search.gadem {cobindR} | R Documentation

## function performs TFBS prediction denovo or based on transfac / jaspar matrices pwms using rGADEM.

### Description

function performs TFBS prediction denovo or based on transfac / jaspar matrices pwms using rGADEM. If append=T, predicted hits are appended to the hits in the input object.

### Usage

    ## S4 method for signature 'cobindr'
    search.gadem(x, deNovo = FALSE, append = F, background_scan = FALSE)

### Arguments

`x` | an object of the class "cobindr", which will hold all necessary information about the sequences and the hits.
`deNovo` | logical flag, if deNOVO=TRUE a denovo search is startet. Otherwise the given PFMs are used as seed.
`append` | logical flag, if append=TRUE the binding sites will be appended to already existing results
`background_scan` | logical flag, if background_scan=TRUE the function will search for binding sites in the set of background sequences

### Value

`x` | an object of the class "cobindr" including the predicted transcription factor binding sites

### Author(s)

Robert Lehmann <r.lehmann@biologie.hu-berlin.de>

### References

uses package "rGADEM" (http://www.bioconductor.org/packages/release/bioc/html/rGADEM.html)

### See Also

`rtfbs`, `search.pwm`

### Examples

    ############################################################
    # use simulated sequences
    library(Biostrings)

    n <- 600 # number of input sequences
    l <- 150 # length of sequences
    n.hits <- 600 # number of 'true' binding sites
    bases <- c("A","C","G","T") # alphabet
    # generate random input sequences with two groups with differing GC content
    seqs <- sapply(1:(3*n/4), function(x) paste(sample(bases, l, replace=TRUE,
    		prob=c(.3,.22,.2,.28)), collapse=""))
    seqs <- append(seqs, sapply(1:(n/4), function(x) paste(sample(bases, l,
    		replace=TRUE, prob=c(.25,.25,.25,.25)), collapse="")))
    path <- system.file('extdata/pfms/myod.tfpfm',package='cobindR')
    motif <- read.transfac.pfm(path)[[1]] # get PFM of binding site
    # add binding sites with distance specificity
    for(position in c(70, 90)) {
    	hits <- apply(apply(motif, 2, function(x) sample(x=bases, size=n.hits,
    			prob=x, replace=TRUE)), 1, paste, collapse='')
    	pos.hits <- round(rnorm(n.hits, mean=position, sd=8))
    	names(pos.hits) <- sample(1:n, n.hits)
    	for(i in 1:n.hits) substr(seqs[as.integer(names(pos.hits)[i])], start=pos.hits[i],
    							stop=pos.hits[i]+ncol(motif)) <- hits[i]
    }
    #save sample sequences in fasta file
    tmp.file <- tempfile(pattern = "cobindr_sample_seq", tmpdir = tempdir(), fileext = ".fasta")
    writeXStringSet(DNAStringSet(seqs), tmp.file)
    #run cobindr
    cfg <- cobindRConfiguration()
    sequence_type(cfg) <- 'fasta'
    sequence_source(cfg) <- tmp.file
    sequence_origin(cfg) <- 'artificial sequences'
    pfm_path(cfg) <- system.file('extdata/pfms',package='cobindR')
    pairs(cfg) <- 'V$MYOD_01 V$MYOD_01'
    runObj <-cobindr(cfg, name='cobindr test using sampled sequences')

    # perform tfbs prediction using rGADEM - commented out due to long time required
    # runObj.bs <- search.gadem(runObj)
    # show results
    # plot.positions(runObj.bs)

    #clean up
    unlink(tmp.file)

[Package cobindR version 1.18.0 Index]