sliceCounts: Slices out count data from riboCoding object for use in...
In riboSeqR: Analysis of sequencing data from ribosome profiling experiments

Description Usage Arguments Details Value Author(s) See Also Examples

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.

1	sliceCounts(rC, lengths = 27, frames)

`rC`	A `riboCoding` object containing the coordinates of the coding sequences and the number of ribosomal footprint reads of various classes to be found in each.
`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

rnaCounts

#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))