sliceCounts {riboSeqR} | R Documentation |
For any given coding sequence, multiple lengths of reads in various frames (relative to coding start) may align. This function extracts specific size-classes and frames of ribosome footprint reads and sums them to give a single value for each coding sequence and each sequencing library, for use in downstream analysis.
sliceCounts(rC, lengths = 27, frames)
rC |
A |
lengths |
Lengths of ribosome footprints to inform count data. |
frames |
Frames of ribosome footprints (relative to coding start site). If omitted, all frames are used. |
Frames can be given as a single vector (which specifies the frames used for all lengths of footprints, or as a list, specifying the frame for each length given in ‘lengths’.
The count data thus acquired can be compared to counts of RNA-seq data through a beta-binomial analysis (see vignette) to discover differential translation.
A matrix containing counts of ribosomal footprint matches to coding sequences specified in riboCoding object ‘rC’.
Thomas J. Hardcastle
#ribosomal footprint data datadir <- system.file("extdata", package = "riboSeqR") ribofiles <- paste(datadir, "/chlamy236_plus_deNovo_plusOnly_Index", c(17,3,5,7), sep = "") rnafiles <- paste(datadir, "/chlamy236_plus_deNovo_plusOnly_Index", c(10,12,14,16), sep = "") riboDat <- readRibodata(ribofiles, rnafiles, replicates = c("WT", "WT", "M", "M")) # CDS coordinates chlamyFasta <- paste(datadir, "/rsem_chlamy236_deNovo.transcripts.fa", sep = "") fastaCDS <- findCDS(fastaFile = chlamyFasta, startCodon = c("ATG"), stopCodon = c("TAG", "TAA", "TGA")) # frame calling fCs <- frameCounting(riboDat, fastaCDS) # analysis of frame shift for 27 and 28-mers. fS <- readingFrame(rC = fCs, lengths = 27:28) # filter coding sequences. 27-mers are principally in the 1-frame, # 28-mers are principally in the 0-frame relative to coding start (see # readingFrame function). ffCs <- filterHits(fCs, lengths = c(27, 28), frames = list(1, 0), hitMean = 50, unqhitMean = 10, fS = fS) # Extract counts of ribosomal footprints from riboCount data. riboCounts <- sliceCounts(ffCs, lengths = c(27, 28), frames = list(0, 2))