TRAP

Modified code from the TF affinity prediction

See also http://www.molgen.mpg.de/~manke/papers/TFaffinities/

Batch processing

In order to analyze multiple sequences, one has to first extract the promoter sequences, eg.

human.promoter <- read.DNAStringSet('~/data/fimo/human_upstream1000.fa')

where human_upstream1000.fa is the whole genome promoter sequence file, one can then pick her genes of interest (eg. all upregulated genes) and find out the ENTREZ IDs

tf.entrez <- unlist(mget(as.character(df.uptime$gene), org.Hs.egSYMBOL2EG, 
    ifnotfound=NA))
tf.entrez <- tf.entrez[!is.na(tf.entrez)]

which have to be converted to REFSEQ IDs to match the promoter file

tf.refseq <- unlist(mget(tf.entrez, org.Hs.egREFSEQ))
tf.refseq <- tf.refseq[grep('NM',tf.refseq)]
idx <- grep(paste(tf.refseq,collapse='_|'), names(human.promoter))

Here we define a function to convert REFSEQ IDs back to gene symbols, such that our sequence files will have the consistent gene names

refseq2symbol <- function(refseq.str) {
    all.str <- unlist(strsplit(refseq.str, '_'))
    refseq <- paste(all.str[1:2], collapse='_')
    entrez <- unlist(mget(refseq,org.Hs.egREFSEQ2EG,ifnotfound=NA))
    if (is.na(entrez))
        return('')
    else
        return(get(entrez, org.Hs.egSYMBOL))
}

Now we can define our output directory and dump the sequence files into it

wd <- '~/data/hdf/qpcr_seq/'
for (i in 1:length(idx)) {
    symbol <- refseq2symbol(names(human.promoter)[idx[i]])
    if (symbol != '') {
        seq.file <- paste(wd,symbol,'.fa',sep='')
        write.XStringSet(human.promoter[idx[i]], seq.file)
    }
    symbol
}

With all that in place, we can then execute the TRAP program on all sequence files (eg. using the parallel functionality in foreach)

all.file <- dir(wd, 'fa')
foreach(i=1:length(all.file)) %dopar% {
    seq.file <- paste(wd,all.file[i],sep='')
    res.file <- paste(wd,sub('fa','trap',all.file[i]),sep='')
    system(paste('./TRAP ~/data/TRANSFAC/TFP_2012.3/dat/matrix.dat',seq.file,
        '>',res.file))
}

where the matrix.dat is the position score matrix provided by TRANSFAC, and the result files will have the extension of .trap, while locating in the same directory. The result files can then be individually processed to calculate the normalized affinity score.