def getMergedBigWigPeakShift(infiles, outfile):
'''Merge multiple BAM files per replicate to produce a single peak-shifted bigwig file'''
expt = P.snip(os.path.basename(outfile), ".merge.bw").replace("-agg", "")
in_list = " --bamfile=".join(infiles)
offsets = []
for t in infiles:
track = P.snip(os.path.basename(t), ".norm.bam")
fn = "macs/with_input/%s.macs" % track
if os.path.exists(fn):
offsets.append(str(PIntervals.getPeakShiftFromMacs(fn)))
shifts = " --shift=".join(offsets)
statement = '''python %(scriptsdir)s/bam2wiggle.py
--output-format=bigwig
%(in_list)s
%(shifts)s > %(outfile)s'''
P.run()
def getMergedBigWigPeakShift( infiles, outfile ):
'''Merge multiple BAM files per replicate to produce a single peak-shifted bigwig file'''
expt = P.snip( os.path.basename( outfile ), ".merge.bw").replace("-agg","")
in_list = " --bamfile=".join(infiles)
offsets = []
for t in infiles:
track = P.snip(os.path.basename(t), ".norm.bam")
fn = "macs/with_input/%s.macs" % track
if os.path.exists( fn ):
offsets.append( str(PIntervals.getPeakShiftFromMacs( fn )) )
shifts = " --shift=".join(offsets)
statement = '''python %(scriptsdir)s/bam2wiggle.py
--output-format=bigwig
%(in_list)s
%(shifts)s > %(outfile)s'''
P.run()
def loadMergedIntervals(infile, outfile):
'''load combined intervals.
Also, re-evaluate the intervals by counting reads within
the interval. In contrast to the initial pipeline, the
genome is not binned. In particular, the meaning of the
columns in the table changes to:
nProbes: number of reads in interval
PeakCenter: position with maximum number of reads in interval
AvgVal: average coverage within interval
If *replicates* is true, only replicates will be considered
for the counting. Otherwise the counts aggregate both replicates
and conditions.
'''
# Write header to output file
tmpfile = tempfile.NamedTemporaryFile(delete=False)
headers = ("contig", "start", "end", "interval_id", "nPeaks", "PeakCenter",
"Length", "AvgVal", "PeakVal", "nProbes", "Fold")
tmpfile.write("\t".join(headers) + "\n")
contig, start, end, interval_id, npeaks, peakcenter, length, avgval, peakval, nprobes = "", 0, 0, 0, 0, 0, 0, 0, 0, 0
# Get SAM file and Macs offset
samfiles, offsets = [], []
track = P.snip(os.path.basename(infile), ".merged.cleaned.bed")
base_track = track.replace(".solo", "")
fn = "bam/%s.norm.bam" % track
assert os.path.exists(
fn), "could not find bamfile %s for track %s" % (fn, track)
samfiles.append(pysam.Samfile(fn, "rb"))
if track.find("solo") > -1:
fn = "macs/no_input/%s.macs" % track
else:
fn = "macs/with_input/%s.macs" % track
if os.path.exists(fn):
offsets.append(PIntervals.getPeakShiftFromMacs(fn))
# Loop over input Bed file and calculate stats for merged intervals
c = E.Counter()
for line in open(infile, "r"):
c.input += 1
contig, start, end, int_id, fc = line[:-1].split()[:5]
start, end = int(start), int(end)
interval_id = c.input
npeaks, peakcenter, length, avgval, peakval, nprobes = PIntervals.countPeaks(
contig, start, end, samfiles, offsets)
# nreads can be 0 if the intervals overlap only slightly
# and due to the binning, no reads are actually in the overlap region.
# However, most of these intervals should be small and have already be deleted via
# the merge_min_interval_length cutoff.
# do not output intervals without reads.
if nprobes == 0:
c.skipped_reads += 1
c.output += 1
tmpfile.write("\t".join(
map(str, (contig, start, end, int_id, npeaks, peakcenter, length,
avgval, peakval, nprobes, fc))) + "\n")
tmpfile.close()
tmpfilename = tmpfile.name
tablename = "%s_macs_merged_intervals" % track
statement = '''python %(scriptsdir)s/csv2db.py %(csv2db_options)s
--index=interval_id
--index=contig,start
--table=%(tablename)s
< %(tmpfilename)s > %(outfile)s '''
P.run()
os.unlink(tmpfile.name)
L.info("%s\n" % str(c))
def loadMergedIntervals( infile, outfile ):
'''load combined intervals.
Also, re-evaluate the intervals by counting reads within
the interval. In contrast to the initial pipeline, the
genome is not binned. In particular, the meaning of the
columns in the table changes to:
nProbes: number of reads in interval
PeakCenter: position with maximum number of reads in interval
AvgVal: average coverage within interval
If *replicates* is true, only replicates will be considered
for the counting. Otherwise the counts aggregate both replicates
and conditions.
'''
# Write header to output file
tmpfile = tempfile.NamedTemporaryFile(delete=False)
headers = ( "contig","start","end","interval_id","nPeaks","PeakCenter","Length","AvgVal","PeakVal","nProbes", "Fold" )
tmpfile.write( "\t".join(headers) + "\n" )
contig,start,end,interval_id,npeaks,peakcenter,length,avgval,peakval,nprobes = "",0,0,0,0,0,0,0,0,0
# Get SAM file and Macs offset
samfiles, offsets = [], []
track = P.snip( os.path.basename(infile), ".merged.cleaned.bed")
base_track = track.replace(".solo","")
fn = "bam/%s.norm.bam" % track
assert os.path.exists( fn ), "could not find bamfile %s for track %s" % ( fn, track)
samfiles.append( pysam.Samfile( fn, "rb" ) )
if track.find("solo") > -1:
fn = "macs/no_input/%s.macs" % track
else:
fn = "macs/with_input/%s.macs" % track
if os.path.exists( fn ):
offsets.append( PIntervals.getPeakShiftFromMacs( fn ) )
# Loop over input Bed file and calculate stats for merged intervals
c = E.Counter()
for line in open(infile, "r"):
c.input += 1
contig, start, end, int_id, fc = line[:-1].split()[:5]
start, end = int(start), int(end)
interval_id = c.input
npeaks, peakcenter, length, avgval, peakval, nprobes = PIntervals.countPeaks( contig, start, end, samfiles, offsets )
# nreads can be 0 if the intervals overlap only slightly
# and due to the binning, no reads are actually in the overlap region.
# However, most of these intervals should be small and have already be deleted via
# the merge_min_interval_length cutoff.
# do not output intervals without reads.
if nprobes == 0:
c.skipped_reads += 1
c.output += 1
tmpfile.write( "\t".join( map( str, (contig,start,end,int_id,npeaks,peakcenter,length,avgval,peakval,nprobes,fc) )) + "\n" )
tmpfile.close()
tmpfilename = tmpfile.name
tablename = "%s_macs_merged_intervals" % track
statement = '''python %(scriptsdir)s/csv2db.py %(csv2db_options)s
--index=interval_id
--index=contig,start
--table=%(tablename)s
< %(tmpfilename)s > %(outfile)s '''
P.run()
os.unlink( tmpfile.name )
L.info( "%s\n" % str(c) )
