rnaCounts {riboSeqR} | R Documentation |
Takes mRNA count data from riboDat object, maps them to coding sequences specified in GRanges object, and counts the total number of hits. This is a crude approach intended to quickly produce comparable data to ribosome footprint counts. More sophisticated alternatives, addressing coverage variation, isoforms, multireads &c. have been widely described in the literature on mRNA-seq analyses.
rnaCounts(riboDat, CDS)
riboDat |
A |
CDS |
A |
The count data thus acquired can be compared to counts of ribosomal footprint data through a beta-binomial analysis (see vignette) to discover differential translation.
A matrix containing count data for the RNA-seq libraries.
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 RNA hits from riboCount data. rnaCounts <- rnaCounts(riboDat, ffCs@CDS)