findMotif {motifRG} | R Documentation

## De-novo discovery of distriminative motifs

### Description

The function searches motifs that discriminate the given foregound and background sequences.

### Usage

    findMotif(all.seq,  category, weights = rep(1, length(all.seq)),
    start.width=6,min.cutoff=5, min.ratio=1.3,
    min.frac=0.01, both.strand=TRUE, flank=2, max.motif=5,
    mask=TRUE,other.data=NULL, start.nmer=NULL,
    enriched.only=F,n.bootstrap = 5, bootstrap.pvalue=0.1,is.parallel =
    TRUE,mc.cores = 4,min.info=10,max.width=15,discretize=TRUE)

### Arguments

`all.seq` | DNAStringSet; foreground and background sequences.
`category` | numeric vector; specify which sequences are foreground (with value 1), and background (value 0).
`weights` | numeric vector: the weights for all sequences. Default: 1
`start.width` | logical; the width for enumerating seed patterns
`min.cutoff` | numeric; the score cutoff required for seed selection. All scores are negative, the lower the better.
`min.ratio` | numeric; the minimum fold change of motif occurences in foreground vs background.
`min.frac` | numeric; the minimum fraction of fg/bg sequences containing the candidate motifs
`both.strand` | logical; if true, search both strands
`flank` | integer; the length for step-wise pattern extension at both ends on candidate motifs
`max.motif` | integer; the maximum number of output motifs
`mask` | logical; if true, mask previous motifs when searching for the next motif
`other.data` | if not NULL, a matrix with additional terms for the regression model for bias adjustment
`start.nmer` | if not NULL, a matrix with counts for user specified seed pattern in each sequence
`enriched.only` | logical; if true, only predict enriched motif
`n.bootstrap` | integer; the number of bootstrapping tests to estimate score variance
`bootstrap.pvalue` | numeric: the bootstrap t.test pvalues to determine the significance of improvement
`is.parallel` | logical;if true, runs in parallel mode, and requires "parallel" library
`mc.cores` | integer; the number of CPUs for paralel run
`min.info` | minimal information content for the motif to prevent it from being too degenerate
`max.width` | maximum width of the motif for extension
`discretize` | `logical` default TRUE

### Value

return a list with following elements:

`motifs` | a list motif descriptions of class Motif-class

.

`category` | input binary specification of foreground/background
`mask.motifs` | if `mask=T`, then mask.motifs contain the description of motif is based on motif matches after the input sequences being masked by previous motifs. In this case, "motifs" contained the unmasked motif descriptions.

### Examples

    MD.peak.seq <- readDNAStringSet(system.file("extdata","MD.peak.fa", package="motifRG"))
    MD.control.seq <- readDNAStringSet(system.file("extdata","MD.control.fa", package="motifRG"))
    category <- c(rep(1, length(MD.peak.seq)), rep(0, length(MD.control.seq)))
    MD.motifs <- findMotif(append(MD.peak.seq, MD.control.seq),category, max.motif=3,enriched=TRUE)

    ### Get summary of motifs
    summaryMotif(MD.motifs$motifs, MD.motifs$category)

    ### plot the dinucleotide representation of the first motif
    plotMotif(MD.motifs$motifs[[1]]@match$pattern)

    ### Create table of motifs in Latex
    motifLatexTable(MD.motifs, main="MD motifs")

    ### Create table of motifs in Html
    motifHtmlTable(MD.motifs)

[Package motifRG version 1.30.0 Index]