def main():
usage = 'usage: %prog [options] <bam_file>'
parser = OptionParser(usage)
parser.add_option(
'-g',
dest='filter_gff',
help=
'Filter the TEs by overlap with genes in the given gff file [Default: %default]'
)
parser.add_option('-s',
dest='strand_split',
default=False,
action='store_true',
help='Split statistics by strand [Default: %default]')
parser.add_option('-t',
dest='te_gff',
default='%s/hg19.fa.out.tp.gff' % os.environ['MASK'])
#parser.add_option('-f', dest='family', help='Limit to this family')
#parser.add_option('-r', dest='repeat', help='Limit to this repeat')
(options, args) = parser.parse_args()
if len(args) != 1:
parser.error('Must provide BAM file.')
else:
bam_file = args[0]
############################################
# GFF filter
############################################
# filter TEs and read alignments by gff file
if options.filter_gff:
filter_merged_bed_fd, filter_merged_bed_file = tempfile.mkstemp()
subprocess.call('sortBed -i %s | mergeBed -i - > %s' %
(options.filter_gff, filter_merged_bed_file),
shell=True)
# filter TE GFF
te_gff_fd, te_gff_file = tempfile.mkstemp(
dir='%s/research/scratch/temp' % os.environ['HOME'])
subprocess.call('intersectBed -a %s -b %s > %s' %
(options.te_gff, filter_merged_bed_file, te_gff_file),
shell=True)
options.te_gff = te_gff_file
# filter BAM
bam_gff_fd, bam_gff_file = tempfile.mkstemp(
dir='%s/research/scratch/temp' % os.environ['HOME'])
bedtools.abam_f1(bam_files[i], filter_merged_bed_file, bam_gff_file)
bam_file = bam_gff_file
os.close(filter_merged_bed_fd)
os.remove(filter_merged_bed_file)
############################################
# count TE fragments
############################################
fragments, te_fragments = count_te_fragments(bam_file, options.te_gff,
options.strand_split)
############################################
# print table
############################################
for line in open(options.te_gff):
a = line.split('\t')
te_chrom = a[0]
te_start = int(a[3])
if options.strand_split:
te_count = te_fragments.get((te_chrom, te_start, '+'), 0)
te_pct = te_count / float(fragments)
cols = (te_chrom, te_start, te_count, te_pct)
print '%-5s %9d + %6d %9.2e' % cols
te_count = te_fragments.get((te_chrom, te_start, '-'), 0)
te_pct = te_count / float(fragments)
cols = (te_chrom, te_start, te_count, te_pct)
print '%-5s %9d - %6d %9.2e' % cols
else:
te_count = te_fragments.get((te_chrom, te_start), 0)
te_pct = te_count / float(fragments)
cols = (te_chrom, te_start, te_count, te_pct)
print '%-5s %9d %6d %9.2e' % cols
############################################
# clean
############################################
if options.filter_gff:
os.close(te_gff_fd)
os.remove(te_gff_file)
os.close(bam_gff_fd)
os.remove(bam_gff_file)
def main():
usage = 'usage: %prog [options] <bam_file,bam_file2,...>'
parser = OptionParser(usage)
parser.add_option('-c', dest='control_bam_files', help='Control BAM file to paramterize null distribution [Default: %default]')
parser.add_option('-f', dest='filter_gff', help='Filter the TEs by overlap with genes in the given gff file [Default: %default]')
parser.add_option('-g', dest='genome', default='HG19', help='Genome directory to obtain lengths from [Default: %default]')
parser.add_option('-m', dest='mapq', default=False, action='store_true', help='Consider only reads with mapq>0 [Default: %default]')
parser.add_option('-r', dest='repeats_gff', default='%s/hg19.fa.out.tp.gff' % os.environ['MASK'])
parser.add_option('-s', dest='strand_split', default=False, action='store_true', help='Split statistics by strand [Default: %default]')
(options,args) = parser.parse_args()
if len(args) != 1:
parser.error('Must provide BAM files.')
else:
bam_files = args[0].split(',')
control_bam_files = []
if options.control_bam_files:
control_bam_files = options.control_bam_files.split(',')
############################################
# GFF filter
############################################
# filter TEs and read alignments by gff file
if options.filter_gff:
filter_merged_bed_fd, filter_merged_bed_file = tempfile.mkstemp()
subprocess.call('sortBed -i %s | mergeBed -i - > %s' % (options.filter_gff, filter_merged_bed_file), shell=True)
# filter TE GFF
te_gff_fd, te_gff_file = tempfile.mkstemp(dir='%s/research/scratch/temp' % os.environ['HOME'])
subprocess.call('intersectBed -a %s -b %s > %s' % (options.repeats_gff, filter_merged_bed_file, te_gff_file), shell=True)
options.repeats_gff = te_gff_file
# filter BAM
bam_gff_fds = [None]*len(bam_files)
bam_gff_files = [None]*len(bam_files)
for i in range(len(bam_files)):
bam_gff_fds[i], bam_gff_files[i] = tempfile.mkstemp(dir='%s/research/scratch/temp' % os.environ['HOME'])
bedtools.abam_f1(bam_files[i], filter_merged_bed_file, bam_gff_files[i])
bam_files[i] = bam_gff_files[i]
# filter control BAM
if control_bam_files:
cbam_gff_fds = [None]*len(control_bam_files)
cbam_gff_files = [None]*len(control_bam_files)
for i in range(len(control_bam_files)):
cbam_gff_fds[i], cbam_gff_files[i] = tempfile.mkstemp(dir='%s/research/scratch/temp' % os.environ['HOME'])
bedtools.abam_f1(control_bam_files[i], filter_merged_bed_file, cbam_gff_files[i])
control_bam_files[i] = cbam_gff_files[i]
############################################
# lengths
############################################
# estimate read length (just averaging across replicates for now)
read_lens = []
for bam_file in bam_files:
read_lens.append(estimate_read_length(bam_file))
read_len = stats.mean(read_lens)
# compute size of search space
if options.filter_gff:
genome_length = count_bed(filter_merged_bed_file, read_len)
else:
genome_length = count_genome(options.genome)
# hash counted repeat genomic bp
if options.filter_gff:
te_lengths = te_target_size_bed(options.repeats_gff, filter_merged_bed_file, read_len)
else:
te_lengths = te_target_size(options.repeats_gff, read_len)
############################################
# count TE fragments
############################################
fragments = []
te_fragments = []
for bam_file in bam_files:
rep_fragments, rep_te_fragments = count_te_fragments(bam_file, options.repeats_gff, options.strand_split)
fragments.append(rep_fragments)
te_fragments.append(rep_te_fragments)
if control_bam_files:
control_fragments = []
control_te_fragments = []
for control_bam_file in control_bam_files:
rep_fragments, rep_te_fragments = count_te_fragments(control_bam_file, options.repeats_gff, options.strand_split)
control_fragments.append(rep_fragments)
control_te_fragments.append(rep_te_fragments)
############################################
# combine replicates into fragment rates
############################################
te_fragment_rates = {}
for (rep,fam) in te_lengths:
if options.strand_split:
# positive
rate_list = [te_fragments[i].get((rep+'+',fam),1)/float(fragments[i]) for i in range(len(bam_files))]
te_fragment_rates[(rep+'+',fam)] = stats.geo_mean(rate_list)
# negative
rate_list = [te_fragments[i].get((rep+'-',fam),1)/float(fragments[i]) for i in range(len(bam_files))]
te_fragment_rates[(rep+'-',fam)] = stats.geo_mean(rate_list)
else:
rate_list = [te_fragments[i].get((rep,fam),1)/float(fragments[i]) for i in range(len(bam_files))]
te_fragment_rates[(rep,fam)] = stats.geo_mean(rate_list)
if control_bam_files:
control_te_fragment_rates = {}
for te in te_fragment_rates:
rate_list = [control_te_fragments[i].get(te,1)/float(control_fragments[i]) for i in range(len(control_bam_files))]
control_te_fragment_rates[te] = stats.geo_mean(rate_list)
############################################
# compute stats, print table
############################################
for (rep,fam) in te_fragment_rates:
# compute TE length
if options.strand_split:
te_len = te_lengths[(rep[:-1],fam)]
else:
te_len = te_lengths[(rep,fam)]
# parameterize null model
if options.control_bam_files:
null_rate = control_te_fragment_rates[(rep,fam)]
else:
if options.strand_split:
null_rate = float(te_lengths[(rep[:-1],fam)]) / (2*genome_length)
else:
null_rate = float(te_lengths[(rep,fam)]) / genome_length
# compute fragment counts
count = te_fragment_rates[(rep,fam)]*sum(fragments)
null_count = null_rate*sum(fragments)
# compute fold change
if null_rate > 0:
fold = te_fragment_rates[(rep,fam)]/null_rate
else:
fold = 0
# compute p-value of enrichment/depletion
p_val = 1.0
for i in range(len(bam_files)):
if te_fragment_rates[(rep,fam)] > null_rate:
p_val *= binom.sf(int(te_fragments[i].get((rep,fam),1))-1, int(fragments[i]), null_rate)
else:
p_val *= binom.cdf(int(te_fragments[i].get((rep,fam),1)), int(fragments[i]), null_rate)
cols = (rep, fam, te_len, count, null_count, fold, p_val)
print '%-18s %-18s %10d %10.1f %10.1f %10.3f %10.2e' % cols
############################################
# clean
############################################
if options.filter_gff:
os.close(filter_merged_bed_fd)
os.remove(filter_merged_bed_file)
os.close(te_gff_fd)
os.remove(te_gff_file)
for i in range(len(bam_files)):
os.close(bam_gff_fds[i])
os.remove(bam_gff_files[i])
if options.control_bam_files:
for i in range(len(control_bam_files)):
os.close(cbam_gff_fds[i])
os.remove(cbam_gff_files[i])
def main():
usage = 'usage: %prog [options] <bam_file>'
parser = OptionParser(usage)
parser.add_option('-g', dest='filter_gff', help='Filter the TEs by overlap with genes in the given gff file [Default: %default]')
parser.add_option('-s', dest='strand_split', default=False, action='store_true', help='Split statistics by strand [Default: %default]')
parser.add_option('-t', dest='te_gff', default='%s/hg19.fa.out.tp.gff' % os.environ['MASK'])
#parser.add_option('-f', dest='family', help='Limit to this family')
#parser.add_option('-r', dest='repeat', help='Limit to this repeat')
(options,args) = parser.parse_args()
if len(args) != 1:
parser.error('Must provide BAM file.')
else:
bam_file = args[0]
############################################
# GFF filter
############################################
# filter TEs and read alignments by gff file
if options.filter_gff:
filter_merged_bed_fd, filter_merged_bed_file = tempfile.mkstemp()
subprocess.call('sortBed -i %s | mergeBed -i - > %s' % (options.filter_gff, filter_merged_bed_file), shell=True)
# filter TE GFF
te_gff_fd, te_gff_file = tempfile.mkstemp(dir='%s/research/scratch/temp' % os.environ['HOME'])
subprocess.call('intersectBed -a %s -b %s > %s' % (options.te_gff, filter_merged_bed_file, te_gff_file), shell=True)
options.te_gff = te_gff_file
# filter BAM
bam_gff_fd, bam_gff_file = tempfile.mkstemp(dir='%s/research/scratch/temp' % os.environ['HOME'])
bedtools.abam_f1(bam_files[i], filter_merged_bed_file, bam_gff_file)
bam_file = bam_gff_file
os.close(filter_merged_bed_fd)
os.remove(filter_merged_bed_file)
############################################
# count TE fragments
############################################
fragments, te_fragments = count_te_fragments(bam_file, options.te_gff, options.strand_split)
############################################
# print table
############################################
for line in open(options.te_gff):
a = line.split('\t')
te_chrom = a[0]
te_start = int(a[3])
if options.strand_split:
te_count = te_fragments.get((te_chrom, te_start, '+'), 0)
te_pct = te_count / float(fragments)
cols = (te_chrom, te_start, te_count, te_pct)
print '%-5s %9d + %6d %9.2e' % cols
te_count = te_fragments.get((te_chrom, te_start, '-'), 0)
te_pct = te_count / float(fragments)
cols = (te_chrom, te_start, te_count, te_pct)
print '%-5s %9d - %6d %9.2e' % cols
else:
te_count = te_fragments.get((te_chrom, te_start), 0)
te_pct = te_count / float(fragments)
cols = (te_chrom, te_start, te_count, te_pct)
print '%-5s %9d %6d %9.2e' % cols
############################################
# clean
############################################
if options.filter_gff:
os.close(te_gff_fd)
os.remove(te_gff_file)
os.close(bam_gff_fd)
os.remove(bam_gff_file)
def main():
usage = 'usage: %prog [options] <bam_file,bam_file2,...>'
parser = OptionParser(usage)
parser.add_option('-c', dest='control_bam_files', help='Control BAM file to paramterize null distribution [Default: %default]')
parser.add_option('-g', dest='filter_gff', help='Filter the TEs by overlap with genes in the given gff file [Default: %default]')
parser.add_option('-m', dest='mapq', default=False, action='store_true', help='Consider only reads with mapq>0 [Default: %default]')
parser.add_option('-r', dest='repeats_gff', default='%s/hg19.fa.out.tp.gff' % os.environ['MASK'])
parser.add_option('-s', dest='strand_split', default=False, action='store_true', help='Split statistics by strand [Default: %default]')
(options,args) = parser.parse_args()
if len(args) != 1:
parser.error('Must provide a BAM file.')
else:
bam_files = args[0].split(',')
control_bam_files = []
if options.control_bam_files:
control_bam_files = options.control_bam_files.split(',')
############################################
# GFF filter
############################################
# filter TEs and read alignments by gff file
if options.filter_gff:
filter_merged_bed_fd, filter_merged_bed_file = tempfile.mkstemp()
subprocess.call('sortBed -i %s | mergeBed -i - > %s' % (options.filter_gff, filter_merged_bed_file), shell=True)
# filter TE GFF
te_gff_fd, te_gff_file = tempfile.mkstemp(dir='%s/research/scratch/temp' % os.environ['HOME'])
subprocess.call('intersectBed -a %s -b %s > %s' % (options.repeats_gff, filter_merged_bed_file, te_gff_file), shell=True)
options.repeats_gff = te_gff_file
# filter BAM
bam_gff_fds = [None]*len(bam_files)
bam_gff_files = [None]*len(bam_files)
for i in range(len(bam_files)):
bam_gff_fds[i], bam_gff_files[i] = tempfile.mkstemp(dir='%s/research/scratch/temp' % os.environ['HOME'])
bedtools.abam_f1(bam_files[i], filter_merged_bed_file, bam_gff_files[i])
bam_files[i] = bam_gff_files[i]
# filter control BAM
if control_bam_files:
cbam_gff_fds = [None]*len(control_bam_files)
cbam_gff_files = [None]*len(control_bam_files)
for i in range(len(control_bam_files)):
cbam_gff_fds[i], cbam_gff_files[i] = tempfile.mkstemp(dir='%s/research/scratch/temp' % os.environ['HOME'])
bedtools.abam_f1(control_bam_files[i], filter_merged_bed_file, cbam_gff_files[i])
control_bam_files[i] = cbam_gff_files[i]
############################################
# lengths
############################################
# estimate read length (just averaging across replicates for now)
read_lens = []
for bam_file in bam_files:
read_lens.append(estimate_read_length(bam_file))
read_len = stats.mean(read_lens)
# compute size of search space
if options.filter_gff:
genome_length = count_bed(filter_merged_bed_file, read_len)
else:
genome_length = count_hg19()
# hash counted repeat genomic bp
if options.filter_gff:
te_lengths = te_target_size_bed(options.repeats_gff, filter_merged_bed_file, read_len)
else:
te_lengths = te_target_size(options.repeats_gff, read_len)
############################################
# count TE fragments
############################################
fragments = []
te_fragments = []
for bam_file in bam_files:
rep_fragments, rep_te_fragments = count_te_fragments(bam_file, options.repeats_gff, options.strand_split)
fragments.append(rep_fragments)
te_fragments.append(rep_te_fragments)
if control_bam_files:
control_fragments = []
control_te_fragments = []
for control_bam_file in control_bam_files:
rep_fragments, rep_te_fragments = count_te_fragments(control_bam_file, options.repeats_gff, options.strand_split)
control_fragments.append(rep_fragments)
control_te_fragments.append(rep_te_fragments)
############################################
# combine replicates into fragment rates
############################################
te_fragment_rates = {}
for (rep,fam) in te_lengths:
if options.strand_split:
# positive
rate_list = [te_fragments[i].get((rep+'+',fam),1)/float(fragments[i]) for i in range(len(bam_files))]
te_fragment_rates[(rep+'+',fam)] = stats.geo_mean(rate_list)
# negative
rate_list = [te_fragments[i].get((rep+'-',fam),1)/float(fragments[i]) for i in range(len(bam_files))]
te_fragment_rates[(rep+'-',fam)] = stats.geo_mean(rate_list)
else:
rate_list = [te_fragments[i].get((rep,fam),1)/float(fragments[i]) for i in range(len(bam_files))]
te_fragment_rates[(rep,fam)] = stats.geo_mean(rate_list)
if control_bam_files:
control_te_fragment_rates = {}
for te in te_fragment_rates:
rate_list = [control_te_fragments[i].get(te,1)/float(control_fragments[i]) for i in range(len(control_bam_files))]
control_te_fragment_rates[te] = stats.geo_mean(rate_list)
############################################
# compute stats, print table
############################################
for (rep,fam) in te_fragment_rates:
# compute TE length
if options.strand_split:
te_len = te_lengths[(rep[:-1],fam)]
else:
te_len = te_lengths[(rep,fam)]
# parameterize null model
if options.control_bam_files:
null_rate = control_te_fragment_rates[(rep,fam)]
else:
if options.strand_split:
null_rate = float(te_lengths[(rep[:-1],fam)]) / (2*genome_length)
else:
null_rate = float(te_lengths[(rep,fam)]) / genome_length
# compute fragment counts
count = te_fragment_rates[(rep,fam)]*sum(fragments)
null_count = null_rate*sum(fragments)
# compute fold change
if null_rate > 0:
fold = te_fragment_rates[(rep,fam)]/null_rate
else:
fold = 0
# compute p-value of enrichment/depletion
p_val = 1.0
for i in range(len(bam_files)):
if te_fragment_rates[(rep,fam)] > null_rate:
p_val *= binom.sf(int(te_fragments[i].get((rep,fam),1))-1, int(fragments[i]), null_rate)
else:
p_val *= binom.cdf(int(te_fragments[i].get((rep,fam),1)), int(fragments[i]), null_rate)
cols = (rep, fam, te_len, count, null_count, fold, p_val)
print '%-18s %-18s %10d %10.1f %10.1f %10.3f %10.2e' % cols
############################################
# clean
############################################
if options.filter_gff:
os.close(filter_merged_bed_fd)
os.remove(filter_merged_bed_file)
os.close(te_gff_fd)
os.remove(te_gff_file)
for i in range(len(bam_files)):
os.close(bam_gff_fds[i])
os.remove(bam_gff_files[i])
if options.control_bam_files:
for i in range(len(control_bam_files)):
os.close(cbam_gff_fds[i])
os.remove(cbam_gff_files[i])