rtfbs {cobindR} | R Documentation

## function performs TFBS prediction using the package rtfbs

### Description

function performs TFBS prediction using the package rtfbs

### Usage

    ## S4 method for signature 'cobindr'
    rtfbs(x, append = F, background_scan = FALSE, n.cpu = NA)

### Arguments

`x` | an object of the class "cobindr", which will hold all necessary information about the sequences and the hits.
`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 background sequences will be searched for transcription factor binding sites
`n.cpu` | number of CPUs to be used for parallelization. Default value is 'NA' in which case the number of available CPUs is checked and than used.

### Value

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

### Author(s)

Yue-Hien Lee <>

### References

uses the package "rtfbs" (http://cran.r-project.org/web/packages/rtfbs/index.html)

### See Also

`search.pwm`, `search.gadem`

### Examples

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

    n <- 400 # number of input sequences
    l <- 500 # length of sequences
    n.hits <- 250 # 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(110, 150)) {
    	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'
    fdrThreshold(cfg) <- 0
    runObj <- cobindr(cfg, name='cobindr test using sampled sequences')
    # perform tfbs prediction using rtfbs
    runObj.bs <- rtfbs(runObj)
    # show results
    plot.positionprofile(runObj.bs)

    #clean up
    unlink(tmp.file)

[Package cobindR version 1.18.0 Index]