges(1, 6), "+"))
# Ribo-seq reads
range <- IRanges(c(rep(1, 3), 2, 3, rep(4, 2), 5, 6), width = 1 )
reads <- GRanges("1", range, "+")
coverage <- coveragePerTiling(grl, reads, TRUE, as.data.table = TRUE,
withFrames = TRUE)
pSitePlot(coverage)
# See vignette for more examples
QCfolder Get ORFik experiment QC folder path
Description
Get ORFik experiment QC folder path
Usage
QCfolder(x)
Arguments
x an ORFik experiment
Value
a character path
266 QCplots
QCfolder,experiment-method
Get ORFik experiment QC folder path
Description
Get ORFik experiment QC folder path
Usage
## S4 method for signature 'experiment'
QCfolder(x)
Arguments
x an ORFik experiment
Value
a character path
QCplots Correlation and coverage plots for ORFikQC
Description
Correlation plots default to mRNA covering reads. Meta plots defaults to leader, cds, trailer.
Output will be stored in same folder as the libraries in df.
Correlation plots will be fpkm correlation and log2(fpkm + 1) correlation between samples.
Usage
QCplots(
df,
region = "mrna",
stats_folder = QCfolder(df),
plot.ext = ".pdf",
complex.correlation.plots = TRUE,
BPPARAM
)
QCreport 267
Arguments
df an ORFik experiment
region a character (default: mrna), make raw count matrices of whole mrnas or one of
(leaders, cds, trailers)
stats_folder directory to save, default: QCfolder(df)
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg". Note that in pdf format
the complex correlation plots become very slow to load!
complex.correlation.plots
logical, default TRUE. Add in addition to simple correlation plot two compu-
tationally heavy dots + correlation plots. Useful for deeper analysis, but takes
longer time to run, especially on low-quality gpu computers. Set to FALSE to
skip these.
BPPARAM how many cores/threads to use? default: bpparam(). To see number of threads
used, do bpparam()$workers. You can also add a time remaining bar, for a
more detailed pipeline.
Details
Is part of QCreport
Value
invisible(NULL) (objects stored to disc)
See Also
Other QC report: QCreport(), QCstats()
QCreport A post Alignment quality control of reads
Description
The ORFik QC uses the aligned files (usually bam files), fastp and STAR log files combined with
annotation to create relevant statistics.
This report consists of several steps:
1. Convert bam file / Input files to ".ofst" format, if not already done. This format is around 400x
faster to use in R than the bam format. Files are also outputted to R environment specified by
envExp(df)
2. From this report you will get a summary csv table, with distribution of aligned reads and overlap
counts over transcript regions like: leader, cds, trailer, lincRNAs, tRNAs, rRNAs, snoRNAs etc. It
will be called STATS.csv. And can be imported with QCstats function.
3. It will also make correlation plots and meta coverage plots, so you get a good understanding of
how good the quality of your NGS data production + aligner step were.
268 QCreport
4. Count tables are produced, similar to HTseq count tables. Over mrna, leader, cds and trailer sep-
arately. This tables are stored as SummarizedExperiment, for easy loading into DEseq, conversion
to normalized fpkm values, or collapsing replicates in an experiment. And can be imported with
countTable function.
Everything will be outputed in the directory of your NGS data, inside the folder ./QC_STATS/,
relative to data location in ’df’. You can specify new out location with out.dir if you want.
To make a ORFik experiment, see ?ORFik::experiment
To see some normal mrna coverage profiles of different RNA-seq protocols: https://www.ncbi.
nlm.nih.gov/pmc/articles/PMC4310221/figure/F6/
Usage
QCreport(
df,
out.dir = resFolder(df),
plot.ext = ".pdf",
create.ofst = TRUE,
complex.correlation.plots = TRUE,
BPPARAM = bpparam()
)
Arguments
df an ORFik experiment
out.dir optional output directory, default: resFolder(df). Will make a folder within
this called "QC_STATS" with all results in this directory. Warning: If you assign
not default path, you will have a hazzle to load files later. Much easier to load
count tables, statistics, ++ later with default. Update resFolder of df instead if
needed.
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg". Note that in pdf format
the complex correlation plots become very slow to load!
create.ofst logical, default TRUE. Create ".ofst" files from the input libraries, ofst is much
faster to load in R, for later use. Stored in ./ofst/ folder relative to experiment
main folder.
complex.correlation.plots
logical, default TRUE. Add in addition to simple correlation plot two compu-
tationally heavy dots + correlation plots. Useful for deeper analysis, but takes
longer time to run, especially on low-quality gpu computers. Set to FALSE to
skip these.
BPPARAM how many cores/threads to use? default: bpparam(). To see number of threads
used, do bpparam()$workers. You can also add a time remaining bar, for a
more detailed pipeline.
Value
invisible(NULL) (objects are stored to disc)
QCstats 269
See Also
Other QC report: QCplots(), QCstats()
Examples
# Load an experiment
df <- ORFik.template.experiment()
# Run QC
# QCreport(df)
QCstats Load ORFik QC Statistics report
Description
Loads the pre / post alignment statistcs made in ORFik.
Usage
QCstats(df, path = file.path(QCfolder(df), "STATS.csv"))
Arguments
df an ORFik experiment
path path to QC statistics report, default: file.path(dirname(df$filepath[1]), "/QC_STATS/STATS.csv")
Details
The ORFik QC uses the aligned files (usually bam files), fastp and STAR log files combined with
annotation to create relevant statistics.
Value
data.table of QC report or NULL if not exists
See Also
Other QC report: QCplots(), QCreport()
Examples
df <- ORFik.template.experiment()
## First make QC report
# QCreport(df)
# stats <- QCstats(df)
270 QC_count_tables
QCstats.plot Make plot of ORFik QCreport
Description
From post-alignment QC relative to annotation, make a plot for all samples. Will contain among
others read lengths, reads overlapping leaders, cds, trailers, mRNA / rRNA etc.
Usage
QCstats.plot(stats, output.dir = NULL, plot.ext = ".pdf", as_gg_list = FALSE)
Arguments
stats the experiment object or path to custom ORFik QC folder where a file called
"STATS.csv" is located.
output.dir NULL or character path, default: NULL, plot not saved to disc. If defined saves
plot to that directory with the name "/STATS_plot.pdf".
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg".
as_gg_list logical, default FALSE. Return as a list of ggplot objects instead of as a grob.
Gives you the ability to modify plots more directly.
Value
the plot object, a grob of ggplot objects of the the statistics data
Examples
df <- ORFik.template.experiment()[3,]
## First make QC report
# QCreport(df)
## Now you can get plot
# QCstats.plot(df)
QC_count_tables Create count table info for QC report
Description
The better the annotation / gtf used, the more results you get.
Usage
QC_count_tables(df, out.dir, type = "ofst", BPPARAM = bpparam())
r 271
Arguments
df an ORFik experiment
out.dir optional output directory, default: resFolder(df). Will make a folder within
this called "QC_STATS" with all results in this directory. Warning: If you assign
not default path, you will have a hazzle to load files later. Much easier to load
count tables, statistics, ++ later with default. Update resFolder of df instead if
needed.
type a character(default: "default"), load files in experiment or some precomputed
variant, like "ofst" or "pshifted". These are made with ORFik:::convertLibs(),
shiftFootprintsByExperiment(), etc. Can also be custom user made folders in-
side the experiments bam folder. It acts in a recursive manner with priority: If
you state "pshifted", but it does not exist, it checks "ofst". If no .ofst files, it uses
"default", which always must exists.
Presets are (folder is relative to default lib folder, some types fall back to other
formats if folder does not exist):
- "default": load the original files for experiment, usually bam.
- "ofst": loads ofst files from the ofst folder, relative to lib folder (falls back to
default)
- "pshifted": loads ofst, wig or bigwig from pshifted folder (falls back to ofst,
then default)
- "cov": Load covRle objects from cov_RLE folder (fail if not found)
- "covl": Load covRleList objects, from cov_RLE_List folder (fail if not found)
- "bed": Load bed files, from bed folder (falls back to default)
- Other formats must be loaded directly with fimport
BPPARAM how many cores/threads to use? default: bpparam(). To see number of threads
used, do bpparam()$workers. You can also add a time remaining bar, for a
more detailed pipeline.
Value
a data.table of the count info
r strandMode covRle
Description
strandMode covRle
Usage
r(x)
Arguments
x a covRle object
272 rankOrder
Value
the forward RleList
r,covRle-method strandMode covRle
Description
strandMode covRle
Usage
## S4 method for signature 'covRle'
r(x)
Arguments
x a covRle object
Value
the forward RleList
rankOrder ORF rank in transcripts
Description
Creates an ordering of ORFs per transcript, so that ORF with the most upstream start codon is 1,
second most upstream start codon is 2, etc. Must input a grl made from ORFik, txNames_2 -> 2.
Usage
rankOrder(grl)
Arguments
grl a GRangesList object with ORFs
Value
a numeric vector of integers
References
doi: 10.1074/jbc.R116.733899
read.experiment 273
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss() , fpkm(), fpkm_calc(), fractionLength(),
initiationScore(), insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(),
orfScore(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
grl <- ORFik:::makeORFNames(grl)
rankOrder(grl)
read.experiment Read ORFik experiment
Description
Read in runs / samples from an experiment as a single R object. To read an ORFik experiment, you
must of course make one first. See create.experiment The file must be csv and be a valid ORFik
experiment
Usage
read.experiment(
file,
in.dir = ORFik::config()["exp"],
validate = TRUE,
output.env = .GlobalEnv
)
Arguments
file relative path to a ORFik experiment. That is a .csv file following ORFik experi-
ment style ("," as seperator). , or a template data.frame from create.experiment.
Can also be full path to file, then in.dir argument is ignored.
in.dir Directory to load experiment csv file from, default: ORFik::config()["exp"],
which has default "~/Bio_data/ORFik_experiments/"
Set to NULL if you don’t want to save it to disc. Does not apply if file argument
is not a path (can also be a data.frame). Also does not apply if file argument was
given as full path.
274 readBam
validate logical, default TRUE. Abort if any library files does not exist. Do not set this
to FALSE, unless you know what you are doing!
output.env an environment, default .GlobalEnv. Which environment should ORFik output
libraries to (if this is done), can be updated later with envExp(df) <- new.env().
Value
an ORFik experiment
See Also
Other ORFik_experiment: ORFik.template.experiment(), ORFik.template.experiment.zf(),
bamVarName(), create.experiment(), experiment-class, filepath(), libraryTypes(), organism,experiment-method,
outputLibs(), save.experiment(), validateExperiments()
Examples
# From file
## Not run:
# Read from file
df <- read.experiment(filepath) # <- valid ORFik .csv file
## End(Not run)
## Read from (create.experiment() template)
df <- ORFik.template.experiment()
## To save it, do:
# save.experiment(df, file = "path/to/save/experiment")
## You can then do:
# read.experiment("path/to/save/experiment")
# or (identical):
# read.experiment("experiment", in.dir = "path/to/save/")
readBam Custom bam reader
Description
Read in Bam file from either single end or paired end. Safer combined version of readGAlignments
and readGAlignmentPairs that takes care of some common errors.
If QNAMES of the aligned reads are from collapsed fasta files (if the names are formated from
collapsing in either (ORFik, ribotoolkit or fastx)), the bam file will contain a meta column called
"score" with the counts of duplicates per read. Only works for single end reads, as perfect duplica-
tion events for paired end is more rare and therefor not supported!.
Usage
readBam(path, chrStyle = NULL, param = NULL, strandMode = 0)
readBam 275
Arguments
path a character / data.table with path to .bam file. There are 3 input file possibilities.
• single end : a character path (length 1)
• paired end (1 file) : Either a character path (length of 2), where path[2] is
"paired-end", or a data.table with 2 columns, forward = path & reverse =
"paired-end"
• paired end (2 files) : Either a character path (length of 2), where path[2] is
path to R2, or a data.table with 2 columns, forward = path to R1 & reverse
= path to R2. (This one is not used often)
chrStyle a GRanges object, TxDb, FaFile, , a seqlevelsStyle or Seqinfo. (Default:
NULL) to get seqlevelsStyle from. In addition if it is a Seqinfo object, seqinfo
will be updated. Example of seqlevelsStyle update: Is chromosome 1 called
chr1 or 1, is mitocondrial chromosome called MT or chrM etc. Will use 1st
seqlevel-style if more are present. Like: c("NCBI", "UCSC") -> pick "NCBI"
param NULL or a ScanBamParam object. Like for scanBam, this influences what fields
and which records are imported. However, note that the fields specified thru this
ScanBamParam object will be loaded in addition to any field required for gen-
erating the returned object (GAlignments, GAlignmentPairs, or GappedReads
object), but only the fields requested by the user will actually be kept as meta-
data columns of the object.
By default (i.e. param=NULL or param=ScanBamParam()), no additional field is
loaded. The flag used is scanBamFlag(isUnmappedQuery=FALSE) for readGAlignments,
readGAlignmentsList, and readGappedReads. (i.e. only records correspond-
ing to mapped reads are loaded), and scanBamFlag(isUnmappedQuery=FALSE,
isPaired=TRUE, hasUnmappedMate=FALSE) for readGAlignmentPairs (i.e. only
records corresponding to paired-end reads with both ends mapped are loaded).
strandMode numeric, default 0. Only used for paired end bam files. One of (0: strand
= *, 1: first read of pair is +, 2: first read of pair is -). See ?strandMode.
Note: Sets default to 0 instead of 1, as readGAlignmentPairs uses 1. This is
to guarantee hits, but will also make mismatches of overlapping transcripts in
opposite directions.
Details
In the future will use a faster .bam loader for big .bam files in R.
Value
a GAlignments or GAlignmentPairs object of bam file
See Also
Other utils: bedToGR(), convertToOneBasedRanges(), export.bed12(), export.bigWig(), export.fstwig(),
export.wiggle(), fimport(), findFa(), fread.bed(), optimizeReads(), readBigWig(), readWig()
276 readBigWig
Examples
bam_file <- system.file("extdata/Danio_rerio_sample", "ribo-seq.bam", package = "ORFik")
readBam(bam_file, "UCSC")
readBigWig Custom bigWig reader
Description
Given 2 bigWig files (.bw, .bigWig), first is forward second is reverse. Merge them and return as
GRanges object. If they contain name reverse and forward, first and second order does not matter,
it will search for forward and reverse.
Usage
readBigWig(path, chrStyle = NULL, as = "GRanges")
Arguments
path a character path to two .bigWig files, or a data.table with 2 columns, (forward,
filepath) and reverse, only 1 row.
chrStyle a GRanges object, TxDb, FaFile, , a seqlevelsStyle or Seqinfo. (Default:
NULL) to get seqlevelsStyle from. In addition if it is a Seqinfo object, seqinfo
will be updated. Example of seqlevelsStyle update: Is chromosome 1 called
chr1 or 1, is mitocondrial chromosome called MT or chrM etc. Will use 1st
seqlevel-style if more are present. Like: c("NCBI", "UCSC") -> pick "NCBI"
as Specifies the class of the return object. Default is GRanges, which has one range
per range in the file, and a score column holding the value for each range. For
NumericList, one numeric vector is returned for each range in the selection
argument. For RleList, there is one Rle per sequence, and that Rle spans the
entire sequence.
Value
a GRanges object of the file/s
See Also
Other utils: bedToGR(), convertToOneBasedRanges(), export.bed12(), export.bigWig(), export.fstwig(),
export.wiggle(), fimport(), findFa(), fread.bed(), optimizeReads(), readBam(), readWig()
readLengthTable 277
readLengthTable Make table of readlengths
Description
Summarizing all libraries in experiment, make a table of proportion of read lengths.
Usage
readLengthTable(df, output.dir = NULL, type = "ofst", BPPARAM = bpparam())
Arguments
df an ORFik experiment
output.dir NULL or character path, default: NULL, plot not saved to disc. If defined saves
plot to that directory with the name "./readLengths.csv".
type character, default: "ofst". Type of library: either "default", usually bam format
(the one you gave to experiment), "pshifted" pshifted reads, "ofst", "bed", "bedo"
optimized bed, or "wig"
BPPARAM a core param, default: single thread: BiocParallel::SerialParam(). Set to
BiocParallel::bpparam() to use multicore. Be aware, this uses a lot of extra
ram (40GB+) for larger human samples!
Value
a data.table object of the the read length data with columns: c("sample", "sample_id", "read
length", "counts", "counts_per_sample", "perc_of_counts_per_sample")
readWidths Get read widths
Description
Input any reads, e.g. ribo-seq object and get width of reads, this is to avoid confusion between
width, qwidth and meta column containing original read width.
Usage
readWidths(reads, after.softclips = TRUE, along.reference = FALSE)
278 readWig
Arguments
reads a GRanges, GAlignment or GAlignmentPairs object.
after.softclips
logical (TRUE), include softclips in width. Does not apply if along.reference is
TRUE.
along.reference
logical (FALSE), example: The cigar "26MI2" is by default width 28, but if
along.reference is TRUE, it will be 26. The length of the read along the refer-
ence. Also "1D20M" will be 21 if by along.reference is TRUE. Intronic regions
(cigar: N) will be removed. So: "1M200N19M" is 20, not 220.
Details
If input is p-shifted and GRanges, the "$size" or "$score" colum" must exist, and the column must
contain the original read widths. In ORFik "$size" have higher priority than "$score" for defining
length. ORFik P-shifting creates a $size column, other softwares like shoelaces creates a score
column.
Remember to think about how you define length. Like the question: is a Illumina error mismatch
sufficient to reduce size of read and how do you know what is biological variance and what are
Illumina errors?
Value
an integer vector of widths
Examples
gr <- GRanges("chr1", 1)
readWidths(gr)
# GAlignment with hit (1M) and soft clipped base (1S)
ga <- GAlignments(seqnames = "1", pos = as.integer(1), cigar = "1M1S",
strand = factor("+", levels = c("+", "-", "*")))
readWidths(ga) # Without soft-clip bases
readWidths(ga, after.softclips = FALSE) # With soft-clip bases
readWig Custom wig reader
Description
Given 2 wig files, first is forward second is reverse. Merge them and return as GRanges object. If
they contain name reverse and forward, first and second order does not matter, it will search for
forward and reverse.
reassignTSSbyCage 279
Usage
readWig(path, chrStyle = NULL)
Arguments
path a character path to two .wig files, or a data.table with 2 columns, (forward,
filepath) and reverse, only 1 row.
chrStyle a GRanges object, TxDb, FaFile, , a seqlevelsStyle or Seqinfo. (Default:
NULL) to get seqlevelsStyle from. In addition if it is a Seqinfo object, seqinfo
will be updated. Example of seqlevelsStyle update: Is chromosome 1 called
chr1 or 1, is mitocondrial chromosome called MT or chrM etc. Will use 1st
seqlevel-style if more are present. Like: c("NCBI", "UCSC") -> pick "NCBI"
Value
a GRanges object of the file/s
See Also
Other utils: bedToGR(), convertToOneBasedRanges(), export.bed12(), export.bigWig(), export.fstwig(),
export.wiggle(), fimport(), findFa(), fread.bed(), optimizeReads(), readBam(), readBigWig()
reassignTSSbyCage Reassign all Transcript Start Sites (TSS)
Description
Given a GRangesList of 5’ UTRs or transcripts, reassign the start sites using max peaks from
CageSeq data. A max peak is defined as new TSS if it is within boundary of 5’ leader range,
specified by ‘extension‘ in bp. A max peak must also be higher than minimum CageSeq peak cutoff
specified in ‘filterValue‘. The new TSS will then be the positioned where the cage read (with highest
read count in the interval). If removeUnused is TRUE, leaders without cage hits, will be removed,
if FALSE the original TSS will be used.
Usage
reassignTSSbyCage(
fiveUTRs,
cage,
extension = 1000,
filterValue = 1,
restrictUpstreamToTx = FALSE,
removeUnused = FALSE,
preCleanup = TRUE,
cageMcol = FALSE
)
280 reassignTSSbyCage
Arguments
fiveUTRs (GRangesList) The 5’ leaders or full transcript sequences
cage Either a filePath for the CageSeq file as .bed .bam or .wig, with possible com-
pressions (".gzip", ".gz", ".bgz"), or already loaded CageSeq peak data as GRanges
or GAlignment. NOTE: If it is a .bam file, it will add a score column by run-
ning: convertToOneBasedRanges(cage, method = "5prime", addScoreColumn =
TRUE) The score column is then number of replicates of read, if score column
is something else, like read length, set the score column to NULL first.
extension The maximum number of basses upstream of the TSS to search for CageSeq
peak.
filterValue The minimum number of reads on cage position, for it to be counted as possible
new tss. (represented in score column in CageSeq data) If you already filtered,
set it to 0.
restrictUpstreamToTx
a logical (FALSE). If TRUE: restrict leaders to not extend closer than 5 bases
from closest upstream leader, set this to TRUE.
removeUnused logical (FALSE), if False: (standard is to set them to original annotation), If
TRUE: remove leaders that did not have any cage support.
preCleanup logical (TRUE), if TRUE, remove all reads in region (-5:-1, 1:5) of all original
tss in leaders. This is to keep original TSS if it is only +/- 5 bases from the
original.
cageMcol a logical (FALSE), if TRUE, add a meta column to the returned object with the
raw CAGE counts in support for new TSS.
Details
Note: If you used CAGEr, you will get reads of a probability region, with always score of 1. Re-
member then to set filterValue to 0. And you should use the 5’ end of the read as input, use:
ORFik:::convertToOneBasedRanges(cage) NOTE on filtervalue: To get high quality TSS, set filter-
value to median count of reads overlapping per leader. This will make you discard a lot of new TSS
positions though. I usually use 10 as a good standard.
TIP: do summary(countOverlaps(fiveUTRs, cage)) so you can find a good cutoff value for noise.
Value
a GRangesList of newly assigned TSS for fiveUTRs, using CageSeq data.
See Also
Other CAGE: assignTSSByCage(), reassignTxDbByCage()
Examples
# example 5' leader, notice exon_rank column
fiveUTRs <- GenomicRanges::GRangesList(
GenomicRanges::GRanges(seqnames = "chr1",
ranges = IRanges::IRanges(1000, 2000),
reassignTxDbByCage 281
strand = "+",
exon_rank = 1))
names(fiveUTRs) <- "tx1"
# make fake CAGE data from promoter of 5' leaders, notice score column
cage <- GenomicRanges::GRanges(
seqnames = "1",
ranges = IRanges::IRanges(500, width = 1),
strand = "+",
score = 10) # <- Number of tags (reads) per position
# notice also that seqnames use different naming, this is fixed by ORFik
# finally reassign TSS for fiveUTRs
reassignTSSbyCage(fiveUTRs, cage)
# See vignette for example using gtf file and real CAGE data.
reassignTxDbByCage Input a txdb and reassign the TSS for each transcript by CAGE
Description
Given a TxDb object, reassign the start site per transcript using max peaks from CageSeq data. A
max peak is defined as new TSS if it is within boundary of 5’ leader range, specified by ‘extension‘
in bp. A max peak must also be higher than minimum CageSeq peak cutoff specified in ‘filter-
Value‘. The new TSS will then be the positioned where the cage read (with highest read count in
the interval).
Usage
reassignTxDbByCage(
txdb,
cage,
extension = 1000,
filterValue = 1,
restrictUpstreamToTx = FALSE,
removeUnused = FALSE,
preCleanup = TRUE
)
Arguments
txdb a TxDb file, a path to one of: (.gtf ,.gff, .gff2, .gff2, .db or .sqlite) or an ORFik
experiment
cage Either a filePath for the CageSeq file as .bed .bam or .wig, with possible com-
pressions (".gzip", ".gz", ".bgz"), or already loaded CageSeq peak data as GRanges
or GAlignment. NOTE: If it is a .bam file, it will add a score column by run-
ning: convertToOneBasedRanges(cage, method = "5prime", addScoreColumn =
TRUE) The score column is then number of replicates of read, if score column
is something else, like read length, set the score column to NULL first.
282 reduceKeepAttr
extension The maximum number of basses upstream of the TSS to search for CageSeq
peak.
filterValue The minimum number of reads on cage position, for it to be counted as possible
new tss. (represented in score column in CageSeq data) If you already filtered,
set it to 0.
restrictUpstreamToTx
a logical (FALSE). If TRUE: restrict leaders to not extend closer than 5 bases
from closest upstream leader, set this to TRUE.
removeUnused logical (FALSE), if False: (standard is to set them to original annotation), If
TRUE: remove leaders that did not have any cage support.
preCleanup logical (TRUE), if TRUE, remove all reads in region (-5:-1, 1:5) of all original
tss in leaders. This is to keep original TSS if it is only +/- 5 bases from the
original.
Details
Note: If you used CAGEr, you will get reads of a probability region, with always score of 1.
Remember then to set filterValue to 0. And you should use the 5’ end of the read as input, use:
ORFik:::convertToOneBasedRanges(cage)
Value
a TxDb obect of reassigned transcripts
See Also
Other CAGE: assignTSSByCage(), reassignTSSbyCage()
Examples
## Not run:
library(GenomicFeatures)
# Get the gtf txdb file
txdbFile <- system.file("extdata", "hg19_knownGene_sample.sqlite",
package = "GenomicFeatures")
cagePath <- system.file("extdata", "cage-seq-heart.bed.bgz",
package = "ORFik")
reassignTxDbByCage(txdbFile, cagePath)
## End(Not run)
reduceKeepAttr Reduce GRanges / GRangesList
Description
Reduce away all GRanges elements with 0-width.
reduceKeepAttr 283
Usage
reduceKeepAttr(
grl,
keep.names = FALSE,
drop.empty.ranges = FALSE,
min.gapwidth = 1L,
with.revmap = FALSE,
with.inframe.attrib = FALSE,
ignore.strand = FALSE,
min.strand.decreasing = TRUE
)
Arguments
grl a GRangesList or GRanges object
keep.names (FALSE) keep the names and meta columns of the GRangesList
drop.empty.ranges
(FALSE) if a group is empty (width 0), delete it.
min.gapwidth (1L) how long gap can it be between two ranges, to merge them.
with.revmap (FALSE) return info on which mapped to which
with.inframe.attrib
(FALSE) For internal use.
ignore.strand (FALSE), can different strands be reduced together.
min.strand.decreasing
(TRUE), if GRangesList, return minus strand group ranges in decreasing order
(1-5, 30-50) -> (30-50, 1-5)
Details
Extends function reduce by trying to keep names and meta columns, if it is a GRangesList. It
also does not lose sorting for GRangesList, since original reduce sorts all by ascending position. If
keep.names == FALSE, it’s just the normal GenomicRanges::reduce with sorting negative strands
descending for GRangesList.
Value
A reduced GRangesList
See Also
Other ExtendGenomicRanges: asTX(), coveragePerTiling(), extendLeaders(), extendTrailers(),
tile1(), txSeqsFromFa(), windowPerGroup()
284 regionPerReadLength
Examples
ORF <- GRanges(seqnames = "1",
ranges = IRanges(start = c(1, 2, 3), end = c(1, 2, 3)),
strand = "+")
# For GRanges
reduceKeepAttr(ORF, keep.names = TRUE)
# For GRangesList
grl <- GRangesList(tx1_1 = ORF)
reduceKeepAttr(grl, keep.names = TRUE)
regionPerReadLength Find proportion of reads per position per read length in region
Description
This is defined as: Given some transcript region (like CDS), get coverage per position. By default
only returns positions that have hits, set drop.zero.dt to FALSE to get all 0 positions.
Usage
regionPerReadLength(
grl,
reads,
acceptedLengths = NULL,
withFrames = TRUE,
scoring = "transcriptNormalized",
weight = "score",
exclude.zero.cov.grl = TRUE,
drop.zero.dt = TRUE,
BPPARAM = bpparam()
)
Arguments
grl a GRangesList object with usually either leaders, cds’, 3’ utrs or ORFs
reads a GAlignments, GRanges, or precomputed coverage as covRleList (where
names of covRle objects are readlengths) of RiboSeq, RnaSeq etc.
Weigths for scoring is default the ’score’ column in ’reads’. Can also be random
access paths to bigWig or fstwig file. Do not use random access for more than a
few genes, then loading the entire files is usually better.
acceptedLengths
an integer vector (NULL), the read lengths accepted. Default NULL, means all
lengths accepted.
withFrames logical TRUE, add ORF frame (frame 0, 1, 2), starting on first position of every
grl.
remakeTxdbExonIds 285
scoring a character (transcriptNormalized), which meta coverage scoring ? one of (zs-
core, transcriptNormalized, mean, median, sum, sumLength, fracPos), see ?cov-
erageScorings for more info. Use to decide a scoring of hits per position for
metacoverage etc. Set to NULL if you do not want meta coverage, but instead
want per gene per position raw counts.
weight (default: ’score’), if defined a character name of valid meta column in subject.
GRanges("chr1", 1, "+", score = 5), would mean score column tells that this
alignment region was found 5 times. ORFik ofst, bedoc and .bedo files contains
a score column like this. As do CAGEr CAGE files and many other package
formats. You can also assign a score column manually.
exclude.zero.cov.grl
logical, default TRUE. Do not include ranges that does not have any coverage
(0 reads on them), this makes it faster to run.
drop.zero.dt logical, default TRUE. If TRUE and as.data.table is TRUE, remove all 0 count
positions. This greatly speeds up and most importantly, greatly reduces memory
usage. Will not change any plots, unless 0 count positions are used in some
sense.
BPPARAM how many cores/threads to use? default: bpparam()
Value
a data.table with lengths by coverage.
See Also
Other coverage: coverageScorings(), metaWindow(), scaledWindowPositions(), windowPerReadLength()
Examples
# Raw counts per gene per position
cds <- GRangesList(tx1 = GRanges("1", 100:129, "+"))
reads <- GRanges("1", seq(79,129, 3), "+")
reads$size <- 28 # <- Set read length of reads
regionPerReadLength(cds, reads, scoring = NULL)
## Sum up reads in each frame per read length per gene
regionPerReadLength(cds, reads, scoring = "frameSumPerLG")
remakeTxdbExonIds Get new exon ids after update of txdb
Description
Get new exon ids after update of txdb
Usage
remakeTxdbExonIds(txList)
286 remove.experiments
Arguments
txList a list, call of as.list(txdb)
Value
a new valid ordered list of exon ids (integer)
remove.experiments Remove ORFik experiment libraries load in R
Description
Variable names defined by df, in envir defined
Usage
remove.experiments(df, envir = envExp(df))
Arguments
df an ORFik experiment
envir environment to save to, default envExp(df), which defaults to .GlobalEnv, but
can be set with envExp(df) <- new.env() etc.
Value
NULL (objects removed from envir specified)
Examples
df <- ORFik.template.experiment()
# Output to .GlobalEnv with:
# outputLibs(df)
# Then remove them with:
# remove.experiments(df)
remove.file_ext 287
remove.file_ext Remove file extension of path
Description
Allows removal of compression
Usage
remove.file_ext(path, basename = FALSE)
Arguments
path character path (allows multiple paths)
basename relative path (TRUE) or full path (FALSE)? (default: FALSE)
Value
character path without file extension
removeMetaCols Removes meta columns
Description
Removes meta columns
Usage
removeMetaCols(grl)
Arguments
grl a GRangesList or GRanges object
Value
same type and structure as input without meta columns
288 removeORFsWithSameStartAsCDS
removeORFsWithinCDS Remove ORFs that are within cds
Description
Remove ORFs that are within cds
Usage
removeORFsWithinCDS(grl, cds)
Arguments
grl (GRangesList), the ORFs to filter
cds (GRangesList), the coding sequences (main ORFs on transcripts), to filter against.
Value
(GRangesList) of filtered uORFs
See Also
Other uorfs: addCdsOnLeaderEnds(), filterUORFs(), removeORFsWithSameStartAsCDS(), removeORFsWithSameStopAsCDS(),
removeORFsWithStartInsideCDS(), uORFSearchSpace()
removeORFsWithSameStartAsCDS
Remove ORFs that have same start site as the CDS
Description
Remove ORFs that have same start site as the CDS
Usage
removeORFsWithSameStartAsCDS(grl, cds)
Arguments
grl (GRangesList), the ORFs to filter
cds (GRangesList), the coding sequences (main ORFs on transcripts), to filter against.
Value
(GRangesList) of filtered uORFs
removeORFsWithSameStopAsCDS 289
See Also
Other uorfs: addCdsOnLeaderEnds(), filterUORFs(), removeORFsWithSameStopAsCDS(), removeORFsWithStartInsideCDS(),
removeORFsWithinCDS(), uORFSearchSpace()
removeORFsWithSameStopAsCDS
Remove ORFs that have same stop site as the CDS
Description
Remove ORFs that have same stop site as the CDS
Usage
removeORFsWithSameStopAsCDS(grl, cds)
Arguments
grl (GRangesList), the ORFs to filter
cds (GRangesList), the coding sequences (main ORFs on transcripts), to filter against.
Value
(GRangesList) of filtered uORFs
See Also
Other uorfs: addCdsOnLeaderEnds(), filterUORFs(), removeORFsWithSameStartAsCDS(), removeORFsWithStartInsideCDS(),
removeORFsWithinCDS(), uORFSearchSpace()
removeORFsWithStartInsideCDS
Remove ORFs that have start site within the CDS
Description
Remove ORFs that have start site within the CDS
Usage
removeORFsWithStartInsideCDS(grl, cds)
Arguments
grl (GRangesList), the ORFs to filter
cds (GRangesList), the coding sequences (main ORFs on transcripts), to filter against.
290 removeTxdbTranscripts
Value
(GRangesList) of filtered uORFs
See Also
Other uorfs: addCdsOnLeaderEnds(), filterUORFs(), removeORFsWithSameStartAsCDS(), removeORFsWithSameStopAsCDS(),
removeORFsWithinCDS(), uORFSearchSpace()
removeTxdbExons Remove exons in txList that are not in fiveUTRs
Description
Remove exons in txList that are not in fiveUTRs
Usage
removeTxdbExons(txList, fiveUTRs)
Arguments
txList a list, call of as.list(txdb)
fiveUTRs a GRangesList of 5’ leaders
Value
a list, modified call of as.list(txdb)
removeTxdbTranscripts Remove specific transcripts in txdb List
Description
Remove all transcripts, except the ones in fiveUTRs.
Usage
removeTxdbTranscripts(txList, fiveUTRs)
Arguments
txList a list, call of as.list(txdb)
fiveUTRs a GRangesList of 5’ leaders
Value
a txList
rename.SRA.files 291
rename.SRA.files Rename SRA files from metadata
Description
Rename SRA files from metadata
Usage
rename.SRA.files(files, new_names)
Arguments
files a character vector, with full path to all the files
new_names a character vector of new names or a data.table with metadata to use to rename
(usually from SRA metadata). Priority of renaming from the metadata is to
check for unique names in the LibraryName column, then the sample_title col-
umn if no valid names in LibraryName. If found and still duplicates, will add
"_rep1", "_rep2" to make them unique. Paired end data will get a extension of
_p1 and _p2. If no valid names, will not rename, that is keep the SRR numbers,
you then can manually rename files to something more meaningful.
Value
a character vector of new file names
See Also
Other sra: browseSRA(), download.SRA(), download.SRA.metadata(), download.ebi(), get_bioproject_candidates(),
install.sratoolkit()
repNames Get replicate name variants
Description
Used to standardize nomeclature for experiments.
Example: 1 is main naming, but a variant is rep1 rep1 will then be renamed to 1
Usage
repNames()
Value
a data.table with 2 columns, the main name, and all name variants of the main name in second
column as a list.
292 resFolder,experiment-method
See Also
Other experiment_naming: batchNames(), cellLineNames(), cellTypeNames(), conditionNames(),
fractionNames(), inhibitorNames(), libNames(), mainNames(), stageNames(), tissueNames()
resFolder Get ORFik experiment main output directory
Description
Get ORFik experiment main output directory
Usage
resFolder(x)
Arguments
x an ORFik experiment
Value
a character path
resFolder,experiment-method
Get ORFik experiment main output directory
Description
Get ORFik experiment main output directory
Usage
## S4 method for signature 'experiment'
resFolder(x)
Arguments
x an ORFik experiment
Value
a character path
restrictTSSByUpstreamLeader 293
restrictTSSByUpstreamLeader
Restrict extension of 5’ UTRs to closest upstream leader end
Description
Basicly this function restricts all startSites, to the upstream GRangesList objects end. Usually
leaders, for CAGE. Example: leader1: start on 10, leader2: stop on 8, extend leader1 to 5 -> this
function will resize leader1 to 9, to be outside leader2, so that CAGE reads can not wrongly overlap.
Usage
restrictTSSByUpstreamLeader(fiveUTRs, shiftedfiveUTRs)
Arguments
fiveUTRs The 5’ leader sequences as GRangesList
shiftedfiveUTRs
The 5’ leader sequences as GRangesList shifted by CAGE
Value
GRangesList object of restricted fiveUTRs
revElementsF Reverse elements within list
Description
A faster version of S4Vectors::revElements
Usage
revElementsF(x)
Arguments
x RleList
Value
a RleList (reversed inside list elements)
294 riboORFs
reverseMinusStrandPerGroup
Reverse minus strand
Description
Reverse minus strand per group in a GRangesList Only reverse if minus strand is in increasing order
Usage
reverseMinusStrandPerGroup(grl, onlyIfIncreasing = TRUE)
Arguments
grl a GRangesList
onlyIfIncreasing
logical, default (TRUE), only reverse if decreasing
Value
a GRangesList
riboORFs Load Predicted translons
Description
Load Predicted translons
Usage
riboORFs(df, type = "table", folder = riboORFsFolder(df))
Arguments
df ORFik experiment
type default "table", alternatives: c("table", "ranges_candidates", "ranges_predictions",
"predictions")
folder base folder to check for computed results, default: riboORFsFolder(df)
Value
a data.table, GRangesList or list of logical vector depending on input
riboORFsFolder 295
Examples
df <- ORFik.template.experiment()
df <- df[df$libtype == "RFP",][c(1,2),]
# riboORFs(df) # Works when you have run prediction
riboORFsFolder Define folder for prediction output
Description
Define folder for prediction output
Usage
riboORFsFolder(df, parrent_dir = resFolder(df))
Arguments
df ORFik experiment
parrent_dir Parrent directory of computed study results, default: resFolder(df)
Value
a file path (full path)
Examples
df <- ORFik.template.experiment()
df <- df[df$libtype == "RFP",][c(1,2),]
riboORFsFolder(df)
riboORFsFolder(df, tempdir())
RiboQC.plot Quality control for pshifted Ribo-seq data
Description
Combines several statistics from the pshifted reads into a plot:
-1 Coding frame distribution per read length
-2 Alignment statistics
-3 Biotype of non-exonic pshifted reads
-4 mRNA localization of pshifted reads
296 RiboQC.plot
Usage
RiboQC.plot(
df,
output.dir = QCfolder(df),
width = 6.6,
height = 4.5,
plot.ext = ".pdf",
type = "pshifted",
weight = "score",
bar.position = "dodge",
as_gg_list = FALSE,
BPPARAM = BiocParallel::SerialParam(progressbar = TRUE)
)
Arguments
df an ORFik experiment
output.dir NULL or character path, default: NULL, plot not saved to disc. If defined saves
plot to that directory with the name "/STATS_plot.pdf".
width width of plot, default 6.6 (in inches)
height height of plot, default 4.5 (in inches)
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg".
type type of library loaded, default pshifted, warning if not pshifted might crash if
too many read lengths!
weight which column in reads describe duplicates, default "score".
bar.position character, default "dodge". Should Ribo-seq frames per read length be posi-
tioned as "dodge" or "stack" (on top of each other).
as_gg_list logical, default FALSE. Return as a list of ggplot objects instead of as a grob.
Gives you the ability to modify plots more directly.
BPPARAM how many cores/threads to use? default: bpparam(). To see number of threads
used, do bpparam()$workers. You can also add a time remaining bar, for a
more detailed pipeline.
Value
the plot object, a grob of ggplot objects of the the data
Examples
df <- ORFik.template.experiment()
df <- df[3,] #lets only p-shift RFP sample at index 3
#shiftFootprintsByExperiment(df)
#RiboQC.plot(df)
ribosomeReleaseScore 297
ribosomeReleaseScore Ribosome Release Score (RRS)
Description
Ribosome Release Score is defined as
(RPFs over ORF)/(RPFs over 3' utrs)
and additionaly normalized by lengths. If RNA is added as argument, it will normalize by RNA
counts to justify location of 3’ utrs. It can be understood as a ribosome stalling feature. A pseudo-
count of one was added to both the ORF and downstream sums.
Usage
ribosomeReleaseScore(
grl,
RFP,
GtfOrThreeUtrs,
RNA = NULL,
weight.RFP = 1L,
weight.RNA = 1L,
overlapGrl = NULL
)
Arguments
grl a GRangesList object with usually either leaders, cds’, 3’ utrs or ORFs.
RFP RiboSeq reads as GAlignments, GRanges or GRangesList object
GtfOrThreeUtrs if Gtf: a TxDb object of a gtf file transcripts is called from: ‘threeUTRsByTran-
script(Gtf, use.names = TRUE)‘, if object is GRangesList, it is presumed to be
the 3’ utrs
RNA RnaSeq reads as GAlignments, GRanges or GRangesList object
weight.RFP a vector (default: 1L). Can also be character name of column in RFP. As in trans-
lationalEff(weight = "score") for: GRanges("chr1", 1, "+", score = 5), would
mean score column tells that this alignment region was found 5 times.
weight.RNA Same as weightRFP but for RNA weights. (default: 1L)
overlapGrl an integer, (default: NULL), if defined must be countOverlaps(grl, RFP), added
for speed if you already have it
Value
a named vector of numeric values of scores, NA means that no 3’ utr was found for that transcript.
References
doi: 10.1016/j.cell.2013.06.009
298 ribosomeStallingScore
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss() , fpkm(), fpkm_calc(), fractionLength(),
initiationScore(), insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(),
orfScore(), rankOrder(), ribosomeStallingScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
ORF <- GRanges(seqnames = "1",
ranges = IRanges(start = c(1, 10, 20), end = c(5, 15, 25)),
strand = "+")
grl <- GRangesList(tx1_1 = ORF)
threeUTRs <- GRangesList(tx1 = GRanges("1", IRanges(40, 50), "+"))
RFP <- GRanges("1", IRanges(25, 25), "+")
RNA <- GRanges("1", IRanges(1, 50), "+")
ribosomeReleaseScore(grl, RFP, threeUTRs, RNA)
ribosomeStallingScore Ribosome Stalling Score (RSS)
Description
Is defined as
(RPFs over ORF stop sites)/(RPFs over ORFs)
and normalized by lengths A pseudo-count of one was added to both the ORF and downstream
sums.
Usage
ribosomeStallingScore(grl, RFP, weight = 1L, overlapGrl = NULL)
Arguments
grl a GRangesList object with usually either leaders, cds’, 3’ utrs or ORFs.
RFP RiboSeq reads as GAlignments, GRanges or GRangesList object
weight a vector (default: 1L, if 1L it is identical to countOverlaps()), if single number
(!= 1), it applies for all, if more than one must be equal size of ’reads’. else it
must be the string name of a defined meta column in subject "reads", that gives
number of times a read was found. GRanges("chr1", 1, "+", score = 5), would
mean "score" column tells that this alignment region was found 5 times.
overlapGrl an integer, (default: NULL), if defined must be countOverlaps(grl, RFP), added
for speed if you already have it
ribo_fft 299
Value
a named vector of numeric values of RSS scores
References
doi: 10.1016/j.cels.2017.08.004
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss() , fpkm(), fpkm_calc(), fractionLength(),
initiationScore(), insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(),
orfScore(), rankOrder(), ribosomeReleaseScore(), startRegion(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
ORF <- GRanges(seqnames = "1",
ranges = IRanges(start = c(1, 10, 20), end = c(5, 15, 25)),
strand = "+")
grl <- GRangesList(tx1_1 = ORF)
RFP <- GRanges("1", IRanges(25, 25), "+")
ribosomeStallingScore(grl, RFP)
ribo_fft Get periodogram data per read length
Description
A data.table of periods and amplitudes, great to detect ribosomal read lengths. Uses 5’ end of reads
to detect periodicity. Works both before and after p-shifting. Plot results with ribo_fft_plot.
Usage
ribo_fft(footprints, cds, read_lengths = 26:34, firstN = 150)
Arguments
footprints Ribosome footprints in either GAlignments or GRanges
cds a GRangesList of coding sequences. Length must match length of argument
mrna, and all must have length > arugment firstN.
read_lengths integer vector, default: 26:34, which read length to check for. Will exclude all
read_lengths that does not exist for footprints.
firstN (integer) Represents how many bases of the transcripts downstream of start
codons to use for initial estimation of the periodicity.
300 ribo_fft_plot
Value
a data.table with read_length, amplitude and periods
Examples
## Note, this sample data is not intended to be strongly periodic.
## Real data should have a cleaner peak for x = 3 (periodicity)
# Load sample data
df <- ORFik.template.experiment()
# Load annotation
loadRegions(df, "cds", names.keep = filterTranscripts(df))
# Select a riboseq library
df <- df[df$libtype == "RFP", ]
footprints <- fimport(filepath(df[1,], "default"))
fft_dt <-ribo_fft(footprints, cds)
ribo_fft_plot(fft_dt)
ribo_fft_plot Get periodogram plot per read length
Description
Get periodogram plot per read length
Usage
ribo_fft_plot(fft_dt, period_window = c(0, 6))
Arguments
fft_dt a data.table with read_length, amplitude and periods
period_window x axis limits, default c(0,6)
Value
a ggplot, geom_line plot facet by read length.
Examples
## Note, this sample data is not intended to be strongly periodic.
## Real data should have a cleaner peak for x = 3 (periodicity)
# Load sample data
df <- ORFik.template.experiment()
# Load annotation
cds <- loadRegion(df, "cds", names.keep = filterTranscripts(df))
# Select a riboseq library
df <- df[df$libtype == "RFP", ]
footprints <- fimport(filepath(df[1,], "default"))
fft_dt <-ribo_fft(footprints, cds)
ribo_fft_plot(fft_dt)
rnaNormalize 301
rnaNormalize Normalize a data.table of coverage by RNA seq per position
Description
Normalizes per position per gene by this function: (reads at position / min(librarysize, 1) * number
of genes) / fpkm of that gene’s RNA-seq
Usage
rnaNormalize(coverage, df, dfr = NULL, tx, normalizeMode = "position")
Arguments
coverage a data.table containing at least columns (count/score, position), it is possible to
have additionals: (genes, fraction, feature)
df an ORFik experiment
dfr an ORFik experiment of RNA-seq to normalize against. Will add RNA nor-
malized to plot name if this is done.
tx a GRangesList of mrna transcripts
normalizeMode a character (default: "position"), how to normalize library against rna library.
Either on "position", normalize by number of genes, sum of reads and RNA seq,
on tx "region" or "feature": same as position but RNA is split into the feature
groups to normalize. Useful if you have a list of targets and background genes.
Details
Good way to compare libraries
Value
a data.table of normalized transcripts by RNA.
runIDs Get SRR/DRR/ERR run ids from ORFik experiment
Description
Get SRR/DRR/ERR run ids from ORFik experiment
Usage
runIDs(x)
302 save.experiment
Arguments
x an ORFik experiment
Value
a character vector of runIDs, "" if not existing.
runIDs,experiment-method
Get SRR/DRR/ERR run ids from ORFik experiment
Description
Get SRR/DRR/ERR run ids from ORFik experiment
Usage
## S4 method for signature 'experiment'
runIDs(x)
Arguments
x an ORFik experiment
Value
a character vector of runIDs, "" if not existing.
save.experiment Save experiment to disc
Description
Save experiment to disc
Usage
save.experiment(df, file)
Arguments
df an ORFik experiment
file name of file to save df as
savePlot 303
Value
NULL (experiment save only)
See Also
Other ORFik_experiment: ORFik.template.experiment(), ORFik.template.experiment.zf(),
bamVarName(), create.experiment(), experiment-class, filepath(), libraryTypes(), organism,experiment-method,
outputLibs(), read.experiment(), validateExperiments()
Examples
df <- ORFik.template.experiment()
## Save with:
#save.experiment(df, file = "path/to/save/experiment.csv")
## Identical (.csv not needed, can be added):
#save.experiment(df, file = "path/to/save/experiment")
savePlot Helper function for writing plots to disc
Description
Helper function for writing plots to disc
Usage
savePlot(
plot,
output = NULL,
width = 200,
height = 150,
plot.ext = ".pdf",
dpi = 300
)
Arguments
plot the ggplot to save
output character string (NULL), if set, saves the plot as pdf or png to path given. If no
format is given, is save as specified by plot.ext argument.
width width of output in mm
height height of output in mm
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg".
dpi (300) dpi of plot
304 scaledWindowPositions
Value
a ggplot object of the coverage plot, NULL if output is set, then the plot will only be saved to
location.
See Also
Other coveragePlot: coverageHeatMap(), pSitePlot(), windowCoveragePlot()
scaledWindowPositions Scale (bin) windows to a meta window of given size
Description
For example scale a coverage table of a all human CDS to width 100
Usage
scaledWindowPositions(
grl,
reads,
scaleTo = 100,
scoring = "meanPos",
weight = "score",
is.sorted = FALSE,
drop.zero.dt = FALSE
)
Arguments
grl a GRangesList of 5’ utrs, CDS, transcripts, etc.
reads a GAlignments, GRanges, or precomputed coverage as covRle (one for each
strand) of RiboSeq, RnaSeq etc.
Weigths for scoring is default the ’score’ column in ’reads’. Can also be random
access paths to bigWig or fstwig file. Do not use random access for more than a
few genes, then loading the entire files is usually better. File streaming is still in
beta, so use with care!
scaleTo an integer (100), if windows have different size, a meta window can not directly
be created, since a meta window must have equal size for all windows. Rescale
all windows to scaleTo. i.e c(1,2,3) -> size 2 -> c(1, mean(2,3)) etc. Can also be
a vector, 1 number per grl group.
scoring a character, one of (meanPos, sumPos, ..) Check the coverageScoring function
for more options.
weight (default: ’score’), if defined a character name of valid meta column in subject.
GRanges("chr1", 1, "+", score = 5), would mean score column tells that this
alignment region was found 5 times. ORFik ofst, bedoc and .bedo files contains
a score column like this. As do CAGEr CAGE files and many other package
formats. You can also assign a score column manually.
scoreSummarizedExperiment 305
is.sorted logical (FALSE), is grl sorted. That is + strand groups in increasing ranges
(1,2,3), and - strand groups in decreasing ranges (3,2,1)
drop.zero.dt logical FALSE, if TRUE and as.data.table is TRUE, remove all 0 count posi-
tions. This greatly speeds up and most importantly, greatly reduces memory
usage. Will not change any plots, unless 0 positions are used in some sense.
(mean, median, zscore coverage will only scale differently)
Details
Nice for making metaplots, the score will be mean of merged positions.
Value
A data.table with scored counts (counts) of reads mapped to positions (position) specified in win-
dows along with frame (frame).
See Also
Other coverage: coverageScorings(), metaWindow(), regionPerReadLength(), windowPerReadLength()
Examples
library(GenomicRanges)
windows <- GRangesList(GRanges("chr1", IRanges(1, 200), "-"))
x <- GenomicRanges::GRanges(
seqnames = "chr1",
ranges = IRanges::IRanges(c(1, 100, 199), c(2, 101, 200)),
strand = "-")
scaledWindowPositions(windows, x, scaleTo = 100)
scoreSummarizedExperiment
Helper function for makeSummarizedExperimentFromBam
Description
If txdb or gtf path is added, it is a rangedSummerizedExperiment For FPKM values, DESeq2::fpkm(robust
= FALSE) is used
Usage
scoreSummarizedExperiment(
final,
score = "transcriptNormalized",
collapse = FALSE
)
306 seqinfo,covRleList-method
Arguments
final ranged summarized experiment object
score default: "transcriptNormalized" (row normalized raw counts matrix), alternative
is "fpkm", "log2fpkm" or "log10fpkm"
collapse a logical/character (default FALSE), if TRUE all samples within the group SAM-
PLE will be collapsed to one. If "all", all groups will be merged into 1 col-
umn called merged_all. Collapse is defined as rowSum(elements_per_group) /
ncol(elements_per_group)
Value
a DEseq summerizedExperiment object (transcriptNormalized) or matrix (if fpkm input)
seqinfo,covRle-method Seqinfo covRle Extracted from forward RleList
Description
Seqinfo covRle Extracted from forward RleList
Usage
## S4 method for signature 'covRle'
seqinfo(x)
Arguments
x a covRle object
Value
integer vector with names
seqinfo,covRleList-method
Seqinfo covRle Extracted from forward RleList
Description
Seqinfo covRle Extracted from forward RleList
Usage
## S4 method for signature 'covRleList'
seqinfo(x)
seqinfo,experiment-method 307
Arguments
x a covRle object
Value
integer vector with names
seqinfo,experiment-method
Seqinfo ORFik experiment Extracted from fasta genome index
Description
Seqinfo ORFik experiment Extracted from fasta genome index
Usage
## S4 method for signature 'experiment'
seqinfo(x)
Arguments
x an ORFik experiment
Value
integer vector with names
seqlevels,covRle-method
Seqlevels covRle Extracted from forward RleList
Description
Seqlevels covRle Extracted from forward RleList
Usage
## S4 method for signature 'covRle'
seqlevels(x)
Arguments
x a covRle object
Value
integer vector with names
308 seqlevels,experiment-method
seqlevels,covRleList-method
Seqlevels covRleList Extracted from forward RleList
Description
Seqlevels covRleList Extracted from forward RleList
Usage
## S4 method for signature 'covRleList'
seqlevels(x)
Arguments
x a covRle object
Value
integer vector with names
seqlevels,experiment-method
Seqlevels ORFik experiment Extracted from fasta genome index
Description
Seqlevels ORFik experiment Extracted from fasta genome index
Usage
## S4 method for signature 'experiment'
seqlevels(x)
Arguments
x an ORFik experiment
Value
integer vector with names
seqnamesPerGroup 309
seqnamesPerGroup Get list of seqnames per granges group
Description
Get list of seqnames per granges group
Usage
seqnamesPerGroup(grl, keep.names = TRUE)
Arguments
grl a GRangesList
keep.names a boolean, keep names or not, default: (TRUE)
Value
a character vector or Rle of seqnames(if seqnames == T)
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
seqnamesPerGroup(grl)
shiftFootprints Shift footprints by selected offsets
Description
Function shifts footprints (GRanges) using specified offsets for every of the specified lengths. Reads
that do not conform to the specified lengths are filtered out and rejected. Reads are resized to single
base in 5’ end fashion, treated as p site. This function takes account for junctions and soft clips in
cigars of the reads. Length of the footprint is saved in size’ parameter of GRanges output. Footprints
are also sorted according to their genomic position, ready to be saved as a ofst, covRle, bed or wig
file.
Usage
shiftFootprints(footprints, shifts, sort = TRUE)
310 shiftFootprints
Arguments
footprints GAlignments object of RiboSeq reads
shifts a data.frame / data.table with minimum 2 columns, fraction (selected read lengths)
and offsets_start (relative position in nt). Output from detectRibosomeShifts.
Run ORFik::shifts.load(df)[[1]] for an example of input.
sort logical, default TRUE. If False will keep original order of reads, and not sort
output reads in increasing genomic location per chromosome and strand.
Details
The two columns in the shift data.frame/data.table argument are:
- fraction Numeric vector of lengths of footprints you select for shifting.
- offsets_start Numeric vector of shifts for corresponding selected_lengths. eg. c(-10, -10) with
selected_lengths of c(31, 32) means length of 31 will be shifted left by 10. Footprints of length 32
will be shifted right by 10.
NOTE: It will remove softclips from valid width, the CIGAR 3S30M is qwidth 33, but will remove
3S so final read width is 30 in ORFik.
Value
A GRanges object of shifted footprints, sorted and resized to 1bp of p-site, with metacolumn "size"
indicating footprint size before shifting and resizing, sorted in increasing order.
References
https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4912-6
See Also
Other pshifting: changePointAnalysis(), detectRibosomeShifts(), shiftFootprintsByExperiment(),
shiftPlots(), shifts.load()
Examples
## Basic run
# Transcriptome annotation ->
gtf_file <- system.file("extdata/Danio_rerio_sample", "annotations.gtf", package = "ORFik")
# Ribo seq data ->
riboSeq_file <- system.file("extdata/Danio_rerio_sample", "ribo-seq.bam", package = "ORFik")
## Not run:
footprints <- readBam(riboSeq_file)
# detect the shifts automagically
shifts <- detectRibosomeShifts(footprints, gtf_file)
# shift the RiboSeq footprints
shiftedReads <- shiftFootprints(footprints, shifts)
## End(Not run)
shiftFootprintsByExperiment 311
shiftFootprintsByExperiment
Shift footprints of each file in experiment
Description
A function that combines the steps of periodic read length detection, p-site shift detection and p-
shifting into 1 function. For more details, see: detectRibosomeShifts
Saves files to a specified location as .ofst and .wig, The .ofst file will include a score column con-
taining read width.
The .wig files, will be saved in pairs of +/- strand, and score column will be replicates of reads
starting at that position, score = 5 means 5 reads.
Remember that different species might have different default Ribosome read lengths, for human,
mouse etc, normally around 27:30.
Usage
shiftFootprintsByExperiment(
df,
out.dir = pasteDir(libFolder(df), "/pshifted/"),
start = TRUE,
stop = FALSE,
top_tx = 10L,
minFiveUTR = 30L,
minCDS = 150L,
minThreeUTR = if (stop) {
30
} else NULL,
firstN = 150L,
min_reads = 1000,
min_reads_TIS = 50,
accepted.lengths = 26:34,
output_format = c("ofst", "wig"),
BPPARAM = bpparam(),
tx = NULL,
shift.list = NULL,
log = TRUE,
heatmap = FALSE,
must.be.periodic = TRUE,
strict.fft = TRUE,
verbose = FALSE
)
Arguments
df an ORFik experiment
312 shiftFootprintsByExperiment
out.dir output directory for files, default: pasteDir(libFolder(df), "/pshifted/"), making
a /pshifted folder inside default bam file location
start (logical) Whether to include predictions based on the start codons. Default
TRUE.
stop (logical) Whether to include predictions based on the stop codons. Default
FASLE. Only use if there exists 3’ UTRs for the annotation. If peridicity around
stop codon is stronger than at the start codon, use stop instead of start region for
p-shifting.
top_tx (integer), default 10. Specify which % of the top TIS coverage transcripts to use
for estimation of the shifts. By default we take top 10 top covered transcripts as
they represent less noisy data-set. This is only applicable when there are more
than 1000 transcripts.
minFiveUTR (integer) minimum bp for 5’ UTR during filtering for the transcripts. Set to
NULL if no 5’ UTRs exists for annotation.
minCDS (integer) minimum bp for CDS during filtering for the transcripts
minThreeUTR (integer) minimum bp for 3’ UTR during filtering for the transcripts. Set to
NULL if no 3’ UTRs exists for annotation.
firstN (integer) Represents how many bases of the transcripts downstream of start
codons to use for initial estimation of the periodicity.
min_reads default (1000), how many reads must a read-length have in total to be considered
for periodicity.
min_reads_TIS default (50), how many reads must a read-length have in the TIS region to be
considered for periodicity.
accepted.lengths
accepted read lengths, default 26:34, usually ribo-seq is strongest between 27:32.
output_format default c("ofst", "wig"), use export.ofst or wiggle format (wig) using export.wiggle
? Default is both.
Options are: c("ofst", "bigWig", "wig", "bed", "bedo") For future coverage per
nucleotide, we advice to do here ofst and bigWig for other genome browsers,
then call convert_to_covRleList to get much faster R objects.
The wig format version can be used in IGV, the score column is counts of that
read with that read length, the cigar reference width is lost, ofst is much faster to
save and load in R, and retain cigar reference width, but can not be used in IGV.
Also for larger tracks, you can use "bigWig".
BPPARAM how many cores/threads to use? default: bpparam()
tx a GRangesList, if you do not have 5’ UTRs in annotation, send your own ver-
sion. Example: extendLeaders(tx, 30) Where 30 bases will be new "leaders".
Since each original transcript was either only CDS or non-coding (filtered out).
shift.list default NULL, or a list containing named data.frames / data.tables with mini-
mum 2 columns, fraction (selected read lengths) and offsets_start (relative posi-
tion in nt). 1 named data.frame / data.table per library. Output from detectRibosomeShifts.
Run ORFik::shifts.load(df) for an example of input. The names of the list
must be the file.paths of the Ribo-seq libraries. Use this to edit the shifts, if you
suspect some of them are wrong in an experiment.
shiftFootprintsByExperiment 313
log logical, default (TRUE), output a log file with parameters used and a .rds file
with all shifts per library (can be loaded with shifts.load)
heatmap a logical or character string, default FALSE. If TRUE, will plot heatmap of raw
reads before p-shifting to console, to see if shifts given make sense. You can
also set a filepath to save the file there.
must.be.periodic
logical TRUE, if FALSE will not filter on periodic read lengths. (The Fourier
transform filter will be skipped). This is useful if you are not going to do period-
icity analysis, that is: for you more coverage depth (more read lengths) is more
important than only keeping the high quality periodic read lengths.
strict.fft logical, TRUE. Use a FFT without noise filter. This means keep only reads
lengths that are "periodic for the human eye". If you want more coverage, set to
FALSE, to also get read lengths that are "messy", but the noise filter detects the
periodicity of 3. This should only be done when you do not need high quality
periodic reads! Example would be differential translation analysis by counts
over each ORF.
verbose logical, default FALSE. Report details of analysis/periodogram. Good if you are
not sure if the analysis was correct.
Value
NULL (Objects are saved to out.dir/pshited/"name_pshifted.ofst", wig, bedo or .bedo)
References
https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4912-6
See Also
Other pshifting: changePointAnalysis(), detectRibosomeShifts(), shiftFootprints(), shiftPlots(),
shifts.load()
Examples
df <- ORFik.template.experiment.zf()
df <- df[1,] #lets only p-shift first RFP sample
## Output files as both .ofst and .wig(can be viewed in IGV/UCSC)
shiftFootprintsByExperiment(df)
# If you only need in R, do: (then you get no .wig files)
#shiftFootprintsByExperiment(df, output_format = "ofst")
## With debug info:
#shiftFootprintsByExperiment(df, verbose = TRUE)
## Re-shift, if you think some are wrong
## Here as an example we update library 1, third read length to shift 12
shift.list <- shifts.load(df)
shift.list[[1]]$offsets_start[3] <- -12
#shiftFootprintsByExperiment(df, shift.list = shift.list)
## For additional speedup in R for nucleotide coverage (coveragePerTiling etc)
314 shiftPlots
shiftPlots Plot shifted heatmaps per library
Description
Around CDS TISs, plot coverage. A good validation for you p-shifting, to see shifts are correspond-
ing and close to the CDS TIS.
Usage
shiftPlots(
df,
output = NULL,
title = "Ribo-seq",
scoring = "transcriptNormalized",
pShifted = TRUE,
upstream = if (pShifted) 5 else 20,
downstream = if (pShifted) 20 else 5,
type = "bar",
addFracPlot = TRUE,
plot.ext = ".pdf",
BPPARAM = bpparam()
)
Arguments
df an ORFik experiment
output name to save file, full path. (Default NULL) No saving. Sett to "auto" to save to
QC_STATS folder of experiment named: "pshifts_barplots.png" or "pshifts_heatmaps.png"
depending on type argument. Folder must exist!
title Title for top of plot, default "Ribo-seq". A more informative name could be
"Ribo-seq zebrafish Chew et al. 2013"
scoring which scoring scheme to use for heatmap, default "transcriptNormalized". Some
alternatives: "sum", "zscore".
pShifted a logical (TRUE), are Ribo-seq reads p-shifted to size 1 width reads? If upstream
and downstream is set, this argument is irrelevant. So set to FALSE if this is not
p-shifted Ribo-seq.
upstream an integer (5), relative region to get upstream from. Default: ifelse(!is.null(tx),
ifelse(pShifted, 5, 20), min(ifelse(pShifted, 5, 20), 0))
downstream an integer (20), relative region to get downstream from. Default: ifelse(pShifted,
20, 5)
type character, default "bar". Plot as faceted bars, gives more detailed information of
read lengths, but harder to see patterns over multiple read lengths. Alternative:
"heatmap", better overview of patterns over multiple read lengths.
addFracPlot logical, default TRUE, add positional sum plot on top per heatmap.
shifts.load 315
plot.ext default ".pdf". Alternative ".png". Only added if output is "auto".
BPPARAM how many cores/threads to use? default: bpparam()
Value
a ggplot2 grob object
See Also
Other pshifting: changePointAnalysis(), detectRibosomeShifts(), shiftFootprints(), shiftFootprintsByExperiment(),
shifts.load()
Examples
df <- ORFik.template.experiment.zf()
df <- df[df$libtype == "RFP",][1,] #lets only p-shift first RFP sample
#shiftFootprintsByExperiment(df, output_format = "bedo)
#grob <- shiftPlots(df, title = "Ribo-seq Human ORFik et al. 2020")
#plot(grob) #Only plot in RStudio for small amount of files!
shifts.load Load the shifts from experiment
Description
When you p-shift using the function shiftFootprintsByExperiment, you will get a list of shifts per
library. To automatically load them, you can use this function. Defaults to loading pshifts, if you
made a-sites or e-sites, change the path argument to ashifted/eshifted folder instead.
Usage
shifts.load(
df,
path = pasteDir(dirname(df$filepath[1]), "/pshifted/shifting_table.rds")
)
Arguments
df an ORFik experiment
path path to .rds file containing the shifts as a list, one list element per shifted bam
file.
Value
a list of the shifts, one list element per shifted bam file.
316 show,covRleList-method
See Also
Other pshifting: changePointAnalysis(), detectRibosomeShifts(), shiftFootprints(), shiftFootprintsByExperiment(),
shiftPlots()
Examples
df <- ORFik.template.experiment()
# subset on Ribo-seq
df <- df[df$libtype == "RFP",]
#shiftFootprintsByExperiment(df)
#shifts.load(df)
show,covRle-method covRle show definition
Description
Show a simplified version of the covRle
Usage
## S4 method for signature 'covRle'
show(object)
Arguments
object acovRle
Value
print state of covRle
show,covRleList-method
covRleList show definition
Description
Show a simplified version of the covRleList.
Usage
## S4 method for signature 'covRleList'
show(object)
show,experiment-method 317
Arguments
object acovRleList
Value
print state of covRleList
show,experiment-method
experiment show definition
Description
Show a simplified version of the experiment. The show function simplifies the view so that any
column of data (like replicate or stage) is not shown, if all values are identical in that column.
Filepaths are also never shown.
Usage
## S4 method for signature 'experiment'
show(object)
Arguments
object an ORFik experiment
Value
print state of experiment
simpleLibs Converted format of NGS libraries
Description
Export as either .ofst, .wig, .bigWig,.bedo (legacy format) or .bedoc (legacy format) files:
Export files as .ofst for fastest load speed into R.
Export files as .wig / bigWig for use in IGV or other genome browsers.
The input files are checked if they exist from: envExp(df).
318 simpleLibs
Usage
simpleLibs(
df,
out.dir = libFolder(df),
addScoreColumn = TRUE,
addSizeColumn = TRUE,
must.overlap = NULL,
method = "None",
type = "ofst",
input.type = "ofst",
reassign.when.saving = FALSE,
envir = envExp(df),
BPPARAM = bpparam()
)
Arguments
df an ORFik experiment
out.dir optional output directory, default: libFolder(df), if it is NULL, it will just reas-
sign R objects to simplified libraries. Will then create a final folder specfied as:
paste0(out.dir, "/", type, "/"). Here the files will be saved in format given by the
type argument.
addScoreColumn logical, default TRUE, if FALSE will not add replicate numbers as score col-
umn, see ORFik::convertToOneBasedRanges.
addSizeColumn logical, default TRUE, if FALSE will not add size (width) as size column, see
ORFik::convertToOneBasedRanges. Does not apply for (GAlignment version
of.ofst) or .bedoc. Since they contain the original cigar.
must.overlap default (NULL), else a GRanges / GRangesList object, so only reads that over-
lap (must.overlap) are kept. This is useful when you only need the reads over
transcript annotation or subset etc.
method character, default "None", the method to reduce ranges, for more info see convertToOneBasedRanges
type character, output format, default "ofst". Alternatives: "ofst", "bigWig", "wig","bedo"
or "bedoc". Which format you want. Will make a folder within out.dir with this
name containing the files.
input.type character, input type "ofst". Remember this function uses the loaded libraries if
existing, so this argument is usually ignored. Only used if files do not already
exist.
reassign.when.saving
logical, default FALSE. If TRUE, will reassign library to converted form after
saving. Ignored when out.dir = NULL.
envir environment to save to, default envExp(df), which defaults to .GlobalEnv, but
can be set with envExp(df) <- new.env() etc.
BPPARAM how many cores/threads to use? default: bpparam(). To see number of threads
used, do bpparam()$workers. You can also add a time remaining bar, for a
more detailed pipeline.
sortPerGroup 319
Details
We advice you to not use this directly, as other function are more safe for library type conversions.
See family description below. This is mostly used internally in ORFik. It is only adviced to use if
large bam files are already loaded in R and conversions are wanted from those.
See export.ofst, export.wiggle, export.bedo and export.bedoc for information on file for-
mats.
If libraries of the experiment are already loaded into environment (default: .globalEnv) is will ex-
port using those files as templates. If they are not in environment the .ofst files from the bam files
are loaded (unless you are converting to .ofst then the .bam files are loaded).
Value
NULL (saves files to disc or R .GlobalEnv)
See Also
Other lib_converters: convert_bam_to_ofst(), convert_to_bigWig(), convert_to_covRle(),
convert_to_covRleList()
Examples
df <- ORFik.template.experiment()
#convertLibs(df)
# Keep only 5' ends of reads
#convertLibs(df, method = "5prime")
sortPerGroup Sort a GRangesList
Description
A faster, more versatile reimplementation of sort.GenomicRanges for GRangesList, needed since
the original works poorly for more than 10k groups. This function sorts each group, where "+"
strands are increasing by starts and "-" strands are decreasing by ends.
Usage
sortPerGroup(grl, ignore.strand = FALSE, quick.rev = FALSE)
Arguments
grl a GRangesList
ignore.strand a boolean, (default FALSE): should minus strands be sorted from highest to
lowest ends. If TRUE: from lowest to highest ends.
quick.rev default: FALSE, if TRUE, given that you know all ranges are sorted from min
to max for both strands, it will only reverse coordinates for minus strand groups,
and only if they are in increasing order. Much quicker
320 splitIn3Tx
Details
Note: will not work if groups have equal names.
Value
an equally named GRangesList, where each group is sorted within group.
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(14, 7), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(1, 4), c(3, 9)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
sortPerGroup(grl)
splitIn3Tx Create binned coverage of transcripts, split into the 3 parts.
Description
The 3 parts of transcripts are the leaders, the cds’ and trailers. Per transcript part, bin them all to
windowSize (default 100), and make a data.table, rows are positions, useful for plotting with ORFik
and ggplot2.
Usage
splitIn3Tx(
leaders,
cds,
trailers,
reads,
windowSize = 100,
fraction = "1",
weight = "score",
is.sorted = FALSE,
drop.zero.dt = FALSE,
BPPARAM = BiocParallel::SerialParam()
)
stageNames 321
Arguments
leaders a GRangesList of leaders (5’ UTRs)
cds a GRangesList of coding sequences
trailers a GRangesList of trailers (3’ UTRs)
reads GRanges or GAlignment of reads
windowSize an integer (100), size of windows (columns). All genes with region smaller than
this size are filter out for metacoverage.
fraction a character (1), info on reads (which read length, or which type (RNA seq)) (row
names)
weight (default: ’score’), if defined a character name of valid meta column in subject.
GRanges("chr1", 1, "+", score = 5), would mean score column tells that this
alignment region was found 5 times. ORFik ofst, bedoc and .bedo files contains
a score column like this. As do CAGEr CAGE files and many other package
formats. You can also assign a score column manually.
is.sorted logical (FALSE), is grl sorted. That is + strand groups in increasing ranges
(1,2,3), and - strand groups in decreasing ranges (3,2,1)
drop.zero.dt logical FALSE, if TRUE and as.data.table is TRUE, remove all 0 count posi-
tions. This greatly speeds up and most importantly, greatly reduces memory
usage. Will not change any plots, unless 0 positions are used in some sense.
(mean, median, zscore coverage will only scale differently)
BPPARAM how many cores/threads to use? default: bpparam()
Value
a data.table with columns position, score
stageNames Get stage name variants
Description
Used to standardize nomeclature for experiments.
Example: Find timepoints 2 hours, 4 hours etc. Example: If using zebrafish stages as TRUE, 64Cell
stage is same as 2 hours post fertilization, so all 2hpf will be converted to 64Cell etc.
Usage
stageNames(zebrafish.stages = FALSE)
Arguments
zebrafish.stages
logical, FALSE. If true, convert time points to stages.
322 STAR.align.folder
Value
a data.table with 2 columns, the main name, and all name variants of the main name in second
column as a list.
References
https://www.mbl.edu/zebrafish/files/2013/03/Kimmel_stagingseries1.pdf
See Also
Other experiment_naming: batchNames(), cellLineNames(), cellTypeNames(), conditionNames(),
fractionNames(), inhibitorNames(), libNames(), mainNames(), repNames(), tissueNames()
STAR.align.folder Align all libraries in folder with STAR
Description
Does either all files as paired end or single end, so if you have mix, split them in two different
folders.
If STAR halts at .... loading genome, it means the STAR index was aborted early, then you need to
run: STAR.remove.crashed.genome(), with the genome that crashed, and rerun.
Usage
STAR.align.folder(
input.dir,
output.dir,
index.dir,
star.path = STAR.install(),
fastp = install.fastp(),
paired.end = FALSE,
steps = "tr-ge",
adapter.sequence = "auto",
quality.filtering = FALSE,
min.length = 20,
mismatches = 3,
trim.front = 0,
max.multimap = 10,
alignment.type = "Local",
allow.introns = TRUE,
max.cpus = min(90, BiocParallel::bpparam()$workers),
wait = TRUE,
include.subfolders = "n",
resume = NULL,
multiQC = TRUE,
keep.contaminants = FALSE,
STAR.align.folder 323
keep.unaligned.genome = FALSE,
script.folder = system.file("STAR_Aligner", "RNA_Align_pipeline_folder.sh", package =
"ORFik"),
script.single = system.file("STAR_Aligner", "RNA_Align_pipeline.sh", package = "ORFik")
)
Arguments
input.dir path to fast files to align, the valid input files will be search for from formats:
(".fasta", ".fastq", ".fq", or ".fa") with or without compression of .gz. Also ei-
ther paired end or single end reads. Pairs will automatically be detected from
similarity of naming, separated by something as .1 and .2 in the end. If files
are renamed, where pairs are not similarily named, this process will fail to find
correct pairs!
output.dir directory to save indices, default: paste0(dirname(arguments[1]), "/STAR_index/"),
where arguments is the arguments input for this function.
index.dir path to STAR index folder. Path returned from ORFik function STAR.index,
when you created the index folders.
star.path path to STAR, default: STAR.install(), if you don’t have STAR installed at de-
fault location, it will install it there, set path to a runnable star if you already
have it.
fastp path to fastp trimmer, default: install.fastp(), if you have it somewhere else al-
ready installed, give the path. Only works for unix (linux or Mac OS), if not on
unix, use your favorite trimmer and give the output files from that trimmer as
input.dir here.
paired.end a logical: default FALSE, alternative TRUE. If TRUE, will auto detect pairs by
names. Can not be a combination of both TRUE and FALSE!
If running in folder mode: The folder must then contain an even number of files
and they must be named with the same prefix and sufix of either _1 and _2, 1 and
2, etc. If SRR numbers are used, it will start on lowest and match with second
lowest etc.
steps a character, default: "tr-ge", trimming then genome alignment
steps of depletion and alignment wanted: The posible candidates you can use
are:
• tr : trim reads
• co : contamination merged depletion
• ph : phix depletion
• rR : rrna depletion
• nc : ncrna depletion
• tR : trna depletion (Mature tRNA, so no intron checks done)
• ge : genome alignment
• all: run steps: "tr-co-ge" or "tr-ph-rR-nc-tR-ge", depending on if you have
merged contaminants or not
324 STAR.align.folder
If not "all", a subset of these ("tr-co-ph-rR-nc-tR-ge")
If co (merged contaminants) is used, non of the specific contaminants can be
specified, since they should be a subset of co.
The step where you align to the genome is usually always included, unless
you are doing pure contaminant analysis or only trimming. For Ribo-seq and
TCP(RCP-seq) you should do rR (ribosomal RNA depletion), so when you made
the STAR index you need the rRNA step, either use rRNA from .gtf or manual
download. (usually just download a Silva rRNA database for SSU&LSU at:
https://www.arb-silva.de/) for your species.
adapter.sequence
character, default: "auto". Auto detect adapter using fastp adapter auto detec-
tion, checking first 1.5M reads. (Auto detection of adapter will not work 100%
of the time (if the library is of low quality), then you must rerun this function
with specified adapter from fastp adapter analysis. , using FASTQC or other
adapter detection tools, else alignment will most likely fail!). If already trimmed
or trimming not wanted: adapter.sequence = "disable" .You can manually assign
adapter like: "ATCTCGTATGCCGTCTTCTGCTTG" or "AAAAAAAAAAAAA".
You can also specify one of the three presets:
• illumina (TrueSeq ~75/100 bp sequencing): AGATCGGAAGAGC
• small_RNA (standard for ~50 bp sequencing): TGGAATTCTCGG
• nextera: CTGTCTCTTATA
Paired end auto detection uses overlap sequence of pairs, to use the slower more
secure paired end adapter detection, specify as: "autoPE".
quality.filtering
logical, default FALSE. Not needed for modern library prep of RNA-seq, Ribo-
seq etc (usually < ~ 0.5 If you are aligning bad quality data, set this to TRUE.
These filters will then be applied (default of fastp), filter if:
• Number of N bases in read: > 5
• Read quality: > 40% of bases in the read are <Q15
min.length 20, minimum length of aligned read without mismatches to pass filter. Anything
under 20 is dangerous, as chance of random hits will become high!
mismatches 3, max non matched bases. Excludes soft-clipping, this only filters reads that
have defined mismatches in STAR. Only applies for genome alignment step.
trim.front 0, default trim 0 bases 5’. For Ribo-seq use default 0. Ignored if tr (trim) is not
one of the arguments in "steps"
max.multimap numeric, default 10. If a read maps to more locations than specified, will skip
the read. Set to 1 to only get unique mapping reads. Only applies for genome
alignment step. The depletions are allowing for multimapping.
alignment.type default: "Local": standard local alignment with soft-clipping allowed, "End-
ToEnd" (global): force end-to-end read alignment, does not soft-clip.
allow.introns logical, default TRUE. Allow large gaps of N in reads during genome alignment,
if FALSE: sets –alignIntronMax to 1 (no introns). NOTE: You will still get some
spliced reads if you assigned a gtf at the index step.
STAR.align.folder 325
max.cpus integer, default: min(90, BiocParallel:::bpparam()$workers), number of
threads to use. Default is minimum of 90 and maximum cores - 2. So if you
have 8 cores it will use 6.
wait a logical (not NA) indicating whether the R interpreter should wait for the com-
mand to finish, or run it asynchronously. This will be ignored (and the inter-
preter will always wait) if intern = TRUE. When running the command asyn-
chronously, no output will be displayed on the Rgui console in Windows (it will
be dropped, instead).
include.subfolders
"n" (no), do recursive search downwards for fast files if "y".
resume default: NULL, continue from step, lets say steps are "tr-ph-ge": (trim, phix
depletion, genome alignment) and resume is "ge", you will then use the assumed
already trimmed and phix depleted data and start at genome alignment, useful if
something crashed. Like if you specified wrong STAR version, but the trimming
step was completed. Resume mode can only run 1 step at the time.
multiQC logical, default TRUE. Do mutliQC comparison of STAR alignment between all
the samples. Outputted in aligned/LOGS folder. See ?STAR.multiQC
keep.contaminants
logical, default FALSE. Create and keep contaminant aligning bam files, de-
fault is to only keep unaliged fastq reads, which will be further processed in
"ge" genome alignment step. Useful if you want to do further processing on
contaminants, like specific coverage of specific tRNAs etc.
keep.unaligned.genome
logical, default FALSE. Create and keep reads that did not align at the genome
alignment step, default is to only keep the aliged bam file. Useful if you want to
do further processing on plasmids/custom sequences.
script.folder location of STAR index script, default internal ORFik file. You can change it
and give your own if you need special alignments.
script.single location of STAR single file alignment script, default internal ORFik file. You
can change it and give your own if you need special alignments.
Details
Can only run on unix systems (Linux, Mac and WSL (Windows Subsystem Linux)), and requires a
minimum of 30GB memory on genomes like human, rat, zebrafish etc.
If for some reason the internal STAR alignment bash script will not work for you, like if you want
more customization of the STAR/fastp arguments. You can copy the internal alignment script, edit
it and give that as the script used for this function.
The trimmer used is fastp (the fastest I could find), also works on (Linux, Mac and WSL (Windows
Subsystem Linux)). If you want to use your own trimmer set file1/file2 to the location of the
trimmed files from your program.
A note on trimming from creator of STAR about trimming: "adapter trimming it definitely needed
for short RNA sequencing. For long RNA-seq, I would agree with Devon that in most cases adapter
trimming is not advantageous, since, by default, STAR performs local (not end-to-end) alignment,
i.e. it auto-trims." So trimming can be skipped for longer reads.
326 STAR.align.single
Value
output.dir, can be used as as input in ORFik::create.experiment
See Also
Other STAR: STAR.align.single(), STAR.allsteps.multiQC(), STAR.index(), STAR.install(),
STAR.multiQC(), STAR.remove.crashed.genome(), getGenomeAndAnnotation(), install.fastp()
Examples
# First specify directories wanted
annotation.dir <- "~/Bio_data/references/Human"
fastq.input.dir <- "~/Bio_data/raw_data/Ribo_seq_subtelny/"
bam.output.dir <- "~/Bio_data/processed_data/Ribo_seq_subtelny_2014/"
## Download some SRA data and metadata
# info <- download.SRA.metadata("DRR041459", fastq.input.dir)
# download.SRA(info, fastq.input.dir, rename = FALSE)
## Now align 2 different ways, without and with contaminant depletion
## No contaminant depletion:
# annotation <- getGenomeAndAnnotation("Homo sapiens", annotation.dir)
# index <- STAR.index(annotation)
# STAR.align.folder(fastq.input.dir, bam.output.dir,
# index, paired.end = FALSE)
## All contaminants merged:
# annotation <- getGenomeAndAnnotation(
# organism = "Homo_sapiens",
# phix = TRUE, ncRNA = TRUE, tRNA = TRUE, rRNA = TRUE,
# output.dir = annotation.dir
# )
# index <- STAR.index(annotation)
# STAR.align.folder(fastq.input.dir, bam.output.dir,
# index, paired.end = FALSE,
# steps = "tr-ge")
STAR.align.single Align single or paired end pair with STAR
Description
Given a single NGS fastq/fasta library, or a paired setup of 2 mated libraries. Run either combination
of fastq trimming, contamination removal and genome alignment. Works for (Linux, Mac and WSL
(Windows Subsystem Linux))
STAR.align.single 327
Usage
STAR.align.single(
file1,
file2 = NULL,
output.dir,
index.dir,
star.path = STAR.install(),
fastp = install.fastp(),
steps = "tr-ge",
adapter.sequence = "auto",
quality.filtering = FALSE,
min.length = 20,
mismatches = 3,
trim.front = 0,
max.multimap = 10,
alignment.type = "Local",
allow.introns = TRUE,
max.cpus = min(90, BiocParallel::bpparam()$workers),
wait = TRUE,
resume = NULL,
keep.contaminants = FALSE,
keep.unaligned.genome = FALSE,
script.single = system.file("STAR_Aligner", "RNA_Align_pipeline.sh", package = "ORFik")
)
Arguments
file1 library file, if paired must be R1 file. Allowed formats are: (.fasta, .fastq, .fq,
or.fa) with or without compression of .gz. This filename usually contains a suffix
of .1
file2 default NULL, set if paired end to R2 file. Allowed formats are: (.fasta, .fastq,
.fq, or.fa) with or without compression of .gz. This filename usually contains a
suffix of .2
output.dir directory to save indices, default: paste0(dirname(arguments[1]), "/STAR_index/"),
where arguments is the arguments input for this function.
index.dir path to STAR index folder. Path returned from ORFik function STAR.index,
when you created the index folders.
star.path path to STAR, default: STAR.install(), if you don’t have STAR installed at de-
fault location, it will install it there, set path to a runnable star if you already
have it.
fastp path to fastp trimmer, default: install.fastp(), if you have it somewhere else al-
ready installed, give the path. Only works for unix (linux or Mac OS), if not on
unix, use your favorite trimmer and give the output files from that trimmer as
input.dir here.
steps a character, default: "tr-ge", trimming then genome alignment
steps of depletion and alignment wanted: The posible candidates you can use
328 STAR.align.single
are:
• tr : trim reads
• co : contamination merged depletion
• ph : phix depletion
• rR : rrna depletion
• nc : ncrna depletion
• tR : trna depletion (Mature tRNA, so no intron checks done)
• ge : genome alignment
• all: run steps: "tr-co-ge" or "tr-ph-rR-nc-tR-ge", depending on if you have
merged contaminants or not
If not "all", a subset of these ("tr-co-ph-rR-nc-tR-ge")
If co (merged contaminants) is used, non of the specific contaminants can be
specified, since they should be a subset of co.
The step where you align to the genome is usually always included, unless
you are doing pure contaminant analysis or only trimming. For Ribo-seq and
TCP(RCP-seq) you should do rR (ribosomal RNA depletion), so when you made
the STAR index you need the rRNA step, either use rRNA from .gtf or manual
download. (usually just download a Silva rRNA database for SSU&LSU at:
https://www.arb-silva.de/) for your species.
adapter.sequence
character, default: "auto". Auto detect adapter using fastp adapter auto detec-
tion, checking first 1.5M reads. (Auto detection of adapter will not work 100%
of the time (if the library is of low quality), then you must rerun this function
with specified adapter from fastp adapter analysis. , using FASTQC or other
adapter detection tools, else alignment will most likely fail!). If already trimmed
or trimming not wanted: adapter.sequence = "disable" .You can manually assign
adapter like: "ATCTCGTATGCCGTCTTCTGCTTG" or "AAAAAAAAAAAAA".
You can also specify one of the three presets:
• illumina (TrueSeq ~75/100 bp sequencing): AGATCGGAAGAGC
• small_RNA (standard for ~50 bp sequencing): TGGAATTCTCGG
• nextera: CTGTCTCTTATA
Paired end auto detection uses overlap sequence of pairs, to use the slower more
secure paired end adapter detection, specify as: "autoPE".
quality.filtering
logical, default FALSE. Not needed for modern library prep of RNA-seq, Ribo-
seq etc (usually < ~ 0.5 If you are aligning bad quality data, set this to TRUE.
These filters will then be applied (default of fastp), filter if:
• Number of N bases in read: > 5
• Read quality: > 40% of bases in the read are <Q15
min.length 20, minimum length of aligned read without mismatches to pass filter. Anything
under 20 is dangerous, as chance of random hits will become high!
mismatches 3, max non matched bases. Excludes soft-clipping, this only filters reads that
have defined mismatches in STAR. Only applies for genome alignment step.
STAR.align.single 329
trim.front 0, default trim 0 bases 5’. For Ribo-seq use default 0. Ignored if tr (trim) is not
one of the arguments in "steps"
max.multimap numeric, default 10. If a read maps to more locations than specified, will skip
the read. Set to 1 to only get unique mapping reads. Only applies for genome
alignment step. The depletions are allowing for multimapping.
alignment.type default: "Local": standard local alignment with soft-clipping allowed, "End-
ToEnd" (global): force end-to-end read alignment, does not soft-clip.
allow.introns logical, default TRUE. Allow large gaps of N in reads during genome alignment,
if FALSE: sets –alignIntronMax to 1 (no introns). NOTE: You will still get some
spliced reads if you assigned a gtf at the index step.
max.cpus integer, default: min(90, BiocParallel:::bpparam()$workers), number of
threads to use. Default is minimum of 90 and maximum cores - 2. So if you
have 8 cores it will use 6.
wait a logical (not NA) indicating whether the R interpreter should wait for the com-
mand to finish, or run it asynchronously. This will be ignored (and the inter-
preter will always wait) if intern = TRUE. When running the command asyn-
chronously, no output will be displayed on the Rgui console in Windows (it will
be dropped, instead).
resume default: NULL, continue from step, lets say steps are "tr-ph-ge": (trim, phix
depletion, genome alignment) and resume is "ge", you will then use the assumed
already trimmed and phix depleted data and start at genome alignment, useful if
something crashed. Like if you specified wrong STAR version, but the trimming
step was completed. Resume mode can only run 1 step at the time.
keep.contaminants
logical, default FALSE. Create and keep contaminant aligning bam files, de-
fault is to only keep unaliged fastq reads, which will be further processed in
"ge" genome alignment step. Useful if you want to do further processing on
contaminants, like specific coverage of specific tRNAs etc.
keep.unaligned.genome
logical, default FALSE. Create and keep reads that did not align at the genome
alignment step, default is to only keep the aliged bam file. Useful if you want to
do further processing on plasmids/custom sequences.
script.single location of STAR single file alignment script, default internal ORFik file. You
can change it and give your own if you need special alignments.
Details
Can only run on unix systems (Linux, Mac and WSL (Windows Subsystem Linux)), and requires a
minimum of 30GB memory on genomes like human, rat, zebrafish etc.
If for some reason the internal STAR alignment bash script will not work for you, like if you want
more customization of the STAR/fastp arguments. You can copy the internal alignment script, edit
it and give that as the script used for this function.
The trimmer used is fastp (the fastest I could find), also works on (Linux, Mac and WSL (Windows
Subsystem Linux)). If you want to use your own trimmer set file1/file2 to the location of the
trimmed files from your program.
A note on trimming from creator of STAR about trimming: "adapter trimming it definitely needed
330 STAR.allsteps.multiQC
for short RNA sequencing. For long RNA-seq, I would agree with Devon that in most cases adapter
trimming is not advantageous, since, by default, STAR performs local (not end-to-end) alignment,
i.e. it auto-trims." So trimming can be skipped for longer reads.
Value
output.dir, can be used as as input in ORFik::create.experiment
See Also
Other STAR: STAR.align.folder(), STAR.allsteps.multiQC(), STAR.index(), STAR.install(),
STAR.multiQC(), STAR.remove.crashed.genome(), getGenomeAndAnnotation(), install.fastp()
Examples
## Specify output libraries:
output.dir <- "/Bio_data/references/Human"
bam.dir <- "data/processed/human_rna_seq"
# arguments <- getGenomeAndAnnotation("Homo sapiens", output.dir)
# index <- STAR.index(arguments, output.dir)
# STAR.align.single("data/raw_data/human_rna_seq/file1.bam", bam.dir,
# index)
STAR.allsteps.multiQC Create STAR multiQC plot and table
Description
Takes a folder with multiple Log.final.out files from STAR, and create a multiQC report. This is
automatically run with STAR.align.folder function.
Usage
STAR.allsteps.multiQC(folder, steps = "auto", plot.ext = ".pdf")
Arguments
folder path to main output folder of STAR run. The folder that contains /aligned/,
"/trim/, "contaminants_depletion" etc. To find the LOGS folders in, to use for
summarized statistics.
steps a character, default "auto". Find which steps you did. If manual, a combination
of "tr-co-ge". See STAR alignment functions for description.
plot.ext character, default ".pdf". Which format to save QC plot. Alternative: ".png".
Value
data.table of main statistics, plots and data saved to disc. Named: "/00_STAR_LOG_plot.pdf" and
"/00_STAR_LOG_table.csv"
STAR.index 331
See Also
Other STAR: STAR.align.folder(), STAR.align.single(), STAR.index(), STAR.install(),
STAR.multiQC(), STAR.remove.crashed.genome(), getGenomeAndAnnotation(), install.fastp()
STAR.index Create STAR genome index
Description
Used as reference when aligning data
Get genome and gtf by running getGenomeAndFasta()
Usage
STAR.index(
arguments,
output.dir = paste0(dirname(arguments[1]), "/STAR_index/"),
star.path = STAR.install(),
max.cpus = min(90, BiocParallel::bpparam()$workers),
max.ram = 30,
SAsparse = 1,
tmpDirStar = "-",
wait = TRUE,
remake = FALSE,
script = system.file("STAR_Aligner", "STAR_MAKE_INDEX.sh", package = "ORFik"),
notify_load_existing = TRUE
)
Arguments
arguments a named character vector containing paths wanted to use for index creation.
They must be named correctly: names must be a subset of: c("gtf", "genome",
"contaminants", "phix", "rRNA", "tRNA","ncRNA")
output.dir directory to save indices, default: paste0(dirname(arguments[1]), "/STAR_index/"),
where arguments is the arguments input for this function.
star.path path to STAR, default: STAR.install(), if you don’t have STAR installed at de-
fault location, it will install it there, set path to a runnable star if you already
have it.
max.cpus integer, default: min(90, BiocParallel:::bpparam()$workers), number of
threads to use. Default is minimum of 90 and maximum cores - 2. So if you
have 8 cores it will use 6.
max.ram integer, default 30, in Giga Bytes (GB). Maximum amount of RAM allowed for
STAR limitGenomeGenerateRAM argument. RULE: idealy 10x genome size,
but do not set too close to machine limit. Default fits well for human genome
size (3 GB * 10 = 30 GB)
332 STAR.index
SAsparse int > 0, default 1. If you do not have at least 64GB RAM, you might need to
set this to 2. suffux array sparsity, i.e. distance between indices: use bigger
numbers to decrease needed RAM at the cost of mapping speed reduction. Only
applies to genome, not conaminants.
tmpDirStar character, default "-". STAR automatic temp folder creation, deleted when done.
The directory can not exists, as a safety STAR must make it!. If you are on a
NFS file share drive, and you have a non NFS tmp dir, set this to tempfile() or
the manually specified folder to get a considerable speedup!
wait a logical (not NA) indicating whether the R interpreter should wait for the com-
mand to finish, or run it asynchronously. This will be ignored (and the inter-
preter will always wait) if intern = TRUE. When running the command asyn-
chronously, no output will be displayed on the Rgui console in Windows (it will
be dropped, instead).
remake logical, default: FALSE, if TRUE remake everything specified
script location of STAR index script, default internal ORFik file. You can change it
and give your own if you need special alignments.
notify_load_existing
logical, default TRUE. If annotation exists (defined as: locally (a file called
outputs.rds) exists in outputdir), print a small message notifying the user it is
not redownloading. Set to FALSE, if this is not wanted
Details
Can only run on unix systems (Linux and Mac), and requires minimum 30GB memory on genomes
like human, rat, zebrafish etc.
If for some reason the internal STAR index bash script will not work for you, like if you have a very
small genome. You can copy the internal index script, edit it and give that as the Index script used
for this function. It is recommended to run through the RStudio local job tab, to give full info about
the run. The system console will not stall, as can happen in happen in normal RStudio console.
Value
output.dir, can be used as as input for STAR.align..
See Also
Other STAR: STAR.align.folder(), STAR.align.single(), STAR.allsteps.multiQC(), STAR.install(),
STAR.multiQC(), STAR.remove.crashed.genome(), getGenomeAndAnnotation(), install.fastp()
Examples
## Manual way, specify all paths yourself.
#arguments <- c(path.GTF, path.genome, path.phix, path.rrna, path.trna, path.ncrna)
#names(arguments) <- c("gtf", "genome", "phix", "rRNA", "tRNA","ncRNA")
#STAR.index(arguments, "output.dir")
## Or use ORFik way:
output.dir <- "/Bio_data/references/Human"
# arguments <- getGenomeAndAnnotation("Homo sapiens", output.dir)
STAR.install 333
# STAR.index(arguments, output.dir)
STAR.install Download and prepare STAR
Description
Will not run "make", only use precompiled STAR file.
Can only run on unix systems (Linux and Mac), and requires minimum 30GB memory on genomes
like human, rat, zebrafish etc.
Usage
STAR.install(folder = "~/bin", version = "2.7.4a")
Arguments
folder path to folder for download, fille will be named "STAR-version", where version
is version wanted.
version default "2.7.4a"
Details
ORFik for now only uses precompiled STAR binaries, so if you already have a STAR version it
is adviced to redownload the same version, since STAR genome indices usually does not work
between STAR versions.
Value
path to runnable STAR
References
https://www.ncbi.nlm.nih.gov/pubmed/23104886
See Also
Other STAR: STAR.align.folder(), STAR.align.single(), STAR.allsteps.multiQC(), STAR.index(),
STAR.multiQC(), STAR.remove.crashed.genome(), getGenomeAndAnnotation(), install.fastp()
Examples
## Default folder install:
#STAR.install()
## Manual set folder:
folder <- "/I/WANT/IT/HERE"
#STAR.install(folder, version = "2.7.4a")
334 STAR.remove.crashed.genome
STAR.multiQC Create STAR multiQC plot and table
Description
Takes a folder with multiple Log.final.out files from STAR, and create a multiQC report
Usage
STAR.multiQC(folder, type = "aligned", plot.ext = ".pdf")
Arguments
folder path to LOGS folder of ORFik STAR runs. Can also be the path to the aligned/
(parent directory of LOGS), then it will move into LOG from there. Only if no
files with pattern Log.final.out are found in parent directory. If no LOGS folder
is found it can check for a folder /aligned/LOGS/ so to go 2 folders down.
type a character path, default "aligned". Which subfolder to check for. If you want
log files for contamination do type = "contaminants_depletion"
plot.ext character, default ".pdf". Which format to save QC plot. Alternative: ".png".
Value
a data.table with all information from STAR runs, plot and data saved to disc. Named: "/00_STAR_LOG_plot.pdf"
and "/00_STAR_LOG_table.csv"
See Also
Other STAR: STAR.align.folder(), STAR.align.single(), STAR.allsteps.multiQC(), STAR.index(),
STAR.install(), STAR.remove.crashed.genome(), getGenomeAndAnnotation(), install.fastp()
STAR.remove.crashed.genome
Remove crashed STAR genome
Description
This happens if you abort STAR run early, and it halts at: ..... loading genome
Usage
STAR.remove.crashed.genome(index.path, star.path = STAR.install())
startCodons 335
Arguments
index.path path to index folder of genome
star.path path to STAR, default: STAR.install(), if you don’t have STAR installed at de-
fault location, it will install it there, set path to a runnable star if you already
have it.
Value
return value from system call, 0 if all good.
See Also
Other STAR: STAR.align.folder(), STAR.align.single(), STAR.allsteps.multiQC(), STAR.index(),
STAR.install(), STAR.multiQC(), getGenomeAndAnnotation(), install.fastp()
Examples
index.path = "/home/data/human_GRCh38/STAR_INDEX/genomeDir/"
# STAR.remove.crashed.genome(index.path = index.path)
## If you have the index argument from STAR.index function:
# index.path <- STAR.index()
# STAR.remove.crashed.genome(file.path(index.path, "genomeDir"))
# STAR.remove.crashed.genome(file.path(index.path, "contaminants_genomeDir"))
startCodons Get the Start codons(3 bases) from a GRangesList of orfs grouped by
orfs
Description
In ATGTTTTGA, get the positions ATG. It takes care of exons boundaries, with exons < 3 length.
Usage
startCodons(grl, is.sorted = FALSE)
Arguments
grl a GRangesList object
is.sorted a boolean, a speedup if you know the ranges are sorted
Value
a GRangesList of start codons, since they might be split on exons
336 startDefinition
See Also
Other ORFHelpers: defineTrailer(), longestORFs(), mapToGRanges(), orfID(), startSites(),
stopCodons(), stopSites(), txNames(), uniqueGroups(), uniqueOrder()
Examples
gr_plus <- GRanges(seqnames = "chr1",
ranges = IRanges(c(7, 14), width = 3),
strand = "+")
gr_minus <- GRanges(seqnames = "chr2",
ranges = IRanges(c(4, 1), c(9, 3)),
strand = "-")
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
startCodons(grl, is.sorted = FALSE)
startDefinition Returns start codon definitions
Description
According to: <http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/ index.cgi?chapter=tgencodes#SG1>
ncbi genetic code number for translation. This version is a cleaned up version, unknown indices
removed.
Usage
startDefinition(transl_table)
Arguments
transl_table numeric. NCBI genetic code number for translation.
Value
A string of START sites separatd with "|".
See Also
Other findORFs: findMapORFs(), findORFs(), findORFsFasta(), findUORFs(), stopDefinition()
Examples
startDefinition
startDefinition(1)
startRegion 337
startRegion Start region as GRangesList
Description
Get the start region of each ORF. If you want the start codon only, set upstream = 0 or just use
startCodons. Standard is 2 upstream and 2 downstream, a width 5 window centered at start site.
since p-shifting is not 100 usually the reads from the start site.
Usage
startRegion(grl, tx = NULL, is.sorted = TRUE, upstream = 2L, downstream = 2L)
Arguments
grl a GRangesList object with usually either leaders, cds’, 3’ utrs or ORFs
tx default NULL, a GRangesList of transcripts or (container region), names of tx
must contain all grl names. The names of grl can also be the ORFik orf names.
that is "txName_id"
is.sorted logical (TRUE), is grl sorted.
upstream an integer (2), relative region to get upstream from.
downstream an integer (2), relative region to get downstream from
Details
If tx is null, then upstream will be forced to 0 and downstream to a maximum of grl width (3’ UTR
end for mRNAs). Since there is no reference for splicing.
Value
a GRanges, or GRangesList object if any group had > 1 exon.
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss() , fpkm(), fpkm_calc(), fractionLength(),
initiationScore(), insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(),
orfScore(), rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegionCoverage(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
## ORF start region
orf <- GRangesList(tx1 = GRanges("1", 200:300, "+"))
tx <- GRangesList(tx1 = GRanges("1",
IRanges(c(100, 200), c(195, 400)), "+"))
startRegion(orf, tx, upstream = 6, downstream = 6)
338 startRegionCoverage
## 2nd codon of ORF
startRegion(orf, tx, upstream = -3, downstream = 6)
startRegionCoverage Start region coverage
Description
Get the number of reads in the start region of each ORF. If you want the start codon coverage only,
set upstream = 0. Standard is 2 upstream and 2 downstream, a width 5 window centered at start site.
since p-shifting is not 100 start site.
Usage
startRegionCoverage(
grl,
RFP,
tx = NULL,
is.sorted = TRUE,
upstream = 2L,
downstream = 2L,
weight = 1L
)
Arguments
grl a GRangesList object with usually either leaders, cds’, 3’ utrs or ORFs
RFP ribo seq reads as GAlignments, GRanges or GRangesList object
tx default NULL, a GRangesList of transcripts or (container region), names of tx
must contain all grl names. The names of grl can also be the ORFik orf names.
that is "txName_id"
is.sorted logical (TRUE), is grl sorted.
upstream an integer (2), relative region to get upstream from.
downstream an integer (2), relative region to get downstream from
weight a vector (default: 1L, if 1L it is identical to countOverlaps()), if single number
(!= 1), it applies for all, if more than one must be equal size of ’reads’. else it
must be the string name of a defined meta column in subject "reads", that gives
number of times a read was found. GRanges("chr1", 1, "+", score = 5), would
mean "score" column tells that this alignment region was found 5 times.
Details
If tx is null, then upstream will be force to 0 and downstream to a maximum of grl width. Since
there is no reference for splicing.
startRegionString 339
Value
a numeric vector of counts
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss() , fpkm(), fpkm_calc(), fractionLength(),
initiationScore(), insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(),
orfScore(), rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(),
stopRegion(), subsetCoverage(), translationalEff()
Examples
ORF <- GRanges(seqnames = "1",
ranges = IRanges(21, 40),
strand = "+")
names(ORF) <- c("tx1")
grl <- GRangesList(tx1 = ORF)
tx <- extendLeaders(grl, 20)
# 1 width p-shifted reads
reads <- GRanges("1", IRanges(c(21, 23, 50, 50, 50, 53, 53, 56, 59),
width = 1), "+")
score(reads) <- 28 # original width
startRegionCoverage(grl, reads, tx)
startRegionString Get start region as DNA-strings per GRanges group
Description
One window per start site, if upstream and downstream are both 0, then only the startsite is returned.
Usage
startRegionString(grl, tx, faFile, upstream = 20, downstream = 20)
Arguments
grl a GRangesList of ranges to find regions in.
tx a GRangesList of transcripts or (container region), names of tx must contain
all gr names. The names of gr can also be the ORFik orf names. that is "tx-
Name_id".
faFile FaFile, BSgenome, fasta/index file path or an ORFik experiment. This file is
usually used to find the transcript sequences from some GRangesList.
upstream an integer, default (0), relative region to get upstream from.
downstream an integer, default (0), relative region to get downstream from
340 startSites
Value
a character vector of start regions
startSites Get the start sites from a GRangesList of orfs grouped by orfs
Description
In ATGTTTTGG, get the position of the A.
Usage
startSites(grl, asGR = FALSE, keep.names = FALSE, is.sorted = FALSE)
Arguments
grl a GRangesList object
asGR a boolean, return as GRanges object
keep.names a logical (FALSE), keep names of input.
is.sorted a speedup, if you know the ranges are sorted
Value
if asGR is False, a vector, if True a GRanges object
See Also
Other ORFHelpers: defineTrailer(), longestORFs(), mapToGRanges(), orfID(), startCodons(),
stopCodons(), stopSites(), txNames(), uniqueGroups(), uniqueOrder()
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
startSites(grl, is.sorted = FALSE)
stopCodons 341
stopCodons Get the Stop codons (3 bases) from a GRangesList of orfs grouped by
orfs
Description
In ATGTTTTGA, get the positions TGA. It takes care of exons boundaries, with exons < 3 length.
Usage
stopCodons(grl, is.sorted = FALSE)
Arguments
grl a GRangesList object
is.sorted a boolean, a speedup if you know the ranges are sorted
Value
a GRangesList of stop codons, since they might be split on exons
See Also
Other ORFHelpers: defineTrailer(), longestORFs(), mapToGRanges(), orfID(), startCodons(),
startSites(), stopSites(), txNames(), uniqueGroups(), uniqueOrder()
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
stopCodons(grl, is.sorted = FALSE)
342 stopRegion
stopDefinition Returns stop codon definitions
Description
According to: <http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/ index.cgi?chapter=tgencodes#SG1>
ncbi genetic code number for translation. This version is a cleaned up version, unknown indices
removed.
Usage
stopDefinition(transl_table)
Arguments
transl_table numeric. NCBI genetic code number for translation.
Value
A string of STOP sites separatd with "|".
See Also
Other findORFs: findMapORFs(), findORFs(), findORFsFasta(), findUORFs(), startDefinition()
Examples
stopDefinition
stopDefinition(1)
stopRegion Stop region as GRangesList
Description
Get the stop region of each ORF / region. If you want the stop codon only, set downstream = 0 or
just use stopCodons. Standard is 2 upstream and 2 downstream, a width 5 window centered at stop
site.
Usage
stopRegion(grl, tx = NULL, is.sorted = TRUE, upstream = 2L, downstream = 2L)
stopSites 343
Arguments
grl a GRangesList object with usually either leaders, cds’, 3’ utrs or ORFs
tx default NULL, a GRangesList of transcripts or (container region), names of tx
must contain all grl names. The names of grl can also be the ORFik orf names.
that is "txName_id"
is.sorted logical (TRUE), is grl sorted.
upstream an integer (2), relative region to get upstream from.
downstream an integer (2), relative region to get downstream from
Details
If tx is null, then downstream will be forced to 0 and upstream to a minimum of -grl width (to the
TSS). . Since there is no reference for splicing.
Value
a GRanges, or GRangesList object if any group had > 1 exon.
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss() , fpkm(), fpkm_calc(), fractionLength(),
initiationScore(), insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(),
orfScore(), rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(),
startRegionCoverage(), subsetCoverage(), translationalEff()
Examples
## ORF stop region
orf <- GRangesList(tx1 = GRanges("1", 200:300, "+"))
tx <- GRangesList(tx1 = GRanges("1",
IRanges(c(100, 305), c(300, 400)), "+"))
stopRegion(orf, tx, upstream = 6, downstream = 6)
## 2nd last codon of ORF
stopRegion(orf, tx, upstream = 6, downstream = -3)
stopSites Get the stop sites from a GRangesList of orfs grouped by orfs
Description
In ATGTTTTGC, get the position of the C.
Usage
stopSites(grl, asGR = FALSE, keep.names = FALSE, is.sorted = FALSE)
344 strandBool
Arguments
grl a GRangesList object
asGR a boolean, return as GRanges object
keep.names a logical (FALSE), keep names of input.
is.sorted a speedup, if you know the ranges are sorted
Value
if asGR is False, a vector, if True a GRanges object
See Also
Other ORFHelpers: defineTrailer(), longestORFs(), mapToGRanges(), orfID(), startCodons(),
startSites(), stopCodons(), txNames(), uniqueGroups(), uniqueOrder()
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
stopSites(grl, is.sorted = FALSE)
strandBool Get logical list of strands
Description
Helper function to get a logical list of True/False, if GRangesList group have + strand = T, if - strand
= F Also checks for * strands, so a good check for bugs
Usage
strandBool(grl)
Arguments
grl a GRangesList or GRanges object
Value
a logical vector
strandMode,covRle-method 345
Examples
gr <- GRanges(Rle(c("chr2", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)),
IRanges(1:10, width = 10:1),
Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)))
strandBool(gr)
strandMode,covRle-method
strandMode covRle
Description
strandMode covRle
Usage
## S4 method for signature 'covRle'
strandMode(x)
Arguments
x a covRle object
Value
integer vector with names
strandMode,covRleList-method
strandMode covRle
Description
strandMode covRle
Usage
## S4 method for signature 'covRleList'
strandMode(x)
Arguments
x a covRle object
Value
integer vector with names
346 subsetCoverage
strandPerGroup Get list of strands per granges group
Description
Get list of strands per granges group
Usage
strandPerGroup(grl, keep.names = TRUE)
Arguments
grl a GRangesList
keep.names a boolean, keep names or not, default: (TRUE)
Value
a vector named/unnamed of characters
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
strandPerGroup(grl)
subsetCoverage Subset GRanges to get coverage.
Description
GRanges object should be beforehand tiled to size of 1. This subsetting takes account for strand.
Usage
subsetCoverage(cov, y)
Arguments
cov A coverage object from coverage()
y GRanges object for which coverage should be extracted
subsetToFrame 347
Value
numeric vector of coverage of input GRanges object
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss() , fpkm(), fpkm_calc(), fractionLength(),
initiationScore(), insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(),
orfScore(), rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(),
startRegionCoverage(), stopRegion(), translationalEff()
subsetToFrame Subset GRanges to get desired frame.
Description
Usually used for ORFs to get specific frame (0-2): frame 0, frame 1, frame 2
Usage
subsetToFrame(x, frame)
Arguments
x A tiled to size of 1 GRanges object
frame A numeric indicating which frame to extract
Details
GRanges object should be beforehand tiled to size of 1. This subsetting takes account for strand.
Value
GRanges object reduced to only first frame
Examples
subsetToFrame(GRanges("1", IRanges(1:10, width = 1), "+"), 2)
348 symbols,experiment-method
symbols Get ORFik experiment QC folder path
Description
Get ORFik experiment QC folder path
Usage
symbols(x)
Arguments
x an ORFik experiment
Value
a data.table with gene id, gene symbols and tx ids (3 columns)
symbols,experiment-method
Get ORFik experiment QC folder path
Description
Get ORFik experiment QC folder path
Usage
## S4 method for signature 'experiment'
symbols(x)
Arguments
x an ORFik experiment
Value
a character path
te.plot 349
te.plot Translational efficiency plots
Description
Create 2 TE plots of:
- Within sample (TE log2 vs mRNA fpkm) ("default")
- Between all combinations of samples (x-axis: rna1fpkm - rna2fpkm, y-axis rfp1fpkm - rfp2fpkm)
Usage
te.plot(
df.rfp,
df.rna,
output.dir = QCfolder(df.rfp),
type = c("default", "between"),
filter.rfp = 1,
filter.rna = 1,
collapse = FALSE,
plot.title = "",
plot.ext = ".pdf",
width = 6,
height = "auto"
)
Arguments
df.rfp a experiment of Ribo-seq or 80S from TCP-seq.
df.rna a experiment of RNA-seq
output.dir directory to save plots, plots will be named "TE_between.pdf" and "TE_within.pdf"
type which plots to make, default: c("default", "between"). Both plots.
filter.rfp numeric, default 1. minimum fpkm value to be included in plots
filter.rna numeric, default 1. minimum fpkm value to be included in plots
collapse a logical/character (default FALSE), if TRUE all samples within the group SAM-
PLE will be collapsed to one. If "all", all groups will be merged into 1 col-
umn called merged_all. Collapse is defined as rowSum(elements_per_group) /
ncol(elements_per_group)
plot.title title for plots, usually name of experiment etc
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg".
width numeric, default 6 (in inches)
height numeric or character, default "auto", which is: 3 + (ncol(RFP_CDS_FPKM)-2).
Else a numeric value of height (in inches)
350 te.table
Details
Ribo-seq and RNA-seq must have equal nrows, with matching samples. Only exception is if RNA-
seq is 1 single sample. Then it will use that for each of the Ribo-seq samples. Same stages,
conditions etc, with a unique pairing 1 to 1. If not you can run collapse = "all". It will then merge
all and do combined of all RNA-seq vs all Ribo-seq
Value
a data.table with TE values, fpkm and log fpkm values, library samples melted into rows with split
variable called "variable".
Examples
##
# df.rfp <- read.experiment("zf_baz14_RFP")
# df.rna <- read.experiment("zf_baz14_RNA")
# te.plot(df.rfp, df.rna)
## Collapse replicates:
# te.plot(df.rfp, df.rna, collapse = TRUE)
te.table Create a TE table
Description
Creates a data.table with 6 columns, column names are:
variable, rfp_log2, rna_log2, rna_log10, TE_log2, id
Usage
te.table(df.rfp, df.rna, filter.rfp = 1, filter.rna = 1, collapse = FALSE)
Arguments
df.rfp a experiment of usually Ribo-seq or 80S from TCP-seq. (the numerator of the
experiment, usually having a primary role)
df.rna a experiment of usually RNA-seq. (the denominator of the experiment, usually
having a normalizing function)
filter.rfp numeric, default 1. What is the minimum fpkm value?
filter.rna numeric, default 1. What is the minimum fpkm value?
collapse a logical/character (default FALSE), if TRUE all samples within the group SAM-
PLE will be collapsed to one. If "all", all groups will be merged into 1 col-
umn called merged_all. Collapse is defined as rowSum(elements_per_group) /
ncol(elements_per_group)
te_rna.plot 351
Value
a data.table with 6 columns
See Also
Other DifferentialExpression: DEG.plot.static(), DEG_model(), DTEG.analysis(), DTEG.plot(),
te_rna.plot()
Examples
df <- ORFik.template.experiment()
df.rfp <- df[df$libtype == "RFP",]
df.rna <- df[df$libtype == "RNA",]
#te.table(df.rfp, df.rna)
te_rna.plot Translational efficiency plots
Description
Create TE plot of:
- Within sample (TE log2 vs mRNA fpkm)
Usage
te_rna.plot(
dt,
output.dir = NULL,
filter.rfp = 1,
filter.rna = 1,
plot.title = "",
plot.ext = ".pdf",
width = 6,
height = "auto",
dot.size = 0.4,
xlim = c(filter.rna, filter.rna + 2.5)
)
Arguments
dt a data.table with the results from te.table
output.dir a character path, default NULL(no save), or a directory to save to a file will be
called "TE_within.pdf"
filter.rfp numeric, default 1. What is the minimum fpkm value?
filter.rna numeric, default 1. What is the minimum fpkm value?
352 tile1
plot.title title for plots, usually name of experiment etc
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg".
width numeric, default 6 (in inches)
height a numeric, width of plot in inches. Default "auto".
dot.size numeric, default 0.4, size of point dots in plot.
xlim numeric vector of length 2. X-axis limits. Default: c(filter.rna, filter.rna
+ 2.5)
Value
a ggplot object
See Also
Other DifferentialExpression: DEG.plot.static(), DEG_model(), DTEG.analysis(), DTEG.plot(),
te.table()
Examples
df <- ORFik.template.experiment()
df.rfp <- df[df$libtype == "RFP",]
df.rna <- df[df$libtype == "RNA",]
#dt <- te.table(df.rfp, df.rna)
#te_rna.plot(dt, filter.rfp = 0, filter.rna = 5, dot.size = 1)
tile1 Tile each GRangesList group to 1-base resolution.
Description
Will tile a GRangesList into single bp resolution, each group of the list will be splited by positions
of 1. Returned values are sorted as the same groups as the original GRangesList, except they are in
bp resolutions. This is not supported originally by GenomicRanges for GRangesList.
Usage
tile1(grl, sort.on.return = TRUE, matchNaming = TRUE, is.sorted = TRUE)
Arguments
grl a GRangesList object with names.
sort.on.return logical (TRUE), should the groups be sorted before return (Negative ranges
should be in decreasing order). Makes it a bit slower, but much safer for down-
stream analysis.
matchNaming logical (TRUE), should groups keep unlisted names and meta data.(This make
the list very big, for > 100K groups)
is.sorted logical (TRUE), grl is presorted (negative coordinates are decreasing). Set to
FALSE if they are not, else output will most likely be wrong!
tissueNames 353
Value
a GRangesList grouped by original group, tiled to 1. Groups with identical names will be merged.
See Also
Other ExtendGenomicRanges: asTX(), coveragePerTiling(), extendLeaders(), extendTrailers(),
reduceKeepAttr(), txSeqsFromFa(), windowPerGroup()
Examples
gr1 <- GRanges("1", ranges = IRanges(start = c(1, 10, 20),
end = c(5, 15, 25)),
strand = "+")
gr2 <- GRanges("1", ranges = IRanges(start = c(20, 30, 40),
end = c(25, 35, 45)),
strand = "+")
names(gr1) = rep("tx1_1", 3)
names(gr2) = rep("tx1_2", 3)
grl <- GRangesList(tx1_1 = gr1, tx1_2 = gr2)
tile1(grl)
tissueNames Get tissue name variants
Description
Used to standardize nomeclature for experiments.
Example: testis is main naming, but a variant is testicles. testicles will then be renamed to testis.
Usage
tissueNames()
Value
a data.table with 2 columns, the main name, and all name variants of the main name in second
column as a list.
See Also
Other experiment_naming: batchNames(), cellLineNames(), cellTypeNames(), conditionNames(),
fractionNames(), inhibitorNames(), libNames(), mainNames(), repNames(), stageNames()
354 TOP.Motif.ecdf
TOP.Motif.ecdf TOP Motif ecdf plot
Description
Given sequences, DNA or RNA. And some score, scanning efficiency (SE), ribo-seq fpkm, TE etc.
Usage
TOP.Motif.ecdf(
seqs,
rate,
start = 1,
stop = max(nchar(seqs)),
xlim = c("q10", "q99"),
type = "Scanning efficiency",
legend.position.1st = c(0.75, 0.28),
legend.position.motif = c(0.75, 0.28)
)
Arguments
seqs the sequences (character vector, DNAStringSet), of 5’ UTRs (leaders). See ex-
ample below for input.
rate a scoring vector (equal size to seqs)
start position in seqs to start at (first is 1), default 1.
stop position in seqs to stop at (first is 1), default max(nchar(seqs)), that is the longest
sequence length
xlim What interval of rate values you want to show type: numeric or quantile of
length 2, 1. default c("q10","q99"). bigger than 10 percentile and less than 99
percentile. 2. Set to numeric values, like c(5, 1000), 3. Set to NULL if you want
all values. Backend uses coord_cartesian.
type What type is the rate scoring ? default ("Scanning efficiency")
legend.position.1st
adjust left plot label position, default c(0.75, 0.28), ("none", "left", "right", "bot-
tom", "top", or two-element numeric vector)
legend.position.motif
adjust right plot label position, default c(0.75, 0.28), ("none", "left", "right",
"bottom", "top", or two-element numeric vector)
Details
Top motif defined as a TSS of C and 4 T’s or C’s (pyrimidins) downstream of TSS C.
The right plot groups: C nucleotide, TOP motif (C, then 4 pyrimidines) and OTHER (all other TSS
variants).
topMotif 355
Value
a ggplot gtable of the TOP motifs in 2 plots
Examples
## Not run:
if (requireNamespace("BSgenome.Hsapiens.UCSC.hg19")) {
txdbFile <- system.file("extdata", "hg19_knownGene_sample.sqlite",
package = "GenomicFeatures")
#Extract sequences of Coding sequences.
leaders <- loadRegion(txdbFile, "leaders")
# Should update by CAGE if not already done
cageData <- system.file("extdata", "cage-seq-heart.bed.bgz",
package = "ORFik")
leadersCage <- reassignTSSbyCage(leaders, cageData)
# Get region to check
seqs <- startRegionString(leadersCage, NULL,
BSgenome.Hsapiens.UCSC.hg19::Hsapiens, 0, 4)
# Some toy ribo-seq fpkm scores on cds
set.seed(3)
fpkm <- sample(1:115, length(leadersCage), replace = TRUE)
# Standard arguments
TOP.Motif.ecdf(seqs, fpkm, type = "ribo-seq FPKM",
legend.position.1st = "bottom",
legend.position.motif = "bottom")
# with no zoom on x-axis:
TOP.Motif.ecdf(seqs, fpkm, xlim = NULL,
legend.position.1st = "bottom",
legend.position.motif = "bottom")
}
## End(Not run)
topMotif TOP Motif detection
Description
Per leader, detect if the leader has a TOP motif at TSS (5’ end of leader) TOP motif defined as: (C,
then 4 pyrimidines)
Usage
topMotif(seqs, start = 1, stop = max(nchar(seqs)), return.sequence = TRUE)
356 transcriptWindow
Arguments
seqs the sequences (character vector, DNAStringSet), of 5’ UTRs (leaders) start re-
gion. seqs must be of minimum widths start - stop + 1 to be included.
See example below for input.
start position in seqs to start at (first is 1), default 1.
stop position in seqs to stop at (first is 1), default max(nchar(seqs)), that is the longest
sequence length
return.sequence
logical, default TRUE, return as data.table with sequence as columns in addition
to TOP class. If FALSE, return character vector.
Value
default: return.sequence == FALSE, a character vector of either TOP, C or OTHER. C means leaders
started on C, Other means not TOP and did not start on C. If return.sequence == TRUE, a data.table
is returned with the base per position in the motif is included as additional columns (per position
called seq1, seq2 etc) and a id column called X.gene_id (with names of seqs).
Examples
## Not run:
if (requireNamespace("BSgenome.Hsapiens.UCSC.hg19")) {
txdbFile <- system.file("extdata", "hg19_knownGene_sample.sqlite",
package = "GenomicFeatures")
#Extract sequences of Coding sequences.
leaders <- loadRegion(txdbFile, "leaders")
# Should update by CAGE if not already done
cageData <- system.file("extdata", "cage-seq-heart.bed.bgz",
package = "ORFik")
leadersCage <- reassignTSSbyCage(leaders, cageData)
# Get region to check
seqs <- startRegionString(leadersCage, NULL,
BSgenome.Hsapiens.UCSC.hg19::Hsapiens, 0, 4)
topMotif(seqs)
}
## End(Not run)
transcriptWindow Make 100 bases size meta window for all libraries in experiment
Description
Gives you binned meta coverage plots, either saved seperatly or all in one.
transcriptWindow 357
Usage
transcriptWindow(
leaders,
cds,
trailers,
df,
outdir = NULL,
scores = c("sum", "transcriptNormalized"),
allTogether = TRUE,
colors = experiment.colors(df),
title = "Coverage metaplot",
windowSize = min(100, min(widthPerGroup(leaders, FALSE)), min(widthPerGroup(cds,
FALSE)), min(widthPerGroup(trailers, FALSE))),
returnPlot = is.null(outdir),
dfr = NULL,
idName = "",
plot.ext = ".pdf",
type = "ofst",
is.sorted = FALSE,
drop.zero.dt = TRUE,
BPPARAM = bpparam()
)
Arguments
leaders a GRangesList of leaders (5’ UTRs)
cds a GRangesList of coding sequences
trailers a GRangesList of trailers (3’ UTRs)
df an ORFik experiment
outdir directory to save to (default: NULL, no saving)
scores scoring function (default: c("sum", "transcriptNormalized")), see ?coverageScor-
ings for possible scores.
allTogether plot all coverage plots in 1 output? (defualt: TRUE)
colors Which colors to use, default auto color from function experiment.colors, new
color per library type. Else assign colors yourself.
title title of ggplot
windowSize size of binned windows, default: 100
returnPlot return plot from function, default is.null(outdir), so TRUE if outdir is not de-
fined.
dfr an ORFik experiment of RNA-seq to normalize against. Will add RNA nor-
malized to plot name if this is done.
idName A character ID to add to saved name of plot, if you make several plots in the
same folder, and same experiment, like splitting transcripts in two groups like
targets / nontargets etc. (default: "")
358 transcriptWindow1
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg".
type a character(default: "ofst"), load files in experiment or some precomputed vari-
ant, either "ofst", "pshifted" or "default". These are made with ORFik:::simpleLibs(),
shiftFootprintsByExperiment().. Will load default if bedoc is not found
is.sorted logical (FALSE), is grl sorted. That is + strand groups in increasing ranges
(1,2,3), and - strand groups in decreasing ranges (3,2,1)
drop.zero.dt logical FALSE, if TRUE and as.data.table is TRUE, remove all 0 count posi-
tions. This greatly speeds up and most importantly, greatly reduces memory
usage. Will not change any plots, unless 0 positions are used in some sense.
(mean, median, zscore coverage will only scale differently)
BPPARAM how many cores/threads to use? default: bpparam()
Value
NULL, or ggplot object if returnPlot is TRUE
See Also
Other experiment plots: transcriptWindow1(), transcriptWindowPer()
Examples
df <- ORFik.template.experiment()[3,] # Only third library
loadRegions(df) # Load leader, cds and trailers as GRangesList
#transcriptWindow(leaders, cds, trailers, df, outdir = "directory/to/save")
transcriptWindow1 Meta coverage over all transcripts
Description
Given as single window
Usage
transcriptWindow1(
df,
outdir = NULL,
scores = c("sum", "zscore"),
colors = experiment.colors(df),
title = "Coverage metaplot",
windowSize = 100,
returnPlot = is.null(outdir),
dfr = NULL,
idName = "",
plot.ext = ".pdf",
type = "ofst",
transcriptWindow1 359
drop.zero.dt = drop.zero.dt,
BPPARAM = bpparam()
)
Arguments
df an ORFik experiment
outdir directory to save to (default: NULL, no saving)
scores scoring function (default: c("sum", "transcriptNormalized")), see ?coverageScor-
ings for possible scores.
colors Which colors to use, default auto color from function experiment.colors, new
color per library type. Else assign colors yourself.
title title of ggplot
windowSize size of binned windows, default: 100
returnPlot return plot from function, default is.null(outdir), so TRUE if outdir is not de-
fined.
dfr an ORFik experiment of RNA-seq to normalize against. Will add RNA nor-
malized to plot name if this is done.
idName A character ID to add to saved name of plot, if you make several plots in the
same folder, and same experiment, like splitting transcripts in two groups like
targets / nontargets etc. (default: "")
plot.ext character, default: ".pdf". Alternatives: ".png" or ".jpg".
type a character(default: "ofst"), load files in experiment or some precomputed vari-
ant, either "ofst", "pshifted" or "default". These are made with ORFik:::simpleLibs(),
shiftFootprintsByExperiment().. Will load default if bedoc is not found
drop.zero.dt logical FALSE, if TRUE and as.data.table is TRUE, remove all 0 count posi-
tions. This greatly speeds up and most importantly, greatly reduces memory
usage. Will not change any plots, unless 0 positions are used in some sense.
(mean, median, zscore coverage will only scale differently)
BPPARAM how many cores/threads to use? default: bpparam()
Value
NULL, or ggplot object if returnPlot is TRUE
See Also
Other experiment plots: transcriptWindow(), transcriptWindowPer()
360 transcriptWindowPer
transcriptWindowPer Helper function for transcriptWindow
Description
Make 100 bases size meta window for one library in experiment
Usage
transcriptWindowPer(
leaders,
cds,
trailers,
df,
outdir = NULL,
scores = c("sum", "zscore"),
reads,
returnCoverage = FALSE,
windowSize = 100,
drop.zero.dt = TRUE,
BPPARAM = bpparam()
)
Arguments
leaders a GRangesList of leaders (5’ UTRs)
cds a GRangesList of coding sequences
trailers a GRangesList of trailers (3’ UTRs)
df an ORFik experiment
outdir directory to save to (default: NULL, no saving)
scores scoring function (default: c("sum", "transcriptNormalized")), see ?coverageScor-
ings for possible scores.
reads a GRanges / GAligment object of reads, can also be a list of those.
returnCoverage return data.table with coverage (default: FALSE)
windowSize size of binned windows, default: 100
drop.zero.dt logical FALSE, if TRUE and as.data.table is TRUE, remove all 0 count posi-
tions. This greatly speeds up and most importantly, greatly reduces memory
usage. Will not change any plots, unless 0 positions are used in some sense.
(mean, median, zscore coverage will only scale differently)
BPPARAM how many cores/threads to use? default: bpparam()
Details
Gives you binned meta coverage plots, either saved seperatly or all in one.
translationalEff 361
Value
NULL, or ggplot object if returnPlot is TRUE
See Also
Other experiment plots: transcriptWindow(), transcriptWindow1()
translationalEff Translational efficiency
Description
Uses RnaSeq and RiboSeq to get translational efficiency of every element in ‘grl‘. Translational
efficiency is defined as:
(density of RPF within ORF) / (RNA expression of ORFs transcript)
Usage
translationalEff(
grl,
RNA,
RFP,
tx,
with.fpkm = FALSE,
pseudoCount = 0,
librarySize = "full",
weight.RFP = 1L,
weight.RNA = 1L
)
Arguments
grl a GRangesList object can be either transcripts, 5’ utrs, cds’, 3’ utrs or ORFs as
a special case (uORFs, potential new cds’ etc). If regions are not spliced you
can send a GRanges object.
RNA RnaSeq reads as GAlignments, GRanges or GRangesList object
RFP RiboSeq reads as GAlignments, GRanges or GRangesList object
tx a GRangesList of the transcripts. If you used cage data, then the tss for the the
leaders have changed, therefor the tx lengths have changed. To account for that
call: ‘ translationalEff(grl, RNA, RFP, tx = extendLeaders(tx, cageFiveUTRs))
‘ where cageFiveUTRs are the reannotated by CageSeq data leaders.
with.fpkm logical, default: FALSE, if true return the fpkm values together with transla-
tional efficiency as a data.table
pseudoCount an integer, by default is 0, set it to 1 if you want to avoid NA and inf values.
362 translationalEff
librarySize either numeric value or character vector. Default ("full"), number of alignments
in library (reads). If you just have a subset, you can give the value by library-
Size = length(wholeLib), if you want lib size to be only number of reads over-
lapping grl, do: librarySize = "overlapping" sum(countOverlaps(reads, grl) >
0), if reads[1] has 3 hits in grl, and reads[2] has 2 hits, librarySize will be 2,
not 5. You can also get the inverse overlap, if you want lib size to be total
number of overlaps, do: librarySize = "DESeq" This is standard fpkm way of
DESeq2::fpkm(robust = FALSE) sum(countOverlaps(grl, reads)) if grl[1] has 3
reads and grl[2] has 2 reads, librarySize is 5, not 2.
weight.RFP a vector (default: 1L). Can also be character name of column in RFP. As in trans-
lationalEff(weight = "score") for: GRanges("chr1", 1, "+", score = 5), would
mean score column tells that this alignment region was found 5 times.
weight.RNA Same as weightRFP but for RNA weights. (default: 1L)
Value
a numeric vector of fpkm ratios, if with.fpkm is TRUE, return a data.table with te and fpkm values
(total 3 columns then)
References
doi: 10.1126/science.1168978
See Also
Other features: computeFeatures(), computeFeaturesCage(), countOverlapsW(), disengagementScore(),
distToCds(), distToTSS(), entropy(), floss() , fpkm(), fpkm_calc(), fractionLength(),
initiationScore(), insideOutsideORF(), isInFrame(), isOverlapping(), kozakSequenceScore(),
orfScore(), rankOrder(), ribosomeReleaseScore(), ribosomeStallingScore(), startRegion(),
startRegionCoverage(), stopRegion(), subsetCoverage()
Examples
ORF <- GRanges(seqnames = "1",
ranges = IRanges(start = c(1, 10, 20), end = c(5, 15, 25)),
strand = "+")
grl <- GRangesList(tx1_1 = ORF)
RFP <- GRanges("1", IRanges(25, 25), "+")
RNA <- GRanges("1", IRanges(1, 50), "+")
tx <- GRangesList(tx1 = GRanges("1", IRanges(1, 50), "+"))
# grl must have same names as cds + _1 etc, so that they can be matched.
te <- translationalEff(grl, RNA, RFP, tx, with.fpkm = TRUE, pseudoCount = 1)
te$fpkmRFP
te$te
trimming.table 363
trimming.table Create trimming table
Description
From fastp runs in ORFik alignment process
Usage
trimming.table(trim_folder)
Arguments
trim_folder folder of trimmed files, only reads fastp .json files
Value
a data.table with 6 columns, raw_library (names of library), raw_reads (numeric, number of raw
reads), trim_reads (numeric, number of trimmed reads), raw_mean_length (numeric, raw mean
read length), trim_mean_length (numeric, trim mean read length).
Examples
# Location of fastp trimmed .json files
trimmed_folder <- "path/to/libraries/trim/"
#trimming.table(trimmed_folder)
trim_detection Add trimming info to QC report
Description
Only works if alignment was done using ORFik with STAR.
Usage
trim_detection(df, finals, alignment_folder = libFolder(df, "unique"))
Arguments
df an ORFik experiment
finals a data.table with current output from QCreport
alignment_folder
character, default: libFolder(df, "unique"). All unique folders. trim_folders
should then be relative as: file.path(alignment_folder, "..", "trim/")
364 txNames
Value
a data.table of the update finals object with trim info
txNames Get transcript names from orf names
Description
Using the ORFik definition of orf name, which is: example ENSEMBL:
tx name: ENST0909090909090
orf id: _1 (the first of on that tx)
orf_name: ENST0909090909090_1
So therefor txNames("ENST0909090909090_1") = ENST0909090909090
Usage
txNames(grl, ref = NULL, unique = FALSE)
Arguments
grl a GRangesList grouped by ORF , GRanges object or IRanges object.
ref a reference GRangesList. The object you want grl to subset by names. Add to
make sure naming is valid.
unique a boolean, if true unique the names, used if several orfs map to same transcript
and you only want the unique groups
Details
The names must be extracted from a column called names, or the names of the grl object. If it is
already tx names, it returns the input
NOTE! Do not use _123 etc in end of transcript names if it is not ORFs. Else you will get errors.
Just _ will work, but if transcripts are called ENST_123124124000 etc, it will crash, so substitute
"_" with "." gsub("_", ".", names)
Value
a character vector of transcript names, without _* naming
See Also
Other ORFHelpers: defineTrailer(), longestORFs(), mapToGRanges(), orfID(), startCodons(),
startSites(), stopCodons(), stopSites(), uniqueGroups(), uniqueOrder()
txNamesToGeneNames 365
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1_1 = gr_plus, tx2_1 = gr_minus)
# there are 2 orfs, both the first on each transcript
txNames(grl)
txNamesToGeneNames Convert transcript names to gene names
Description
Works for ensembl, UCSC and other standard annotations.
Usage
txNamesToGeneNames(txNames, txdb)
Arguments
txNames character vector, the transcript names to convert. Can also be a named object
with tx names (like a GRangesList), will then extract names.
txdb the transcript database to use or gtf/gff path to it.
Value
character vector of gene names
Examples
gtf <- system.file("extdata/Danio_rerio_sample", "annotations.gtf", package = "ORFik")
txdb <- loadTxdb(gtf)
loadRegions(txdb, "cds") # using tx names
txNamesToGeneNames(cds, txdb)
# Identical to:
loadRegions(txdb, "cds", by = "gene")
366 txSeqsFromFa
txSeqsFromFa Get transcript sequence from a GRangesList and a faFile or BSgenome
Description
For each GRanges object, find the sequence of it from faFile or BSgenome.
Usage
txSeqsFromFa(grl, faFile, is.sorted = FALSE, keep.names = TRUE)
Arguments
grl a GRangesList object
faFile FaFile, BSgenome, fasta/index file path or an ORFik experiment. This file is
usually used to find the transcript sequences from some GRangesList.
is.sorted a speedup, if you know the grl ranges are sorted
keep.names a logical, default (TRUE), if FALSE: return as character vector without names.
Details
A wrapper around extractTranscriptSeqs that works for DNAStringSet and ORFik experiment
input. For debug of errors do: which(!(unique(seqnamesPerGroup(grl, FALSE)) This happens usu-
ally when the grl contains chromsomes that the fasta file does not have. A normal error is that
mitocondrial chromosome is called MT vs chrM even though they have same seqlevelsStyle. The
above line will give you which chromosome it is missing.
Value
a DNAStringSet of the transcript sequences
See Also
Other ExtendGenomicRanges: asTX(), coveragePerTiling(), extendLeaders(), extendTrailers(),
reduceKeepAttr(), tile1(), windowPerGroup()
uniqueGroups 367
uniqueGroups Get the unique set of groups in a GRangesList
Description
Sometimes GRangesList groups might be identical, for example ORFs from different isoforms can
have identical ranges. Use this function to reduce these groups to unique elements in GRangesList
grl, without names and metacolumns.
Usage
uniqueGroups(grl)
Arguments
grl a GRangesList
Value
a GRangesList of unique orfs
See Also
Other ORFHelpers: defineTrailer(), longestORFs(), mapToGRanges(), orfID(), startCodons(),
startSites(), stopCodons(), stopSites(), txNames(), uniqueOrder()
Examples
gr1 <- GRanges("1", IRanges(1,10), "+")
gr2 <- GRanges("1", IRanges(20, 30), "+")
# make a grl with duplicated ORFs (gr1 twice)
grl <- GRangesList(tx1_1 = gr1, tx2_1 = gr2, tx3_1 = gr1)
uniqueGroups(grl)
uniqueOrder Get unique ordering for GRangesList groups
Description
This function can be used to calculate unique numerical identifiers for each of the GRangesList
elements. Elements of GRangesList are unique when the GRanges inside are not duplicated, so
ranges differences matter as well as sorting of the ranges.
Usage
uniqueOrder(grl)
368 unlistGrl
Arguments
grl a GRangesList
Value
an integer vector of indices of unique groups
See Also
uniqueGroups
Other ORFHelpers: defineTrailer(), longestORFs(), mapToGRanges(), orfID(), startCodons(),
startSites(), stopCodons(), stopSites(), txNames(), uniqueGroups()
Examples
gr1 <- GRanges("1", IRanges(1,10), "+")
gr2 <- GRanges("1", IRanges(20, 30), "+")
# make a grl with duplicated ORFs (gr1 twice)
grl <- GRangesList(tx1_1 = gr1, tx2_1 = gr2, tx3_1 = gr1)
uniqueOrder(grl) # remember ordering
# example on unique ORFs
uniqueORFs <- uniqueGroups(grl)
# now the orfs are unique, let's map back to original set:
reMappedGrl <- uniqueORFs[uniqueOrder(grl)]
unlistGrl Safe unlist
Description
Same as [AnnotationDbi::unlist2()], keeps names correctly. Two differences is that if grl have no
names, it will not make integer names, but keep them as null. Also if the GRangesList has names ,
and also the GRanges groups, then the GRanges group names will be kept.
Usage
unlistGrl(grl)
Arguments
grl a GRangesList
Value
a GRanges object
uORFSearchSpace 369
Examples
ORF <- GRanges(seqnames = "1",
ranges = IRanges(start = c(1, 10, 20),
end = c(5, 15, 25)),
strand = "+")
grl <- GRangesList(tx1_1 = ORF)
unlistGrl(grl)
uORFSearchSpace Create search space to look for uORFs
Description
Given a GRangesList of 5’ UTRs or transcripts, reassign the start sites using max peaks from
CageSeq data (if CAGE is given). A max peak is defined as new TSS if it is within boundary
of 5’ leader range, specified by ‘extension‘ in bp. A max peak must also be higher than minimum
CageSeq peak cutoff specified in ‘filterValue‘. The new TSS will then be the positioned where the
cage read (with highest read count in the interval). If you want to include uORFs going into the
CDS, add this argument too.
Usage
uORFSearchSpace(
fiveUTRs,
cage = NULL,
extension = 1000,
filterValue = 1,
restrictUpstreamToTx = FALSE,
removeUnused = FALSE,
cds = NULL
)
Arguments
fiveUTRs (GRangesList) The 5’ leaders or full transcript sequences
cage Either a filePath for the CageSeq file as .bed .bam or .wig, with possible com-
pressions (".gzip", ".gz", ".bgz"), or already loaded CageSeq peak data as GRanges
or GAlignment. NOTE: If it is a .bam file, it will add a score column by run-
ning: convertToOneBasedRanges(cage, method = "5prime", addScoreColumn =
TRUE) The score column is then number of replicates of read, if score column
is something else, like read length, set the score column to NULL first.
extension The maximum number of basses upstream of the TSS to search for CageSeq
peak.
filterValue The minimum number of reads on cage position, for it to be counted as possible
new tss. (represented in score column in CageSeq data) If you already filtered,
set it to 0.
370 updateTxdbRanks
restrictUpstreamToTx
a logical (FALSE). If TRUE: restrict leaders to not extend closer than 5 bases
from closest upstream leader, set this to TRUE.
removeUnused logical (FALSE), if False: (standard is to set them to original annotation), If
TRUE: remove leaders that did not have any cage support.
cds (GRangesList) CDS of relative fiveUTRs, applicable only if you want to extend
5’ leaders downstream of CDS’s, to allow upstream ORFs that can overlap into
CDS’s.
Value
a GRangesList of newly assigned TSS for fiveUTRs, using CageSeq data.
See Also
Other uorfs: addCdsOnLeaderEnds(), filterUORFs(), removeORFsWithSameStartAsCDS(), removeORFsWithSameStopAsCDS(),
removeORFsWithStartInsideCDS(), removeORFsWithinCDS()
Examples
# example 5' leader, notice exon_rank column
fiveUTRs <- GenomicRanges::GRangesList(
GenomicRanges::GRanges(seqnames = "chr1",
ranges = IRanges::IRanges(1000, 2000),
strand = "+",
exon_rank = 1))
names(fiveUTRs) <- "tx1"
# make fake CageSeq data from promoter of 5' leaders, notice score column
cage <- GenomicRanges::GRanges(
seqnames = "chr1",
ranges = IRanges::IRanges(500, 510),
strand = "+",
score = 10)
# finally reassign TSS for fiveUTRs
uORFSearchSpace(fiveUTRs, cage)
updateTxdbRanks Update exon ranks of exon data.frame inside txdb object
Description
Update exon ranks of exon data.frame inside txdb object
Usage
updateTxdbRanks(exons)
updateTxdbStartSites 371
Arguments
exons a data.frame, call of as.list(txdb)$splicings
Value
a data.frame, modified call of as.list(txdb)
updateTxdbStartSites Update start sites of leaders
Description
Update start sites of leaders
Usage
updateTxdbStartSites(txList, fiveUTRs, removeUnused)
Arguments
txList a list, call of as.list(txdb)
fiveUTRs a GRangesList of 5’ leaders
removeUnused logical (FALSE), remove leaders that did not have any cage support. (standard
is to set them to original annotation)
Value
a list, modified call of as.list(txdb)
upstreamFromPerGroup Get rest of objects upstream (inclusive)
Description
Per group get the part upstream of position. upstreamFromPerGroup(tx, stopSites(fiveUTRs, asGR
= TRUE)) will return the 5’ utrs per transcript as GRangesList, usually used for interesting parts of
the transcripts.
Usage
upstreamFromPerGroup(tx, upstreamFrom)
372 upstreamOfPerGroup
Arguments
tx a GRangesList, usually of Transcripts to be changed
upstreamFrom a vector of integers, for each group in tx, where is the new start point of first
valid exon.
Details
If you don’t want to include the points given in the region, use upstreamOfPerGroup
Value
a GRangesList of upstream part
See Also
Other GRanges: assignFirstExonsStartSite(), assignLastExonsStopSite(), downstreamFromPerGroup(),
downstreamOfPerGroup(), upstreamOfPerGroup()
upstreamOfPerGroup Get rest of objects upstream (exclusive)
Description
Per group get the part upstream of position upstreamOfPerGroup(tx, startSites(cds, asGR = TRUE))
will return the 5’ utrs per transcript, usually used for interesting parts of the transcripts.
Usage
upstreamOfPerGroup(
tx,
upstreamOf,
allowOutside = TRUE,
is.circular = all(isCircular(tx) %in% TRUE)
)
Arguments
tx a GRangesList, usually of Transcripts to be changed
upstreamOf a vector of integers, for each group in tx, where is the the base after the new stop
point of last valid exon.
allowOutside a logical (T), can upstreamOf extend outside range of tx, can set boundary as a
false hit, so beware.
is.circular logical, default FALSE if not any is: all(isCircular(grl) Where grl is the ranges
checked. If TRUE, allow ranges to extend below position 1 on chromosome.
Since circular genomes can have negative coordinates.
validateExperiments 373
Value
a GRangesList of upstream part
See Also
Other GRanges: assignFirstExonsStartSite(), assignLastExonsStopSite(), downstreamFromPerGroup(),
downstreamOfPerGroup(), upstreamFromPerGroup()
validateExperiments Validate ORFik experiment
Description
Check for valid existing, non-empty and all unique. A good way to see if your experiment is valid.
Usage
validateExperiments(df)
Arguments
df an ORFik experiment
Value
NULL (Stops if failed)
See Also
Other ORFik_experiment: ORFik.template.experiment(), ORFik.template.experiment.zf(),
bamVarName(), create.experiment(), experiment-class, filepath(), libraryTypes(), organism,experiment-method,
outputLibs(), read.experiment(), save.experiment()
validGRL Helper Function to check valid GRangesList input
Description
Helper Function to check valid GRangesList input
Usage
validGRL(class, type = "grl", checkNULL = FALSE)
374 validSeqlevels
Arguments
class as character vector the given class of supposed GRangesList object
type a character vector, is it gtf, cds, 5’, 3’, for messages.
checkNULL should NULL classes be checked and return indeces of these?
Value
either NULL or indices (checkNULL == TRUE)
See Also
Other validity: checkRFP(), checkRNA(), is.ORF(), is.gr_or_grl(), is.grl(), is.range(),
validSeqlevels()
validSeqlevels Helper function to find overlaping seqlevels
Description
Keep only seqnames in reads that are in grl Useful to avoid seqname warnings in bioC
Usage
validSeqlevels(grl, reads)
Arguments
grl a GRangesList or GRanges object
reads a GRanges, GAlignment or GAlignmentPairs object
Value
a character vector of valid seqlevels
See Also
Other validity: checkRFP(), checkRNA(), is.ORF(), is.gr_or_grl(), is.grl(), is.range(),
validGRL()
widthPerGroup 375
widthPerGroup Get list of widths per granges group
Description
Get list of widths per granges group
Usage
widthPerGroup(grl, keep.names = TRUE)
Arguments
grl a GRangesList
keep.names a boolean, keep names or not, default: (TRUE)
Value
an integer vector (named/unnamed) of widths
Examples
gr_plus <- GRanges(seqnames = c("chr1", "chr1"),
ranges = IRanges(c(7, 14), width = 3),
strand = c("+", "+"))
gr_minus <- GRanges(seqnames = c("chr2", "chr2"),
ranges = IRanges(c(4, 1), c(9, 3)),
strand = c("-", "-"))
grl <- GRangesList(tx1 = gr_plus, tx2 = gr_minus)
widthPerGroup(grl)
windowCoveragePlot Get meta coverage plot of reads
Description
Spanning a region like a transcripts, plot how the reads distribute.
Usage
windowCoveragePlot(
coverage,
output = NULL,
scoring = "zscore",
colors = c("skyblue4", "orange"),
title = "Coverage metaplot",
376 windowCoveragePlot
type = "transcripts",
scaleEqual = FALSE,
setMinToZero = FALSE
)
Arguments
coverage a data.table, e.g. output of scaledWindowCoverage
output character string (NULL), if set, saves the plot as pdf or png to path given. If no
format is given, is save as pdf.
scoring character vector, default "zscore", either of zscore, transcriptNormalized, sum,
mean, median, .. or NULL. Set NULL if already scored. see ?coverageScorings
for info and more alternatives.
colors character vector colors to use in plot, will fix automaticly, using binary splits
with colors c(’skyblue4’, ’orange’).
title a character (metaplot) (what is the title of plot?)
type a character (transcripts), what should legends say is the whole region? Tran-
scripts, genes, non coding rnas etc.
scaleEqual a logical (FALSE), should all fractions (rows), have same max value, for easy
comparison of max values if needed.
setMinToZero a logical (FALSE), should minimum y-value be 0 (TRUE). With FALSE mini-
mum value is minimum score at any position. This parameter overrides scaleE-
qual.
Details
If coverage has a column called feature, this can be used to subdivide the meta coverage into parts
as (5’ UTRs, cds, 3’ UTRs) These are the columns in the plot. The fraction column divide sequence
libraries. Like ribo-seq and rna-seq. These are the rows of the plot. If you return this function
without assigning it and output is NULL, it will automaticly plot the figure in your session. If
output is assigned, no plot will be shown in session. NULL is returned and object is saved to
output.
Colors: Remember if you want to change anything like colors, just return the ggplot object, and
reassign like: obj + scale_color_brewer() etc.
Value
a ggplot object of the coverage plot, NULL if output is set, then the plot will only be saved to
location.
See Also
Other coveragePlot: coverageHeatMap(), pSitePlot(), savePlot()
windowPerGroup 377
Examples
library(data.table)
coverage <- data.table(position = seq(20),
score = sample(seq(20), 20, replace = TRUE))
windowCoveragePlot(coverage)
#Multiple plots in one frame:
coverage2 <- copy(coverage)
coverage$fraction <- "Ribo-seq"
coverage2$fraction <- "RNA-seq"
dt <- rbindlist(list(coverage, coverage2))
windowCoveragePlot(dt, scoring = "log10sum")
# See vignette for a more practical example
windowPerGroup Get window region of GRanges object
Description
Per GRanges input (gr) of single position inputs (center point), create a GRangesList window output
of specified upstream, downstream region relative to some transcript "tx".
If downstream is 20, it means the window will start 20 downstream of gr start site (-20 in relative
transcript coordinates.) If upstream is 20, it means the window will start 20 upstream of gr start site
(+20 in relative transcript coordinates.) It will keep exon structure of tx, so if -20 is on next exon, it
jumps to next exon.
Usage
windowPerGroup(gr, tx, upstream = 0L, downstream = 0L)
Arguments
gr a GRanges/IRanges object (startSites or others, must be single point per in ge-
nomic coordinates)
tx a GRangesList of transcripts or (container region), names of tx must contain
all gr names. The names of gr can also be the ORFik orf names. that is "tx-
Name_id".
upstream an integer, default (0), relative region to get upstream from.
downstream an integer, default (0), relative region to get downstream from
Details
If a region has a part that goes out of bounds, E.g if you try to get window around the CDS start
site, goes longer than the 5’ leader start site, it will set start to the edge boundary (the TSS of the
transcript in this case). If region has no hit in bound, a width 0 GRanges object is returned. This is
useful for things like countOverlaps, since 0 hits will then always be returned for the correct object
index. If you don’t want the 0 width windows, use reduce() to remove 0-width windows.
378 windowPerReadLength
Value
a GRanges, or GRangesList object if any group had > 1 exon.
See Also
Other ExtendGenomicRanges: asTX(), coveragePerTiling(), extendLeaders(), extendTrailers(),
reduceKeepAttr(), tile1(), txSeqsFromFa()
Examples
# find 2nd codon of an ORF on a spliced transcript
ORF <- GRanges("1", c(3), "+") # start site
names(ORF) <- "tx1_1" # ORF 1 on tx1
tx <- GRangesList(tx1 = GRanges("1", c(1,3,5,7,9,11,13), "+"))
windowPerGroup(ORF, tx, upstream = -3, downstream = 5) # <- 2nd codon
# With multiple extensions downstream
ORF <- rep(ORF, 2)
names(ORF)[2] <- "tx1_2"
windowPerGroup(ORF, tx, upstream = 0, downstream = c(2, 5))
# The last one gives 2nd and (1st and 2nd) codon as two groups
windowPerReadLength Find proportion of reads per position per read length in window
Description
This is defined as: Fraction of reads per read length, per position in whole window (defined by
upstream and downstream) If tx is not NULL, it gives a metaWindow, centered around startSite
of grl from upstream and downstream. If tx is NULL, it will use only downstream , since it has
no reference on how to find upstream region. The exception is when upstream is negative, that is,
going into downstream region of the object.
Usage
windowPerReadLength(
grl,
tx = NULL,
reads,
pShifted = TRUE,
upstream = ifelse(!is.null(tx), ifelse(pShifted, 5, 20), min(ifelse(pShifted, 5, 20),
0)),
downstream = ifelse(pShifted, 20, 5),
acceptedLengths = NULL,
zeroPosition = upstream,
scoring = "transcriptNormalized",
weight = "score",
windowPerReadLength 379
drop.zero.dt = FALSE,
append.zeroes = FALSE,
windows = startRegion(grl, tx, TRUE, upstream, downstream)
)
Arguments
grl a GRangesList object with usually either leaders, cds’, 3’ utrs or ORFs
tx default NULL, a GRangesList of transcripts or (container region), names of tx
must contain all grl names. The names of grl can also be the ORFik orf names.
that is "txName_id"
reads a GAlignments, GRanges, or precomputed coverage as covRleList (where
names of covRle objects are readlengths) of RiboSeq, RnaSeq etc.
Weigths for scoring is default the ’score’ column in ’reads’. Can also be random
access paths to bigWig or fstwig file. Do not use random access for more than a
few genes, then loading the entire files is usually better.
pShifted a logical (TRUE), are Ribo-seq reads p-shifted to size 1 width reads? If upstream
and downstream is set, this argument is irrelevant. So set to FALSE if this is not
p-shifted Ribo-seq.
upstream an integer (5), relative region to get upstream from. Default: ifelse(!is.null(tx),
ifelse(pShifted, 5, 20), min(ifelse(pShifted, 5, 20), 0))
downstream an integer (20), relative region to get downstream from. Default: ifelse(pShifted,
20, 5)
acceptedLengths
an integer vector (NULL), the read lengths accepted. Default NULL, means all
lengths accepted.
zeroPosition an integer DEFAULT (upstream), what is the center point? Like leaders and cds
combination, then 0 is the TIS and -1 is last base in leader. NOTE!: if windows
have different widths, this will be ignored.
scoring a character (transcriptNormalized), which meta coverage scoring ? one of (zs-
core, transcriptNormalized, mean, median, sum, sumLength, fracPos), see ?cov-
erageScorings for more info. Use to decide a scoring of hits per position for
metacoverage etc. Set to NULL if you do not want meta coverage, but instead
want per gene per position raw counts.
weight (default: ’score’), if defined a character name of valid meta column in subject.
GRanges("chr1", 1, "+", score = 5), would mean score column tells that this
alignment region was found 5 times. ORFik ofst, bedoc and .bedo files contains
a score column like this. As do CAGEr CAGE files and many other package
formats. You can also assign a score column manually.
drop.zero.dt logical FALSE, if TRUE and as.data.table is TRUE, remove all 0 count posi-
tions. This greatly speeds up and most importantly, greatly reduces memory
usage. Will not change any plots, unless 0 positions are used in some sense.
(mean, median, zscore coverage will only scale differently)
append.zeroes logical, default FALSE. If TRUE and drop.zero.dt is TRUE and all windows
have equal length, it will add back 0 values after transformation. Sometimes
needed for correct plots, if TRUE, will call abort if not all windows are equal
length!
380 windowPerTranscript
windows the GRangesList windows to actually check, default: startRegion(grl, tx,
TRUE, upstream, downstream).
Details
Careful when you create windows where not all transcripts are long enough, this function usually is
used first with filterTranscripts to make sure they are of all of valid length!
Value
a data.table with 4 columns: position (in window), score, fraction (read length). If score is NULL,
will also return genes (index of grl). A note is that if no coverage is found, it returns an empty
data.table.
See Also
Other coverage: coverageScorings(), metaWindow(), regionPerReadLength(), scaledWindowPositions()
Examples
cds <- GRangesList(tx1 = GRanges("1", 100:129, "+"))
tx <- GRangesList(tx1 = GRanges("1", 80:129, "+"))
reads <- GRanges("1", seq(79,129, 3), "+")
windowPerReadLength(cds, tx, reads, scoring = "sum")
windowPerReadLength(cds, tx, reads, scoring = "transcriptNormalized")
windowPerTranscript Get a binned coverage window per transcript
Description
Per transcript (or other regions), bin them all to windowSize (default 100), and make a data.table,
rows are positions, useful for plotting with ORFik and ggplot2.
Usage
windowPerTranscript(
txdb,
reads,
splitIn3 = TRUE,
windowSize = 100,
fraction = "1",
weight = "score",
drop.zero.dt = FALSE,
BPPARAM = bpparam()
)
xAxisScaler 381
Arguments
txdb a TxDb object or a path to gtf/gff/db file.
reads GRanges or GAlignment of reads
splitIn3 a logical(TRUE), split window in 3 (leader, cds, trailer)
windowSize an integer (100), size of windows (columns). All genes with region smaller than
this size are filter out for metacoverage.
fraction a character (1), info on reads (which read length, or which type (RNA seq)) (row
names)
weight (default: ’score’), if defined a character name of valid meta column in subject.
GRanges("chr1", 1, "+", score = 5), would mean score column tells that this
alignment region was found 5 times. ORFik ofst, bedoc and .bedo files contains
a score column like this. As do CAGEr CAGE files and many other package
formats. You can also assign a score column manually.
drop.zero.dt logical FALSE, if TRUE and as.data.table is TRUE, remove all 0 count posi-
tions. This greatly speeds up and most importantly, greatly reduces memory
usage. Will not change any plots, unless 0 positions are used in some sense.
(mean, median, zscore coverage will only scale differently)
BPPARAM how many cores/threads to use? default: bpparam()
Details
NOTE: All ranges with smaller width than windowSize, will of course be removed. What is the
100th position on a 1 width object ?
Value
a data.table with columns position, score
xAxisScaler Scale x axis correctly
Description
Works for all coverage plots, that need 0 position aligning
Usage
xAxisScaler(covPos)
Arguments
covPos a numeric vector of positions in coverage
Details
It basicly bins the x axis on floor(length of x axis / 20) or 1 if x < 20
382 yAxisScaler
Value
a numeric vector from the seq() function, aligned to 0.
yAxisScaler Scale y axis correctly
Description
Works for all coverage plots.
Usage
yAxisScaler(covPos, increments.y = "auto")
Arguments
covPos a levels object from a factor of y axis
increments.y increments of y axis, default "auto". Or a numeric value < max position & >
min position.
Value
a character vector from the seq() function, aligned to 0.
Index
∗ CAGE
assignTSSByCage, 19
reassignTSSbyCage, 279
reassignTxDbByCage, 281
∗ DifferentialExpression
DEG.plot.static, 87
DEG_model, 88
DTEG.analysis, 110
DTEG.plot, 113
te.table, 350
te_rna.plot, 351
∗ ExtendGenomicRanges
asTX, 20
coveragePerTiling, 72
extendLeaders, 132
extendTrailers, 134
reduceKeepAttr, 282
tile1, 352
txSeqsFromFa, 366
windowPerGroup, 377
∗ GRanges
assignFirstExonsStartSite, 17
assignLastExonsStopSite, 18
downstreamFromPerGroup, 108
downstreamOfPerGroup, 109
upstreamFromPerGroup, 371
upstreamOfPerGroup, 372
∗ ORFHelpers
defineTrailer, 84
longestORFs, 228
mapToGRanges, 234
orfID, 248
startCodons, 335
startSites, 340
stopCodons, 341
stopSites, 343
txNames, 364
uniqueGroups, 367
uniqueOrder, 367
∗ ORFik_experiment
bamVarName, 22
create.experiment, 80
experiment-class, 119
filepath, 137
libraryTypes, 222
ORFik.template.experiment, 249
ORFik.template.experiment.zf, 249
organism,experiment-method, 254
outputLibs, 255
read.experiment, 273
save.experiment, 302
validateExperiments, 373
∗ QC report
QCplots, 266
QCreport, 267
QCstats, 269
∗ STAR
getGenomeAndAnnotation, 174
install.fastp, 206
STAR.align.folder, 322
STAR.align.single, 326
STAR.allsteps.multiQC, 330
STAR.index, 331
STAR.install, 333
STAR.multiQC, 334
STAR.remove.crashed.genome, 334
∗ codon
codon_usage, 30
codon_usage_exp, 32
codon_usage_plot, 34
∗ countTable
countTable, 64
countTable_regions, 66
∗ covRLE
covRle, 76
covRle-class, 77
covRleFromGR, 78
covRleList, 79
383
384 INDEX
covRleList-class, 79
∗ coveragePlot
coverageHeatMap, 70
pSitePlot, 264
savePlot, 303
windowCoveragePlot, 375
∗ coverage
coverageScorings, 74
metaWindow, 239
regionPerReadLength, 284
scaledWindowPositions, 304
windowPerReadLength, 378
∗ experiment plots
transcriptWindow, 356
transcriptWindow1, 358
transcriptWindowPer, 360
∗ experiment_naming
batchNames, 24
cellLineNames, 26
cellTypeNames, 27
conditionNames, 47
fractionNames, 168
inhibitorNames, 202
libNames, 221
mainNames, 229
repNames, 291
stageNames, 321
tissueNames, 353
∗ features
computeFeatures, 42
computeFeaturesCage, 44
countOverlapsW, 63
disengagementScore, 99
distToCds, 101
distToTSS, 102
entropy, 115
floss, 163
fpkm, 165
fpkm_calc, 166
fractionLength, 167
initiationScore, 203
insideOutsideORF, 204
isInFrame, 210
isOverlapping, 211
kozakSequenceScore, 214
orfScore, 252
rankOrder, 272
ribosomeReleaseScore, 297
ribosomeStallingScore, 298
startRegion, 337
startRegionCoverage, 338
stopRegion, 342
subsetCoverage, 346
translationalEff, 361
∗ findORFs
findMapORFs, 146
findORFs, 149
findORFsFasta, 151
findUORFs, 154
startDefinition, 336
stopDefinition, 342
∗ heatmaps
coverageHeatMap, 70
heatMap_single, 197
heatMapL, 193
heatMapRegion, 195
∗ internal
addCdsOnLeaderEnds, 11
addNewTSSOnLeaders, 12
alignmentFeatureStatistics, 12
allFeaturesHelper, 13
appendZeroes, 15
assignAnnotations, 16
assignFirstExonsStartSite, 17
assignLastExonsStopSite, 18
bamVarNamePicker, 23
batchNames, 24
bedToGR, 25
cellLineNames, 26
cellTypeNames, 27
changePointAnalysis, 27
checkRFP, 28
checkRNA, 29
codonSumsPerGroup, 29
collapse.by.scores, 35
conditionNames, 47
coverageGroupings, 69
defineIsoform, 83
download.ebi, 103
downstreamFromPerGroup, 108
downstreamN, 109
downstreamOfPerGroup, 109
exists.ftp.dir.fast, 118
exists.ftp.file.fast, 118
extendsTSSexons, 133
filterCage, 138
INDEX 385
filterUORFs, 142
find_url_ebi_safe, 159
findFromPath, 145
findLibrariesInFolder, 145
findMaxPeaks, 148
findNewTSS, 148
findNGSPairs, 149
footprints.analysis, 164
fpkm_calc, 166
fractionNames, 168
get_genome_fasta, 181
get_genome_gtf, 183
get_noncoding_rna, 186
get_phix_genome, 188
getGAlignments, 172
getGAlignmentsPairs, 173
getGRanges, 178
getGtfPathFromTxdb, 178
getNGenesCoverage, 179
getWeights, 179
gSort, 192
hasHits, 192
heatMapL, 193
inhibitorNames, 202
is.gr_or_grl, 208
is.grl, 208
is.ORF, 209
is.range, 209
isPeriodic, 212
libNames, 221
mainNames, 229
makeExonRanks, 230
mapToGRanges, 234
matchColors, 235
matchNaming, 235
matchSeqStyle, 236
numCodons, 243
optimized_txdb_path, 246
optimizeReads, 247
orfID, 248
pasteDir, 257
percentage_to_ratio, 259
plotHelper, 259
prettyScoring, 263
pseudo.transform, 263
QC_count_tables, 270
QCplots, 266
readLengthTable, 277
remakeTxdbExonIds, 285
remove.file_ext, 287
removeMetaCols, 287
removeORFsWithinCDS, 288
removeORFsWithSameStartAsCDS, 288
removeORFsWithSameStopAsCDS, 289
removeORFsWithStartInsideCDS, 289
removeTxdbExons, 290
removeTxdbTranscripts, 290
rename.SRA.files, 291
repNames, 291
restrictTSSByUpstreamLeader, 293
revElementsF, 293
reverseMinusStrandPerGroup, 294
savePlot, 303
splitIn3Tx, 320
stageNames, 321
subsetCoverage, 346
tissueNames, 353
transcriptWindow1, 358
transcriptWindowPer, 360
trim_detection, 363
updateTxdbRanks, 370
updateTxdbStartSites, 371
upstreamFromPerGroup, 371
upstreamOfPerGroup, 372
validateExperiments, 373
validGRL, 373
validSeqlevels, 374
windowPerTranscript, 380
xAxisScaler, 381
yAxisScaler, 382
∗ lib_converters
convert_bam_to_ofst, 54
convert_to_bigWig, 56
convert_to_covRle, 57
convert_to_covRleList, 58
convertLibs, 51
∗ pshifting
changePointAnalysis, 27
detectRibosomeShifts, 93
shiftFootprints, 309
shiftFootprintsByExperiment, 311
shiftPlots, 314
shifts.load, 315
∗ sra
browseSRA, 25
download.ebi, 103
386 INDEX
download.SRA, 104
download.SRA.metadata, 106
get_bioproject_candidates, 180
install.sratoolkit, 207
rename.SRA.files, 291
∗ uorfs
addCdsOnLeaderEnds, 11
filterUORFs, 142
removeORFsWithinCDS, 288
removeORFsWithSameStartAsCDS, 288
removeORFsWithSameStopAsCDS, 289
removeORFsWithStartInsideCDS, 289
uORFSearchSpace, 369
∗ utils
bedToGR, 25
convertToOneBasedRanges, 52
export.bed12, 122
export.bigWig, 124
export.fstwig, 126
export.wiggle, 131
fimport, 142
findFa, 144
fread.bed, 169
optimizeReads, 247
readBam, 274
readBigWig, 276
readWig, 278
∗ validity
checkRFP, 28
checkRNA, 29
is.gr_or_grl, 208
is.grl, 208
is.ORF, 209
is.range, 209
validGRL, 373
validSeqlevels, 374
addCdsOnLeaderEnds, 11, 142, 288–290, 370
addNewTSSOnLeaders, 12
alignmentFeatureStatistics, 12
allFeaturesHelper, 13
appendZeroes, 15
artificial.orfs, 15
assignAnnotations, 16
assignFirstExonsStartSite, 17, 18, 108,
110, 372, 373
assignLastExonsStopSite, 17, 18, 108, 110,
372, 373
assignTSSByCage, 19, 280, 282
asTX, 20, 73, 133, 134, 283, 353, 366, 378
bamVarName, 22, 82, 120, 138, 222, 249, 250,
254, 257, 274, 303, 373
bamVarNamePicker, 23
batchNames, 24, 26, 27, 47, 168, 203, 221,
229, 292, 322, 353
bedToGR, 25, 54, 122, 125, 126, 132, 143, 144,
169, 247, 275, 276, 279
browseSRA, 25, 104, 105, 107, 180, 207, 291
cellLineNames, 24, 26, 27, 47, 168, 203, 221,
229, 292, 322, 353
cellTypeNames, 24, 26, 27, 47, 168, 203, 221,
229, 292, 322, 353
changePointAnalysis, 27, 95, 310, 313, 315,
316
checkRFP, 28, 29, 208, 209, 374
checkRNA, 29, 29, 208, 209, 374
codon_usage, 30, 34, 35
codon_usage_exp, 32, 32, 35
codon_usage_plot, 32, 34, 34
codonSumsPerGroup, 29
collapse.by.scores, 35
collapse.fastq, 36
collapseDuplicatedReads, 37
collapseDuplicatedReads,data.table-method,
38
collapseDuplicatedReads,GAlignmentPairs-method,
39
collapseDuplicatedReads,GAlignments-method,
39
collapseDuplicatedReads,GRanges-method,
40
combn.pairs, 41
computeFeatures, 42, 46, 64, 100–102, 115,
164, 166–168, 204, 205, 210, 211,
215, 253, 273, 298, 299, 337, 339,
343, 347, 362
computeFeaturesCage, 44, 44, 64, 100–102,
115, 164, 166–168, 204, 205, 210,
211, 215, 253, 273, 298, 299, 337,
339, 343, 347, 362
conditionNames, 24, 26, 27, 47, 168, 203,
221, 229, 292, 322, 353
config, 47
config.exper, 48
config.save, 49
config_file, 50
INDEX 387
convert_bam_to_ofst, 52, 54, 57–59, 319
convert_to_bigWig, 52, 55, 56, 58, 59, 319
convert_to_covRle, 52, 55, 57, 57, 59, 319
convert_to_covRleList, 52, 55, 57, 58, 58,
312, 319
convert_to_fstWig, 59
convertLibs, 51, 55, 57–59
convertToOneBasedRanges, 25, 51, 52, 122,
125, 126, 132, 143, 144, 169, 247,
275, 276, 279, 318
cor_plot, 62
cor_table, 63
correlation.plots, 60
countOverlapsW, 44, 46, 63, 100–102, 115,
164, 166–168, 204, 205, 210, 211,
215, 253, 273, 298, 299, 337, 339,
343, 347, 362
countTable, 64, 67, 250, 268
countTable_regions, 66, 66
coverage_to_dt, 75
coverageByTranscript, 68
coverageByTranscriptC, 68
coverageByTranscriptW, 68
coverageGroupings, 69
coverageHeatMap, 70, 195, 197, 199, 265,
304, 376
coveragePerTiling, 21, 72, 133, 134, 283,
353, 366, 378
coverageScorings, 74, 241, 285, 305, 380
covRle, 30, 72, 76, 77–79, 304, 316
covRle-class, 77
covRleFromGR, 77, 78, 79
covRleList, 77–79, 79, 198, 284, 317, 379
covRleList-class, 79
create.experiment, 23, 80, 120, 138, 222,
249, 250, 254, 257, 273, 274, 303,
373
data.frame, 25
defineIsoform, 83
defineTrailer, 84, 229, 234, 248, 336, 340,
341, 344, 364, 367, 368
DEG.analysis, 85, 87
DEG.plot.static, 86, 87, 89, 112, 114, 351,
352
DEG_model, 86, 88, 88, 112, 114, 351, 352
DEG_model_results, 90
DEG_model_simple, 91
design,experiment-method, 92
detect_ribo_orfs, 96
detectRibosomeShifts, 28, 93, 252,
310–313, 315, 316
disengagementScore, 44, 46, 64, 99, 101,
102, 115, 164, 166–168, 204, 205,
210, 211, 215, 253, 273, 298, 299,
337, 339, 343, 347, 362
distToCds, 44, 46, 64, 100, 101, 102, 115,
164, 166–168, 204, 205, 210, 211,
215, 253, 273, 298, 299, 337, 339,
343, 347, 362
distToTSS, 44, 46, 64, 100, 101, 102, 115,
164, 166–168, 204, 205, 210, 211,
215, 253, 273, 298, 299, 337, 339,
343, 347, 362
DNAStringSet, 366
download.ebi, 26, 103, 105, 107, 180, 207,
291
download.SRA, 26, 104, 104, 107, 180, 207,
291
download.SRA.metadata, 26, 104, 105, 106,
180, 207, 291
downstreamFromPerGroup, 17, 18, 108, 110,
372, 373
downstreamN, 109
downstreamOfPerGroup, 17, 18, 108, 109,
372, 373
DTEG.analysis, 88, 89, 110, 113, 114, 351,
352
DTEG.plot, 86, 88, 89, 112, 113, 351, 352
entropy, 44, 46, 64, 100–102, 115, 164,
166–168, 204, 205, 210, 211, 215,
253, 273, 298, 299, 337, 339, 343,
347, 362
envExp, 116, 256
envExp,experiment-method, 116
envExp<-, 117
envExp<-,experiment-method, 117
exists.ftp.dir.fast, 118
exists.ftp.file.fast, 118
experiment, 13, 14, 22, 24, 33, 43, 45, 47, 51,
55–58, 60, 61, 65, 67, 80, 85, 89, 91,
92, 97, 110, 111, 116, 117, 121, 137,
144, 145, 157, 171, 193, 195, 214,
216, 220–222, 231, 237, 241–243,
248–251, 254, 255, 258, 260,
265–269, 271, 273, 274, 277, 286,
292, 296, 301, 302, 307, 308, 311,
388 INDEX
314, 315, 317, 318, 339, 348–350,
357, 359, 360, 363, 366, 373
experiment (experiment-class), 119
experiment-class, 119
experiment.colors, 121, 260, 357, 359
export.bed12, 25, 54, 122, 125, 126, 132,
143, 144, 169, 247, 275, 276, 279
export.bedo, 52, 123, 319
export.bedoc, 52, 124, 319
export.bigWig, 25, 54, 122, 124, 126, 132,
143, 144, 169, 247, 275, 276, 279
export.fstwig, 25, 54, 122, 125, 126, 132,
143, 144, 169, 247, 275, 276, 279
export.ofst, 52, 127, 319
export.ofst,GAlignmentPairs-method,
128
export.ofst,GAlignments-method, 129
export.ofst,GRanges-method, 130
export.wiggle, 25, 52, 54, 122, 125, 126,
131, 143, 144, 169, 247, 275, 276,
279, 312, 319
extendLeaders, 21, 73, 132, 134, 283, 353,
366, 378
extendsTSSexons, 133
extendTrailers, 21, 73, 133, 134, 283, 353,
366, 378
extract_run_id, 135
extractTranscriptSeqs, 366
f, 136
f,covRle-method, 136
FaFile, 14, 33, 43, 45, 144, 146, 150, 155,
214, 216, 339, 366
filepath, 23, 82, 120, 137, 222, 249, 250,
254, 257, 274, 303, 373
filterCage, 138
filterExtremePeakGenes, 139
filterTranscripts, 140
filterUORFs, 11, 142, 288–290, 370
fimport, 25, 54, 122, 125, 126, 132, 142, 144,
169, 247, 275, 276, 279
find_url_ebi, 158
find_url_ebi_safe, 159
findFa, 25, 54, 122, 125, 126, 132, 143, 144,
169, 247, 275, 276, 279
findFromPath, 145
findLibrariesInFolder, 145
findMapORFs, 146, 149, 150, 152, 156, 158,
336, 342
findMaxPeaks, 148
findNewTSS, 148
findNGSPairs, 149
findORFs, 147, 149, 152, 156, 158, 336, 342
findORFsFasta, 147, 150, 151, 156, 158, 336,
342
findPeaksPerGene, 153
findUORFs, 147, 150, 152, 154, 336, 342
findUORFs_exp, 156
firstEndPerGroup, 160
firstExonPerGroup, 160
firstStartPerGroup, 161
fix_malformed_gff, 162
flankPerGroup, 162
floss, 44, 46, 64, 100–102, 115, 163,
166–168, 204, 205, 210, 211, 215,
253, 273, 298, 299, 337, 339, 343,
347, 362
footprints.analysis, 164
fpkm, 44, 46, 64, 100–102, 115, 164, 165, 167,
168, 204, 205, 210, 211, 215, 253,
273, 298, 299, 337, 339, 343, 347,
362
fpkm_calc, 44, 46, 64, 100–102, 115, 164,
166, 166, 168, 204, 205, 210, 211,
215, 253, 273, 298, 299, 337, 339,
343, 347, 362
fractionLength, 44, 46, 64, 100–102, 115,
164, 166, 167, 167, 204, 205, 210,
211, 215, 253, 273, 298, 299, 337,
339, 343, 347, 362
fractionNames, 24, 26, 27, 47, 168, 203, 221,
229, 292, 322, 353
fread.bed, 25, 54, 122, 125, 126, 132, 143,
144, 169, 247, 275, 276, 279
GAlignmentPairs, 143, 201, 256, 275
GAlignments, 14, 30, 43, 45, 69, 72, 94, 115,
143, 163, 165, 196, 198, 203, 252,
256, 275, 284, 297, 299, 304, 310,
361, 379
GappedReads, 143, 256, 275
gcContent, 170
geneToSymbol, 170
get_bioproject_candidates, 26, 104, 105,
107, 180, 207, 291
get_genome_fasta, 181
get_genome_gtf, 183
get_noncoding_rna, 186
INDEX 389
get_phix_genome, 188
get_silva_rRNA, 189
getGAlignments, 172
getGAlignmentsPairs, 173
getGenomeAndAnnotation, 174, 207, 326,
330–335
getGRanges, 178
getGtfPathFromTxdb, 178
getNGenesCoverage, 179
getWeights, 43, 179, 203, 253
GRanges, 14, 25, 30, 43, 45, 69, 72, 115, 143,
163, 165, 169, 196, 198, 252, 276,
279, 284, 297, 299, 304, 310, 361,
367, 379
GRangesList, 14, 17, 18, 21, 30, 43, 45, 68,
69, 72, 100–102, 108, 109, 115, 132,
134, 146, 160–163, 165, 167, 192,
193, 198, 203, 205, 214, 216–218,
228, 230, 232, 234, 235, 243, 244,
247, 248, 252, 272, 283, 284, 294,
297–299, 301, 304, 309, 319, 321,
335, 337–341, 343, 344, 346, 352,
357, 360, 361, 364, 366–368, 372,
374, 375, 377, 379
groupGRangesBy, 190
groupings, 191
gSort, 192
hasHits, 192
heatMap_single, 71, 195, 197, 197
heatMapL, 71, 193, 197, 199
heatMapRegion, 71, 195, 195, 199
import.bed, 169
import.bedo, 199
import.bedoc, 199
import.fstwig, 200
import.ofst, 201
importGtfFromTxdb, 202
inhibitorNames, 24, 26, 27, 47, 168, 202,
221, 229, 292, 322, 353
initiationScore, 44, 46, 64, 100–102, 115,
164, 166–168, 203, 205, 210, 211,
215, 253, 273, 298, 299, 337, 339,
343, 347, 362
insideOutsideORF, 44, 46, 64, 100–102, 115,
164, 166–168, 204, 204, 210, 211,
215, 253, 273, 298, 299, 337, 339,
343, 347, 362
install.fastp, 177, 182, 185, 187, 189, 206,
326, 330–335
install.sratoolkit, 26, 104, 105, 107, 180,
207, 291
IRanges, 149
IRangesList, 149
is.gr_or_grl, 29, 208, 208, 209, 374
is.grl, 29, 208, 208, 209, 374
is.ORF, 29, 208, 209, 209, 374
is.range, 29, 208, 209, 209, 374
isInFrame, 44, 46, 64, 100–102, 115, 164,
166–168, 204, 205, 210, 211, 215,
253, 273, 298, 299, 337, 339, 343,
347, 362
isOverlapping, 44, 46, 64, 100–102, 115,
164, 166–168, 204, 205, 210, 211,
215, 253, 273, 298, 299, 337, 339,
343, 347, 362
isPeriodic, 95, 212
kozak_IR_ranking, 216
kozakHeatmap, 213
kozakSequenceScore, 44, 46, 64, 100–102,
115, 164, 166–168, 204, 205, 210,
211, 214, 253, 273, 298, 299, 337,
339, 343, 347, 362
lastExonEndPerGroup, 216
lastExonPerGroup, 217
lastExonStartPerGroup, 218
length,covRle-method, 218
length,covRleList-method, 219
lengths,covRle-method, 219
lengths,covRleList-method, 220
libFolder, 220
libFolder,experiment-method, 221
libNames, 24, 26, 27, 47, 168, 203, 221, 229,
292, 322, 353
libraryTypes, 23, 82, 120, 138, 222, 249,
250, 254, 257, 274, 303, 373
list.experiments, 81, 222
list.genomes, 223
loadRegion, 224
loadRegions, 225
loadTranscriptType, 227
loadTxdb, 227
longestORFs, 84, 98, 147, 150, 152, 155, 157,
228, 234, 248, 336, 340, 341, 344,
364, 367, 368
390 INDEX
mainNames, 24, 26, 27, 47, 168, 203, 221, 229,
292, 322, 353
makeExonRanks, 230
makeORFNames, 230
makeSummarizedExperimentFromBam, 65,
231
makeTxdbFromGenome, 171, 172, 233
mapToGRanges, 84, 229, 234, 248, 336, 340,
341, 344, 364, 367, 368
matchColors, 235
matchNaming, 235
matchSeqStyle, 236
mergeFastq, 236
mergeLibs, 237
metadata.autnaming, 239
metaWindow, 75, 239, 285, 305, 380
model.matrix,experiment-method, 241
name, 242
name,experiment-method, 242
nrow,experiment-method, 243
numCodons, 243
numExonsPerGroup, 244
ofst_merge, 244
optimized_txdb_path, 246
optimizedTranscriptLengths, 245
optimizeReads, 25, 54, 122, 125, 126, 132,
143, 144, 169, 247, 275, 276, 279
orfFrameDistributions, 247
orfID, 84, 229, 234, 248, 336, 340, 341, 344,
364, 367, 368
ORFik (ORFik-package), 10
ORFik-package, 10
ORFik.template.experiment, 23, 82, 120,
138, 222, 249, 250, 254, 257, 274,
303, 373
ORFik.template.experiment.zf, 23, 82,
120, 138, 222, 249, 249, 254, 257,
274, 303, 373
ORFikQC, 64, 250
orfScore, 44, 46, 64, 100–102, 115, 164,
166–168, 204, 205, 210, 211, 215,
252, 273, 298, 299, 337, 339, 343,
347, 362
organism,experiment-method, 254
outputLibs, 23, 82, 120, 138, 222, 249, 250,
254, 255, 274, 303, 373
pasteDir, 257
pcaExperiment, 86, 89, 91, 111, 258
percentage_to_ratio, 259
plotHelper, 259
pmapFromTranscriptF, 260
pmapToTranscriptF, 261
prettyScoring, 263
pseudo.transform, 263
pSitePlot, 71, 264, 304, 376
QC_count_tables, 270
QCfolder, 265
QCfolder,experiment-method, 266
QCplots, 251, 266, 269
QCreport, 267, 267, 269
QCstats, 250, 251, 267, 269, 269
QCstats.plot, 270
r, 271
r,covRle-method, 272
rankOrder, 44, 46, 64, 100–102, 115, 164,
166–168, 204, 205, 210, 211, 215,
253, 272, 298, 299, 337, 339, 343,
347, 362
read.experiment, 23, 82, 120, 138, 222, 249,
250, 254, 257, 273, 303, 373
readBam, 25, 54, 122, 125, 126, 132, 143, 144,
169, 247, 274, 276, 279
readBigWig, 25, 54, 122, 125, 126, 132, 143,
144, 169, 247, 275, 276, 279
readGAlignments, 274
readLengthTable, 277
readWidths, 277
readWig, 25, 54, 122, 125, 126, 132, 143, 144,
169, 247, 275, 276, 278
reassignTSSbyCage, 20, 279, 282
reassignTxDbByCage, 20, 280, 281
reduce, 283
reduceKeepAttr, 21, 73, 133, 134, 282, 353,
366, 378
regionPerReadLength, 75, 241, 284, 305,
380
remakeTxdbExonIds, 285
remove.experiments, 286
remove.file_ext, 287
removeMetaCols, 287
removeORFsWithinCDS, 11, 142, 288, 289,
290, 370
INDEX 391
removeORFsWithSameStartAsCDS, 11, 142,
288, 288, 289, 290, 370
removeORFsWithSameStopAsCDS, 11, 142,
288, 289, 289, 290, 370
removeORFsWithStartInsideCDS, 11, 142,
288, 289, 289, 370
removeTxdbExons, 290
removeTxdbTranscripts, 290
rename.SRA.files, 26, 104, 105, 107, 180,
207, 291
repNames, 24, 26, 27, 47, 168, 203, 221, 229,
291, 322, 353
resFolder, 292
resFolder,experiment-method, 292
restrictTSSByUpstreamLeader, 293
revElementsF, 293
reverseMinusStrandPerGroup, 294
ribo_fft, 299
ribo_fft_plot, 300
riboORFs, 294
riboORFsFolder, 295
RiboQC.plot, 295
ribosomeReleaseScore, 44, 46, 64, 100–102,
115, 164, 166–168, 204, 205, 210,
211, 215, 253, 273, 297, 299, 337,
339, 343, 347, 362
ribosomeStallingScore, 44, 46, 64,
100–102, 115, 164, 166–168, 204,
205, 210, 211, 215, 253, 273, 298,
298, 337, 339, 343, 347, 362
rnaNormalize, 301
runIDs, 301
runIDs,experiment-method, 302
save.experiment, 23, 82, 120, 138, 222, 249,
250, 254, 257, 274, 302, 373
savePlot, 71, 265, 303, 376
scaledWindowPositions, 75, 241, 285, 304,
380
scanBam, 143, 256, 275
ScanBamParam, 143, 256, 275
scoreSummarizedExperiment, 305
Seqinfo, 143, 169, 228, 236, 256, 275, 276,
279
seqinfo,covRle-method, 306
seqinfo,covRleList-method, 306
seqinfo,experiment-method, 307
seqlevels,covRle-method, 307
seqlevels,covRleList-method, 308
seqlevels,experiment-method, 308
seqlevelsStyle, 143, 169, 228, 236, 256,
275, 276, 279
seqnamesPerGroup, 309
shiftFootprints, 28, 95, 309, 313, 315, 316
shiftFootprintsByExperiment, 28, 95, 310,
311, 315, 316
shiftPlots, 28, 95, 310, 313, 314, 316
shifts.load, 28, 95, 310, 313, 315, 315
show,covRle-method, 316
show,covRleList-method, 316
show,experiment-method, 317
simpleLibs, 317
sort.GenomicRanges, 319
sortPerGroup, 132, 134, 319
splitIn3Tx, 320
stageNames, 24, 26, 27, 47, 168, 203, 221,
229, 292, 321, 353
STAR.align.folder, 177, 182, 185, 187, 189,
207, 322, 330–335
STAR.align.single, 177, 182, 185, 187, 189,
207, 326, 326, 331–335
STAR.allsteps.multiQC, 177, 182, 185, 187,
189, 207, 326, 330, 330, 332–335
STAR.index, 177, 182, 185, 187, 189, 207,
326, 330, 331, 331, 333–335
STAR.install, 177, 182, 185, 187, 189, 207,
326, 330–332, 333, 334, 335
STAR.multiQC, 177, 182, 185, 187, 189, 207,
326, 330–333, 334, 335
STAR.remove.crashed.genome, 177, 182,
185, 187, 189, 207, 326, 330–334,
334
startCodons, 84, 229, 234, 248, 335, 337,
340, 341, 344, 364, 367, 368
startDefinition, 98, 146, 147, 150, 152,
155–158, 336, 342
startRegion, 44, 46, 64, 100–102, 115, 164,
166–168, 204, 205, 210, 211, 215,
253, 273, 298, 299, 337, 339, 343,
347, 362
startRegionCoverage, 44, 46, 64, 100–102,
115, 164, 166–168, 204, 205, 210,
211, 215, 253, 273, 298, 299, 337,
338, 343, 347, 362
startRegionString, 339
startSites, 84, 229, 234, 248, 336, 340, 341,
344, 364, 367, 368
392 INDEX
stopCodons, 84, 229, 234, 248, 336, 340, 341,
342, 344, 364, 367, 368
stopDefinition, 98, 146, 147, 150, 152,
155–158, 336, 342
stopRegion, 44, 46, 64, 100–102, 115, 164,
166–168, 204, 205, 210, 211, 215,
253, 273, 298, 299, 337, 339, 342,
347, 362
stopSites, 84, 229, 234, 248, 336, 340, 341,
343, 364, 367, 368
strandBool, 344
strandMode,covRle-method, 345
strandMode,covRleList-method, 345
strandPerGroup, 346
subsetCoverage, 44, 46, 64, 100–102, 115,
164, 166–168, 204, 205, 210, 211,
215, 253, 273, 298, 299, 337, 339,
343, 346, 362
subsetToFrame, 347
SummarizedExperiment, 111, 119, 232, 250,
268
symbols, 348
symbols,experiment-method, 348
te.plot, 349
te.table, 86, 88, 89, 112, 114, 350, 351, 352
te_rna.plot, 86, 88, 89, 112, 114, 351, 351
tile1, 21, 73, 133, 134, 283, 352, 366, 378
tissueNames, 24, 26, 27, 47, 168, 203, 221,
229, 292, 322, 353
TOP.Motif.ecdf, 354
topMotif, 355
transcriptLengths, 245
transcriptWindow, 356, 359, 361
transcriptWindow1, 358, 358, 361
transcriptWindowPer, 358, 359, 360
translationalEff, 44, 46, 64, 100–102, 115,
164, 166–168, 204, 205, 210, 211,
215, 253, 273, 298, 299, 337, 339,
343, 347, 361
trim_detection, 363
trimming.table, 363
TxDb, 100, 205
txNames, 84, 229, 234, 248, 336, 340, 341,
344, 364, 367, 368
txNamesToGeneNames, 365
txSeqsFromFa, 21, 73, 133, 134, 283, 353,
366, 378
uniqueGroups, 84, 229, 234, 248, 336, 340,
341, 344, 364, 367, 368
uniqueOrder, 84, 229, 234, 248, 336, 340,
341, 344, 364, 367, 367
unlistGrl, 368
uORFSearchSpace, 11, 142, 288–290, 369
updateTxdbRanks, 370
updateTxdbStartSites, 371
upstreamFromPerGroup, 17, 18, 108, 110,
371, 373
upstreamOfPerGroup, 17, 18, 108, 110, 372,
372
validateExperiments, 23, 82, 120, 138, 222,
249, 250, 254, 257, 274, 303, 373
validGRL, 29, 208, 209, 373, 374
validSeqlevels, 29, 208, 209, 374, 374
widthPerGroup, 375
windowCoveragePlot, 71, 265, 304, 375
windowPerGroup, 21, 73, 133, 134, 283, 353,
366, 377
windowPerReadLength, 75, 241, 285, 305,
378
windowPerTranscript, 380
xAxisScaler, 381
yAxisScaler, 382
