groHMM (version 1.6.0)

detectTranscripts: detectTranscripts detects transcripts de novo using a two-state hidden Markov model (HMM).

Description

Read counts can be specified as either a GRanges object (reads), or using a fixed-step wiggle-format passed in a list (Fp and Fm). Either reads or BOTH Fp and Fm must be specified.

Usage

detectTranscripts(reads = NULL, Fp = NULL, Fm = NULL, LtProbA = -5, LtProbB = -200, UTS = 5, size = 50, threshold = 0.1, debug = TRUE, ...)

Arguments

reads

A GRanges object representing a set of mapped reads.

Fp

Wiggle-formatted read counts on "+" strand. Optionally, Fp and Fm represent list() filled with a vector of counts for each chromosome. Can detect transcripts starting from a fixed-step wiggle.

Fm

Wiggle-formatted read counts on "-" strand.

LtProbA

Log probability of t... . Default: -5. One of these is just an initialization, and the final value is set by EM. The other is a holdout parameter.

LtProbB

Log probability of t... . Default: -200.

UTS

Varience in read counts of the untranscribed sequence. Default: 5.

size

Log probability of t... . Default: -5.

threshold

Threshold change in total likelihood, below which EM exits.

debug

If set to TRUE, provides additional print options. Default: FALSE

...

Extra argument passed to mclapply

Value

Returns a list of emisParams, trnasParams, viterbiStates, and transcripts. The transcript element is a GRanges object representing the predicted genomic coordinates of transcripts on both the + and - strand.

Details

Supports parallel processing using mclapply in the 'parallel' package. To change the number of processors set the option 'mc.cores'.

Reference: Hah N, Danko CG, Core L, Waterfall JJ, Siepel A, Lis JT, Kraus WL. A rapid, extensive, and transient transcriptional response to estrogen signaling in breast cancer cells. Cell. 2011 May 13;145(4):622-34. doi: 10.1016/j.cell.2011.03.042.

Examples

S0mR1 <- as(readGAlignments(system.file("extdata", "S0mR1.bam",
               package="groHMM")), "GRanges")
## Not run:
# hmmResult <- detectTranscripts(S0mR1, LtProbB=-200, UTS=5, threshold=1)
# txHMM <- hmmResult$transcripts