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BitSeq (version 1.16.0)

parseAlignment: Compute probabilities of alignments

Description

Compute probability of alignments and save them into .prob file.

Usage

parseAlignment( alignFile, outFile, trSeqFile, inputFormat=NULL, trInfoFile=NULL, expressionFile=NULL, readsN=NULL, uniform=TRUE, limitA=NULL, lenMu=NULL, lenSigma=NULL, excludeSingletons=NULL, mateNamesDiffer=NULL, verbose=NULL, veryVerbose=NULL, procN=NULL, pretend=FALSE)

Arguments

alignFile
File containing read alignments.
outFile
Name of the output file.
inputFormat
Input format: possible values SAM, BAM. (This should be detected automatically in most cases.)
trInfoFile
File to save transcript information extracted from [BS]AM file and reference.
trSeqFile
File containing transcript sequence in FASTA format.
expressionFile
Transcript relative expression estimates --- for better non-uniform read distribution estimation.
readsN
Total number of reads. This is usually not necessary if SAM/BAM contains also reads with no valid alignments.
uniform
Use uniform read distribution.
limitA
Limit maximum number of alignments per read. Reads with more alignments than limit will be discarded.
lenMu
Set mean of log fragment length distribution. $l_frag ~ LogNormal(mu,sigma^2)$
lenSigma
Set $sigma^2$ (or variance) of log fragment length distribution. $l_frag ~ LogNormal(mu,sigma^2)$
excludeSingletons
Exclude single mate alignments for paired-end reads.
mateNamesDiffer
Mates from paired-end reads have different names.
verbose
Verbose output.
veryVerbose
Very verbose output.
procN
Maximum number of threads to be used.
pretend
Do not execute, only print out command line calls for the C++ version of the program.

Value

.prob
file containing the alignment probabilities
.tr
file containing reference transcript names, lengths and effective lengths - optional

Details

This function uses the alignments and reference file to assign probability to each alignment. It uses either bias-corrected or uniform model for the read distribution, assumes Log-Normal distribution of fragment lengths for pair-end read data and uses quality scores and mismatches to assign probability for every alignment of a read (or fragment) to a transcript.

See Also

estimateExpression