Tet1_CpG_promoters.py

def TSS_Profile(ifile1,ifile2):
    '''read in three files, ifile1 is the sortedbamfile prepared by samtool
    ifile2 is the promoters (upstream 5kb of TSS) bed  file with five  columns: chr, start ,end, name and  strand'''
    
    import HTSeq
    import numpy
    import itertools

    sortedbamfile=HTSeq.BAM_Reader(ifile1)
    promoters = open(ifile2)
    halfwinwidth=5000
    fragmentsize=200

        
    promoters_pos=set() 
    for line in promoters:
        linelist=line.split()
        if linelist[4] == "+":
            promoters_pos.add(HTSeq.GenomicPosition(linelist[0],int(linelist[2]),linelist[4]))
        else:
            promoters_pos.add(HTSeq.GenomicPosition(linelist[0],int(linelist[1]),linelist[4]))
        
            
    for promoter in itertools.islice(promoters_pos,10):
        print promoter  # print out 10 promoters

    profile=numpy.zeros(2*halfwinwidth, dtype='i')
    for p in promoters_pos:
        try:
            window=HTSeq.GenomicInterval(p.chrom, p.pos-halfwinwidth-fragmentsize,p.pos+halfwinwidth + fragmentsize,".")
            for almnt in sortedbamfile[window]:
                almnt.iv.length=fragmentsize
                if p.strand=="+":
                    start_in_window=almnt.iv.start- p.pos +halfwinwidth
                    end_in_window  =almnt.iv.end  - p.pos +halfwinwidth
                else:
                    start_in_window=p.pos+halfwinwidth-almnt.iv.end
                    end_in_window =p.pos+halfwinwidth-almnt.iv.start 
                start_in_window=max(start_in_window,0)
                end_in_window=min(end_in_window, 2*halfwinwidth)
                if start_in_window >= 2*halfwinwidth or end_in_window <0:
                    continue
                profile[start_in_window : end_in_window] +=1
        except:
           continue
    return profile
    ifile1.close()
    ifile2.close()

def aligned_counts(ifile1):
    '''count how many alignments are aligned back to genome, ifile1 is a sorted bam file'''
    import HTSeq
    sortedbamfile= HTSeq.BAM_Reader(ifile1)
    aligned_counts=0
    unaligned_counts=0
    for almnt in sortedbamfile:
        if almnt.aligned:
            aligned_counts+= 1
        else:
            unaligned_counts+=1
    print "number of aligned tags of %s is %d " % (ifile1, aligned_counts)
    print "number of unaligned tags of %s is %d "% (ifile1, unaligned_counts)
    return aligned_counts

input_bamfile=("/home/tommy/Tet1/Tet1.sorted.bam")
    
counts=aligned_counts(input_bamfile)

halfwinwidth=5000

profile1=TSS_Profile(input_bamfile,\
                     "/home/tommy/Tet1/CpG_rich_promoters.bed")
profile1_normalized= (profile1 *1000000.0/counts) /16171.0
#normalize to count per million (cpm)
profile2=TSS_Profile(input_bamfile,\
                     "/home/tommy/Tet1/CpG_poor_promoters.bed")
profile2_normalized= (profile2*1000000.0/counts) /16384.0                 


from matplotlib import pyplot
import numpy


line1=pyplot.plot(numpy.arange(-halfwinwidth, halfwinwidth), profile1_normalized, color="red",label="CpG_rich_promoters")
line2=pyplot.plot(numpy.arange(-halfwinwidth, halfwinwidth), profile2_normalized, color="blue",label="CpG_poor_promoters")

pyplot.legend()
pyplot.xlabel("distance related to TSS  bp")
pyplot.ylabel("Tet1 tag density cpm")
pyplot.title("Tet1 ChIP-seq intensity around TSS ")


pyplot.show()