class TestBigWig(unittest.TestCase):
def setUp(self):
f = open("test_data/bbi_tests/test.bw")
self.bw = BigWigFile(file=f)
def test_get_summary(self):
data = self.bw.query("chr1", 10000, 20000, 10)
means = [x['mean'] for x in data]
print means
assert numpy.allclose(map(float, means), [
-0.17557571594973645, -0.054009292602539061, -0.056892242431640622,
-0.03650328826904297, 0.036112907409667966, 0.0064466032981872557,
0.036949024200439454, 0.076638259887695306, 0.043518108367919923,
0.01554749584197998
])
# Summarize variant
sd = self.bw.summarize("chr1", 10000, 20000, 10)
assert numpy.allclose(sd.sum_data / sd.valid_count, [
-0.17557571594973645, -0.054009292602539061, -0.056892242431640622,
-0.03650328826904297, 0.036112907409667966, 0.0064466032981872557,
0.036949024200439454, 0.076638259887695306, 0.043518108367919923,
0.01554749584197998
])
# Test min and max for this entire summary region
data = self.bw.query("chr1", 10000, 20000, 1)
maxs = [x['max'] for x in data]
mins = [x['min'] for x in data]
self.assertEqual(map(float, maxs), [0.289000004529953])
self.assertEqual(map(float, mins), [-3.9100000858306885])
def test_get_leaf(self):
data = self.bw.query("chr1", 11000, 11005, 5)
means = [x['mean'] for x in data]
assert numpy.allclose(map(float, means), [
0.050842501223087311, -2.4589500427246094, 0.050842501223087311,
0.050842501223087311, 0.050842501223087311
])
# Test min and max for this entire leaf region
data = self.bw.query("chr1", 11000, 11005, 1)
maxs = [x['max'] for x in data]
mins = [x['min'] for x in data]
self.assertEqual(map(float, maxs), [0.050842501223087311])
self.assertEqual(map(float, mins), [-2.4589500427246094])
def test_wrong_nochrom(self):
data = self.bw.query("chr2", 0, 10000, 10)
self.assertEqual(data, None)
def main():
p = optparse.OptionParser(__doc__)
p.add_option('-A', '--absolute', action='store_true',dest='A',\
default=False, help='absolute threshold')
p.add_option('-s','--standard_background', action='store_true',\
dest='stdbg')
p.add_option('-D', '--debug', action='store_true', dest='debug')
options, args = p.parse_args()
debug_c = 0
BEDFILE = open(args[0], 'rU')
BW = BigWigFile(file=open(args[1]))
BEDout = open(args[2], 'w')
for line in BEDFILE:
print(line)
line = line.strip().split('\t')
x = BW.query(line[0], int(line[1]), int(line[2]),1)
line.append(str(round(x[0]['mean'], 5)))
BEDout.write("\t".join(line)+"\n")
"""
for i in x:
print i['mean']
"""
if options.debug:
debug_c +=1
if debug_c >= 10:
break
if __name__ == '__main__':
main()
def createMappabilityList(fragmentsMap, bwfile, fragmentCount, options):
# keep record which fragment has decent mappability
mappable = np.zeros((fragmentCount, ), dtype=np.float)
# lazy load
from bx.intervals.io import GenomicIntervalReader
from bx.bbi.bigwig_file import BigWigFile
bw = BigWigFile(open(bwfile))
for fragmentId in fragmentsMap.keys():
(chrom, start, end) = fragmentsMap[fragmentId]
if (options.vverbose):
print >> sys.stdout, "- process %s %d-%d " % (chrom, start, end)
try:
mappable[fragmentId] = bw.query(chrom, start, end, 1)[0]["mean"]
if (np.isnan(mappable[fragmentId])):
mappable[fragmentId] = 0
except:
mappable[fragmentId] = 0.
# problem with invalid values
if (options.vverbose):
print >> sys.stderr, "Problem with bw file at %s %d-%d" % (
chrom, start, end)
print traceback.format_exc()
return mappable
def createMappabilityList(fragmentsMap, bwfile, fragmentCount, options):
# keep record which fragment has decent mappability
mappable = np.zeros((fragmentCount,), dtype=np.float)
# lazy load
from bx.intervals.io import GenomicIntervalReader
from bx.bbi.bigwig_file import BigWigFile
bw = BigWigFile( open( bwfile ) )
for fragmentId in fragmentsMap.keys():
(chrom, start, end) = fragmentsMap[fragmentId]
if (options.vverbose):
print >> sys.stdout, "- process %s %d-%d " % (chrom, start, end)
try:
mappable[fragmentId] = bw.query(chrom, start, end, 1)[0]["mean"]
if (np.isnan(mappable[fragmentId])):
mappable[fragmentId] = 0
except:
mappable[fragmentId] = 0.
# problem with invalid values
if (options.vverbose):
print >> sys.stderr, "Problem with bw file at %s %d-%d" % (chrom, start, end)
print traceback.format_exc()
return mappable
def big_wig_summary_worker((span, bw_list, region_bed_file_name, nb_proc,
verbose)):
results = list()
bw_label = [os.path.basename(p) for p in bw_list]
bw_label = [os.path.splitext(os.path.basename(p))[0] for p in bw_list]
if verbose:
sys.stderr.write("Processing: " + region_bed_file_name)
for big_wig, cpt in zip(bw_list, range(len(bw_list))):
bigwig = BigWigFile(open(big_wig, "r"))
if verbose:
sys.stderr.write("Computing coverage for file: " + big_wig + " [" +
str(multiprocessing.current_process()) + "], " +
str(span[1] - span[0]) + " chunks to process.\n")
bed_windows = pybedtools.BedTool(region_bed_file_name)
chr_cur = None
# Loop through bed lines (features object)
for i in bed_windows[slice(span[0], span[1])]:
if chr_cur == None:
chr_cur = i.chrom
else:
if i.chrom != chr_cur:
chr_cur = i.chrom
# Note: bigWig is zero-based/half open as bed.
bw_sum = bigwig.query(i.chrom, i.start, i.end, 1)
if bw_sum is not None:
bw_sum = bw_sum[0]['mean']
bw_sum = np.nan_to_num(bw_sum)
bw_sum = np.round(bw_sum, 2)
else:
bw_sum = 0.00
results.append(
(i.chrom + ":" + str(i.start), bw_label[cpt], float(bw_sum)))
if verbose:
sys.stderr.write("Computing coverage for file: " + big_wig + " [" +
str(multiprocessing.current_process()) +
"]. Job done.\n")
return results
def get_mappability(mappability_file,
vcf_file,
out_file,
region=None,
append_chr=True):
map_reader = BigWigFile(open(mappability_file, 'rb'))
vcf_reader = vcf.Reader(filename=vcf_file)
if region is not None:
chrom, beg, end = parse_region_for_vcf(region)
try:
vcf_reader = vcf_reader.fetch(chrom, start=beg, end=end)
except ValueError:
print("no data for region {} in vcf".format(region))
vcf_reader = []
data = []
for record in vcf_reader:
if append_chr:
chrom = 'chr{0}'.format(record.CHROM)
else:
chrom = record.CHROM
coord = record.POS
beg = coord - 100
beg = max(beg, 0)
end = coord + 100
result = map_reader.query(chrom, beg, end, 1)
if result is None:
mappability = 0
else:
mappability = result[0]['mean']
data.append({
'chrom': record.CHROM,
'coord': record.POS,
'mappability': mappability
})
data = pd.DataFrame(data)
csvutils.write_dataframe_to_csv_and_yaml(data, out_file, dtypes())
class TestBigWig(unittest.TestCase):
def setUp(self):
f = open( "test_data/bbi_tests/test.bw" )
self.bw = BigWigFile(file=f)
def test_get_summary(self):
data = self.bw.query("chr1", 10000, 20000, 10)
means = [ x['mean'] for x in data ]
print means
assert numpy.allclose( map(float, means), [-0.17557571594973645, -0.054009292602539061, -0.056892242431640622, -0.03650328826904297, 0.036112907409667966, 0.0064466032981872557, 0.036949024200439454, 0.076638259887695306, 0.043518108367919923, 0.01554749584197998] )
# Summarize variant
sd = self.bw.summarize( "chr1", 10000, 20000, 10)
assert numpy.allclose( sd.sum_data / sd.valid_count, [-0.17557571594973645, -0.054009292602539061, -0.056892242431640622, -0.03650328826904297, 0.036112907409667966, 0.0064466032981872557, 0.036949024200439454, 0.076638259887695306, 0.043518108367919923, 0.01554749584197998] )
# Test min and max for this entire summary region
data = self.bw.query("chr1", 10000, 20000, 1)
maxs = [ x['max'] for x in data ]
mins = [ x['min'] for x in data ]
self.assertEqual( map(float, maxs), [0.289000004529953] )
self.assertEqual( map(float, mins), [-3.9100000858306885] )
def test_get_leaf(self):
data = self.bw.query("chr1", 11000, 11005, 5)
means = [ x['mean'] for x in data ]
assert numpy.allclose( map(float, means), [0.050842501223087311, -2.4589500427246094, 0.050842501223087311, 0.050842501223087311, 0.050842501223087311] )
# Test min and max for this entire leaf region
data = self.bw.query("chr1", 11000, 11005, 1)
maxs = [ x['max'] for x in data ]
mins = [ x['min'] for x in data ]
self.assertEqual( map(float, maxs), [0.050842501223087311] )
self.assertEqual( map(float, mins), [-2.4589500427246094] )
def test_wrong_nochrom(self):
data = self.bw.query("chr2", 0, 10000, 10)
self.assertEqual( data, None )
def output(fragmentsMap , fragmentList, fragmentPairs, fragmentCount, fragmentsChrom):
'''
outputs 2 files, the first containing
"chr extraField fragmentMid marginalizedContactCount mappable? (0/1)"
and the second containing:
"chr1 fragmentMid1 chr2 fragmentMid2 contactCount"
optionally output the 2D contact matrix
'''
if (options.verbose):
print >> sys.stdout, "- %s START : output data " % (timeStamp())
if ( options.outputFilename != "" ):
outfile1 = gzip.open(options.outputDir+options.outputFilename+".fragmentLists.gz","wb")
else:
outfile1 = gzip.open(options.outputDir+os.path.basename(args[0])+".fragmentLists.gz","wb")
fragmentIds = fragmentsMap.keys()
fragmentIds.sort()
# lookup mean mappability ratio
bw = ""
if (options.mappability != ""):
# lazy load
from bx.intervals.io import GenomicIntervalReader
from bx.bbi.bigwig_file import BigWigFile
bw = BigWigFile( open( options.mappability ) )
for fragmentId in fragmentIds:
contactCounts = 0
chrom = fragmentsMap[fragmentId][0]
midpoint = fragmentsMap[fragmentId][1]
if (options.vverbose):
print >> sys.stdout, "- process %s %d " % (chrom, midpoint)
if (fragmentList.has_key(fragmentId)):
contactCounts = fragmentList[fragmentId]
if (bw != ""):
try:
mappable = bw.query(chrom, midpoint-options.resolution/2, midpoint+options.resolution/2, 1)[0]["mean"]
except:
mappable = 0
# problem with invalid values
if (options.vverbose):
print >> sys.stderr, "Problem with bw file at %s %d-%d" % (chrom, midpoint-options.resolution/2, midpoint+options.resolution/2)
print traceback.format_exc()
elif (contactCounts>0):
mappable=1
outfile1.write("%s\t%d\t%s\t%f\n" % (chrom, midpoint, "NA", mappable))
outfile1.close()
if ( options.outputFilename != "" ):
outfile2 = gzip.open(options.outputDir+options.outputFilename+".contactCounts.gz","wb")
else:
outfile2 = gzip.open(options.outputDir+os.path.basename(args[0])+".contactCounts.gz","wb")
for fragmentIds, contactCounts in fragmentPairs.iteritems():
chrom1 = fragmentsMap[fragmentIds[0]][0]
midpoint1 = fragmentsMap[fragmentIds[0]][1]
chrom2 = fragmentsMap[fragmentIds[1]][0]
midpoint2 = fragmentsMap[fragmentIds[1]][1]
outfile2.write("%s\t%d\t%s\t%d\t%d\n" % (chrom1, midpoint1, chrom2, midpoint2, contactCounts))
outfile2.close()
if (options.create2DMatrix or options.create2DMatrixPerChr):
# lazy loading
from scipy.sparse import lil_matrix
import numpy
# populate sparse matrix
A = lil_matrix((fragmentCount, fragmentCount), dtype='i')
for fragmentIds, contactCounts in fragmentPairs.iteritems():
A[fragmentIds[0],fragmentIds[1]] = contactCounts
A[fragmentIds[1],fragmentIds[0]] = contactCounts
# convert to coordinate format
B = A.tocoo()
if (options.create2DMatrix):
if ( options.outputFilename != "" ):
outfile3 = options.outputDir+options.outputFilename+".matrix"
else:
outfile3 = options.outputDir+os.path.basename(args[0])+".matrix"
if (options.verbose):
print >> sys.stdout, "- save 2Dmatrix to %s " % (outfile3)
f_handle=open(outfile3,'w')
C = B.tocsr()
for i in xrange(fragmentCount):
numpy.savetxt(f_handle, C[i].toarray(),fmt='%i', delimiter='\t')
f_handle.close()
if (options.create2DMatrixPerChr):
for chr in fragmentsChrom.keys():
C = B.tocsc()[:,fragmentsChrom[chr][0]:fragmentsChrom[chr][1]].tocsr()[fragmentsChrom[chr][0]:fragmentsChrom[chr][1],:]
fragmentRange=fragmentsChrom[chr][1]-fragmentsChrom[chr][0]
header=['d']+[ "%s%d" % i for i in zip(['r']*fragmentRange,range(fragmentRange))]
if ( options.outputFilename != "" ):
outfile3 = options.outputDir+options.outputFilename+"."+chr+".matrix"
else:
outfile3 = options.outputDir+os.path.basename(args[0])+"."+chr+".matrix"
if (options.verbose):
print >> sys.stdout, "- save 2Dmatrix for chromosome %s to %s " % (chr, outfile3)
f_handle=open(outfile3,'w')
f_handle.write('\t'.join(header)+"\n")
for i in xrange(fragmentRange):
f_handle.write(header[i+1]+"\t")
numpy.savetxt(f_handle, C[i].toarray(),fmt='%i', delimiter='\t')
f_handle.close()
if (options.verbose):
print >> sys.stdout, "- %s FINISHED: output data" % (timeStamp())
def processChromatin(offBed, f_configuration):
""" process chromatin data into blocks of specific resolution """
if (options.verbose):
print "Start chromatin processing"
if (not f_configuration["conf"].has_key("genomesize")):
calculateChromosomeSpecifics(chromsizes, f_configuration)
step_size = int(f_configuration["conf"]["genomesize"] * options.chromatinResolution / (360. - f_configuration["conf"]["target_radius"] ))
if (options.verbose):
print "chromatin stepsize: %d nts" % (step_size)
samfile = ""
bigwig = ""
# check if chromatin file has been supplied
if (options.chromatinFormat == "bam"):
import pysam # lazy import of required module
# try open the bam file
try:
samfile = pysam.Samfile(options.chromatin, "rb" )
except:
print >> sys.stderr, "[ERROR] chromatin file (bam) could not be read : %s" % (options.chromatin)
traceback.print_exc()
exit(1)
elif (options.chromatinFormat == "bigwig"):
from bx.intervals.io import GenomicIntervalReader # lazy import of required module
from bx.bbi.bigwig_file import BigWigFile # lazy import of required module
# try open the bigwig file
try:
bigwig = BigWigFile(open(options.chromatin))
except:
print >> sys.stderr, "[ERROR] chromatin file (bigwig) could not be read : %s" % (options.chromatin)
traceback.print_exc()
exit(1)
if (options.verbose):
print "... chromatin file opened"
max_value = 0.
# open output file
if (os.path.exists(options.output_dir+"chromatin.txt")):
if (options.verbose):
print "using existing chromatin file"
for line in fileinput.input([options.output_dir+"chromatin.txt"]):
c_value = float(line.strip().split("\t")[3])
if (c_value > max_value):
max_value = c_value
else:
f_chromatin = open(options.output_dir+"chromatin.txt","w")
# process calculate mean chromatin density value for region
for region in offBed.values():
chrom = region[0]
if (options.verbose):
print "Collecting chromatin data for chromosom %s" % (chrom)
for span_start in range(region[1], region[2], step_size):
span_end = min(span_start + step_size, region[2])
c_value = 0.
if (span_start == span_end):
continue
if (options.chromatinFormat=="bam"):
try:
on_chromatin_features = 0.
for pileupcolumn in samfile.pileup(chrom,span_start,span_end):
on_chromatin_features += pileupcolumn.n
c_value = on_chromatin_features/(span_end-span_start)
except:
if (options.verbose):
print >> sys.stderr, "[WARN] bam file exception : %s:%d-%d" % (chrom,span_start,span_end)
elif (options.chromatinFormat=="bigwig"):
try:
bwsummary = bigwig.query(chrom,span_start,span_end, 1 )
c_value = bwsummary[0]["mean"]
except:
if (options.verbose):
print >> sys.stderr, "[WARN] bigwig file exception : %s:%d-%d" % (chrom,span_start,span_end)
if (math.isnan(c_value)):
c_value = 0.
# add pseudocount to circumvent log issues
c_value += 0.001
if (c_value > max_value):
max_value = c_value
f_chromatin.write("%s\t%d\t%d\t%.3f\n" % (chrom, span_start,span_end, c_value))
# close filehandle
f_chromatin.close()
if (options.verbose):
print "Maximal chromatin value: %.3f" % (max_value)
# add track to configuration file
if (not f_configuration.has_key("plots")):
f_configuration["plots"] = {}
f_configuration["plots"]["type"] = "heatmap"
f_configuration["plots"]["color"] = "spectral-9-div"
f_configuration["plots"]["stroke_thickness"] = "1"
f_configuration["plots"]["stroke_color"] = "black"
if (not f_configuration["plots"].has_key("plot")):
f_configuration["plots"]["plot"] = []
stroke_thickness = 0
if (options.chromatinResolution >= 0.5):
stroke_thickness = 1
f_configuration["plots"]["plot"] += ['''
<plot>
show = conf(show_histogram)
type = histogram
fill_under = yes
fill_color = lgrey
file = %s
r0 = 0.95r
r1 = 0.997r
scale_log_base = 5
stroke_thickness = %d
color = black
min = 0.0
max = %.3f
<axes>
show = data
thickness = 1
color = lgrey
<axis>
spacing = 0.1r
</axis>
</axes>
</plot>
''' % (options.output_dir+"chromatin.txt", stroke_thickness, max_value)]
if (options.verbose):
print "Finished chromatin processing"
pol2 = BigWigFile(open('/Users/JME/Documents/CSE549/hg19.bigWig'))
annotations = open('/Users/JME/Documents/CSE549/hg19.txt', 'r')
for line in annotations:
lines = line.split('\t')
if(lines[1] in chroms.keys()):
chroms[lines[1]][int(lines[3])] = [int(lines[4]), lines[2]]
for i in chroms.keys():
for j in chroms[i]:
if not j in trScores.keys():
start = 0
end = 0
if chroms[i][j][1] == '-':
start = chroms[i][j][0]
end = j
else:
start = j
end = chroms[i][j][0]
startTr = pol2.query(i, start - 30, start + 300, 1)
endTr = pol2.query(i, start + 301, end, 1)
if startTr and endTr:
if startTr[0]['mean'] > 0 and endTr[0]['mean'] > 0:
trScores[j] = 0
trScores[j] = (startTr[0]['mean'])/ (endTr[0]['mean'])
if trScores[j] > maxScore:
maxScore = trScores[j]
maxGene = j
pickle.dump(chroms, open('chrom_data.p', 'wb'))
pickle.dump(trScores, open('scores.p', 'wb'))
print("Max: " + maxScore + ", gene: " + maxGene)
def process():
# get the data
readdata()
# set chromatin flag
withChromatin = 'false'
if (options.chromatin!=""):
withChromatin = 'true'
samfile = ""
bigwig = ""
# check if chromatin file has been supplied
if (options.chromatin != "" and options.chromatinFormat == "bam"):
import pysam # lazy import of required module
# try open the bam file
try:
samfile = pysam.Samfile(options.chromatin, "rb" )
except:
exit(1)
elif (options.chromatin != "" and options.chromatinFormat == "bigwig"):
from bx.intervals.io import GenomicIntervalReader # lazy import of required module
from bx.bbi.bigwig_file import BigWigFile # lazy import of required module
# try open the bigwig file
try:
bigwig = BigWigFile(open(options.chromatin))
except:
exit(1)
processors = 1
if (options.processors <= 0): # figure out number of processors
try:
processors = multiprocessing.cpu_count()
except:
processors = 1
else:
processors = options.processors
output_ontargets = "" # gets filled with a json object for on-targets
# process one on-target region/locus of interest (LOI) at a time
for loi in targets.keys():
# info for whole region
bed = loiBed[loi]
chrom = bed[0]
cstart = int(bed[1])
cstop = int(bed[2])
clabel = bed[3]
cstrand = bed[5]
tts = records[loi].seq
# process individual eligible target sites in this region/locus
for target in targets[loi]:
tstart = int(target[1])
tstop = int(target[2])
targetid = target[3]
tstrand = target[5]
tsize = tstop-tstart
lflank = min(tstart-cstart, options.flanks)
rflank = min(cstop-tstop, options.flanks)
targetSeq = ""
if (cstrand == "+"):
targetSeq = tts[tstart-cstart-lflank: tstop-cstart+rflank]
else:
targetSeq = tts.reverse_complement()[tstart-cstart-lflank: tstop-cstart+rflank]
targetStr = [" " for i in range(lflank+tsize+rflank)]
for i in range(lflank, lflank+tsize):
targetStr[i]='+'
if (target[8]!=""):
for s in target[8].strip('d').split('d'):
if (cstrand == "+"):
targetStr[lflank+int(s)]='_'
else:
targetStr[lflank+tsize-int(s)-1]='_'
on_chromatin_features = 0.
if (options.chromatin != "" and options.chromatinFormat=="bam"):
try:
for pileupcolumn in samfile.pileup(chrom,tstart-lflank,tstop+rflank):
on_chromatin_features += pileupcolumn.n
on_chromatin_features = "%.3f" % (on_chromatin_features/(lflank+tsize+rflank))
except:
print >> sys.stderr, "[WARN] bam file exception : %s:%d-%d" % (chrom,tstart-lflank,tstop+rflank)
elif (options.chromatin != "" and options.chromatinFormat=="bigwig"):
try:
bwsummary = bigwig.query(chrom, tstart-lflank, tstop+rflank, 1 )
on_chromatin_features = "%.3f" % (bwsummary[0]["mean"])
except:
print >> sys.stderr, "[WARN] bigwig file exception : %s:%d-%d" % (chrom,tstart-lflank,tstop+rflank)
else:
on_chromatin_features = "-"
off_targets = {} # hashing start positions (w/r/t on target) and encoding (grouping off-targets)
# branch point
if (processors == 1): # no need to multiprocess
for tfo_region in tfotargets[targetid].keys():
# process each error category
for tfo_error in tfotargets[targetid][tfo_region].keys():
processOfftarget(off_targets, targetid, tfo_region, tfo_error, lflank, rflank, tsize, tstrand, samfile, bigwig);
else: # contribute workload
# load up work queue
work_queue = multiprocessing.JoinableQueue(processors*3)
# create a queue to pass to workers to store the results
result_queue = multiprocessing.Queue()
num_jobs = 0
for tfo_region in tfotargets[targetid].keys():
num_jobs += len(tfotargets[targetid][tfo_region])
# spawn workers
for i in range(min(num_jobs,processors)):
worker = Worker(work_queue, result_queue, options.chromatin, options.chromatinFormat)
worker.daemon=True
worker.start()
# process off-targets w/r/t to on-target
# process each intersecting interval
for tfo_region in tfotargets[targetid].keys():
# process each error category
for tfo_error in tfotargets[targetid][tfo_region].keys():
work_queue.put(Job(targetid, tfo_region, tfo_error, lflank, rflank, tsize, tstrand));
work_queue.close()
work_queue.join()
for job in range(num_jobs):
result = result_queue.get()
for offset in result.keys():
if (not off_targets.has_key(offset)):
off_targets[offset] = {}
for encoding in result[offset].keys():
if (not off_targets[offset].has_key(encoding)):
off_targets[offset][encoding] = result[offset][encoding]
# merge point
output_sequence_w = ""
output_sequence_c = ""
for i in range(len(targetStr)):
if (targetStr[i] ==" "):
output_sequence_w += targetSeq.lower()[i]
output_sequence_c += targetSeq.complement().lower()[i]
else:
output_sequence_w += targetSeq.upper()[i]
output_sequence_c += targetSeq.complement().upper()[i]
# generate oaColumns (data table header)
output_offtargets = ""
for i in range(len(annotations)):
output_offtargets += (',\n { "sTitle":"%s", "sToolTip":"number of off-targets intersecting with annotation data: %s" }' % (annotations[i],annotations[i]))
output_offtargets += (',\n { "sTitle":"","bSortable":false,"bSearchable":false,"sClass":"btn_details","sToolTip":"Toggle detailed off-target inspector" }\n ],\n "aaData":[')
# generate aaData (data table content)
for offset in off_targets.keys():
for encoding in off_targets[offset]:
ots = off_targets[offset][encoding]
# format chromatin data if available
if (options.chromatin != ""):
ots["chromatin"] = "%.3f" % (ots["chromatin_max"])
else:
ots["chromatin"] = "-"
output_offtargets += '\n ["%s",0,%d,%d,"%d-%d",%d,"%s",%d,[' % (encoding, ots["errors"], offset, offset-lflank, offset+len(encoding)-lflank,ots["copies"], ots["chromatin"], ots["length"],)
for ot in ots["data"]:
output_offtargets += '\n ["%s",%d,%d,"%s",%s],' % (ot)
output_offtargets = output_offtargets[:-1]+"\n ]"
if (len(annotations)>0):
output_offtargets += ",%s" % (",".join(["%s" % el for el in ots["annotation"]]))
output_offtargets += ',""],'
# finalise off-target json object and write output
output_offtargets = ('''{
"bPaginate": true,
"bProcessing": true,
"bAutoWidth": false,
"bInfo": true,
"bLengthChange": true,
"bFilter": true,
"iDisplayLength": 10,
"aLengthMenu": [[10, 50, 100, -1], [10, 50, 100, "All"]],
"bJQueryUI": true,
"bDeferRender": true,
"aaSorting": [[2, "asc"]],
"aoColumns": [
{ "sTitle":"%s","sClass":"monospace left", "sToolTip":"shows the part of the primary target that is responsible for off-targets" },
{ "sTitle":"overlap", "sType": "numeric","sToolTip":"number of nucleotide positions off-targets overlap the primary target" },
{ "sTitle":"errors", "sType": "numeric","sToolTip":"expected number of mismatches between the triplex-forming molecule designed against the primary target and off-targets" },
{ "sTitle":"offset","sType": "numeric","bSearchable":false,"bVisible":false, "sToolTip":"the offset is used internally for computing the lefthand-side alignment" },
{ "sTitle":"sub region", "sToolTip":"region of the primary target this off-target spans" },
{ "sTitle":"copies", "sType": "numeric","sToolTip":"number of copies of this off-target category" },
{ "sTitle":"max. chromatin", "bVisible": %s, "sToolTip":"maximal chromatin score observed in any of the off-targets" },
{ "sTitle":"length", "sType": "numeric","sToolTip":"length of the off-target", "bVisible":false },
{ "sTitle":"offtargets","bVisible": false, "sToolTip":"detailed off-target list with location and annotation" } '''+output_offtargets[:-1]+"\n ]\n}") % (output_sequence_w+"<br/>"+output_sequence_c, withChromatin)
output = open(options.output_dir+targetid+"_off_targets.json","w")
output.write(output_offtargets)
output.close()
# pretty sequence encoding for on-target region
output_sequence_w = ""
output_sequence_c = ""
lastpos = " "
interruptions = 0
for i in range(len(targetStr)):
if (targetStr[i] != lastpos):
if (lastpos !=" "):
output_sequence_w+="</span>"
output_sequence_c+="</span>"
if (targetStr[i] =="+"):
output_sequence_w += "<span class='valid_triad'>"
output_sequence_c += "<span class='valid_triad'>"
elif (targetStr[i] =="_"):
output_sequence_w += "<span class='invalid_triad'>"
output_sequence_c += "<span class='invalid_triad'>"
lastpos = targetStr[i]
if (targetStr[i] =="_"):
interruptions += 1
if (targetStr[i] ==" "):
output_sequence_w += targetSeq.lower()[i]
output_sequence_c += targetSeq.complement().lower()[i]
else:
output_sequence_w += targetSeq.upper()[i]
output_sequence_c += targetSeq.complement().upper()[i]
if (lastpos == "+" or lastpos == "_"):
output_sequence_w+="</span>"
output_sequence_c+="</span>"
output_sequence = "<span class='monospace flank_triad'>5'-"+output_sequence_w+"-3'<br/>3'-"+output_sequence_c+"-5'</span>"
# count the number of off-targets
offtarget_counter = 0
for tfo_region in tfotargets[targetid].keys():
for tfo_errors in tfotargets[targetid][tfo_region].keys():
offtarget_counter+=len(tfotargets[targetid][tfo_region][tfo_errors])
output_ontargets += '\n\t\t["%s","%s",%d,%d,%d,"%s",%d,"%s",%d,"%s",%d,%d],' % (targetid, chrom, tstart, tstop, (tstop-tstart), on_chromatin_features, interruptions, output_sequence, offtarget_counter, tstrand, lflank, rflank)
# finalizes on-target json object and write output
output_ontargets = '''{
"bPaginate": true,
"bProcessing": true,
"bAutoWidth": false,
"bInfo": true,
"bLengthChange": true,
"bFilter": false,
"iDisplayLength": 10,
"bJQueryUI": true,
"aLengthMenu": [[5, 10, 25, -1], [5, 10, 25, "All"]],
"aoColumns": [
{ "sTitle": "region Id", "sToolTip":"internal id used to identify regions of overlapping putative primary targets" },
{ "sTitle": "chr", "sToolTip":"chromosome this region is located in" },
{ "sTitle": "start", "sType": "numeric", "sToolTip":"chromosomal start position of this region" },
{ "sTitle": "end", "sType": "numeric", "sToolTip":"chromosomal end position of this region" },
{ "sTitle": "length", "sType": "numeric", "sToolTip":"number of nucleotides spanned by this target region" },
{ "sTitle": "chromatin", "bVisible": '''+withChromatin+''', "sToolTip":"chromatin score averaged over the shown region" },
{ "sTitle": "Y-interruptions", "sType": "numeric", "sToolTip":"number of pyrimdine interruptions in the polypurine/polypyrimidine tract of the region" },
{ "sTitle": "on-target region", "bSortable": false, "sClass": "left", "sToolTip":"sequence of the region (plus additional flanking positions if specified)" },
{ "sTitle": "off-targets", "bSearchable": false, "bVisible": false, "sToolTip":"total number of off-targets accumulated over all putative primary targets in this region" },
{ "sTitle": "strand", "bSearchable": false, "bVisible": false, "sToolTip":"strand on which the purine tract is located" },
{ "sTitle": "offset left", "sType": "numeric", "bSearchable": false, "bVisible": false, "sToolTip":"number of upstream flanking positions shown in the sequence" },
{ "sTitle": "offset right", "sType": "numeric", "bSearchable": false, "bVisible": false, "sToolTip":"number of downstream flanking positions shown in the sequence" }
],
"aaData": [''' + output_ontargets[:-1] + '\n ]\n}'
output = open(options.output_dir+"primary_target_regions.json","w")
output.write(output_ontargets)
output.close()
# close file handle
if (options.chromatin != "" and options.chromatinFormat == "bam"):
try:
samfile.close()
except:
exit(1)
class BigWig(object):
def __init__(self, filename):
self.filename = filename
self.determine_sizes()
self.bwf = BigWigFile(open(filename))
def determine_sizes(self):
self.sizes = {}
fh = open(self.filename, "rb")
# read magic number to guess endianness
magic = fh.read(4)
if magic == '&\xfc\x8f\x88':
endianness = '<'
elif magic == '\x88\x8f\xfc&':
endianness = '>'
else:
raise IOError("The file is not in bigwig format")
# read the header
info = struct.unpack(endianness + 'HHQQQHHQQIQ', fh.read(60))
self.version = info[0]
self.zoom_levels = info[1]
self.chromosome_tree_offset = info[2]
self.full_data_offset = info[3]
self.full_index_offset = info[4]
self.field_count = info[5]
self.defined_field_count = info[6]
self.auto_SQL_offset = info[7]
self.total_summary_offset = info[8]
self.uncompress_buf_size = info[9]
# go to the data
fh.seek(self.chromosome_tree_offset)
# read magic again
magic = fh.read(4)
if magic == '\x91\x8c\xcax':
endianness = '<'
elif magic == 'x\xca\x8c\x91':
endianness = '>'
else:
raise ValueError("Wrong magic for this bigwig data file")
info2 = struct.unpack(endianness + 'IIIQQ', fh.read(28))
self.block_size = info2[0]
self.key_size = info2[1]
self.val_size = info2[2]
self.item_count = info2[3]
info3 = struct.unpack(endianness + 'BBH', fh.read(4))
self.is_leaf = info3[0]
self.count = info3[2]
for n in range(self.count):
format_code = endianness + str(self.key_size) + 'sII'
info = struct.unpack(format_code, fh.read(self.key_size + 2 * 4))
key, chrom_id, chrom_size = info
key = key.replace('\x00', '')
self.sizes[key] = chrom_size
def get_as_array(self, chrom, start, end):
return self.bwf.get_as_array(chrom, start, end)
def get(self, chrom, start, end):
return self.bwf.get(chrom, start, end)
def query(self, chrom, start, end, number):
return self.bwf.query(chrom, start, end, number)
def process():
# get the data
readdata()
# set chromatin flag
withChromatin = 'false'
if (options.chromatin != ""):
withChromatin = 'true'
samfile = ""
bigwig = ""
# check if chromatin file has been supplied
if (options.chromatin != "" and options.chromatinFormat == "bam"):
import pysam # lazy import of required module
# try open the bam file
try:
samfile = pysam.Samfile(options.chromatin, "rb")
except:
exit(1)
elif (options.chromatin != "" and options.chromatinFormat == "bigwig"):
from bx.intervals.io import GenomicIntervalReader # lazy import of required module
from bx.bbi.bigwig_file import BigWigFile # lazy import of required module
# try open the bigwig file
try:
bigwig = BigWigFile(open(options.chromatin))
except:
exit(1)
processors = 1
if (options.processors <= 0): # figure out number of processors
try:
processors = multiprocessing.cpu_count()
except:
processors = 1
else:
processors = options.processors
output_ontargets = "" # gets filled with a json object for on-targets
# process one on-target region/locus of interest (LOI) at a time
for loi in targets.keys():
# info for whole region
bed = loiBed[loi]
chrom = bed[0]
cstart = int(bed[1])
cstop = int(bed[2])
clabel = bed[3]
cstrand = bed[5]
tts = records[loi].seq
# process individual eligible target sites in this region/locus
for target in targets[loi]:
tstart = int(target[1])
tstop = int(target[2])
targetid = target[3]
tstrand = target[5]
tsize = tstop - tstart
lflank = min(tstart - cstart, options.flanks)
rflank = min(cstop - tstop, options.flanks)
targetSeq = ""
if (cstrand == "+"):
targetSeq = tts[tstart - cstart - lflank:tstop - cstart +
rflank]
else:
targetSeq = tts.reverse_complement()[tstart - cstart -
lflank:tstop - cstart +
rflank]
targetStr = [" " for i in range(lflank + tsize + rflank)]
for i in range(lflank, lflank + tsize):
targetStr[i] = '+'
if (target[8] != ""):
for s in target[8].strip('d').split('d'):
if (cstrand == "+"):
targetStr[lflank + int(s)] = '_'
else:
targetStr[lflank + tsize - int(s) - 1] = '_'
on_chromatin_features = 0.
if (options.chromatin != "" and options.chromatinFormat == "bam"):
try:
for pileupcolumn in samfile.pileup(chrom, tstart - lflank,
tstop + rflank):
on_chromatin_features += pileupcolumn.n
on_chromatin_features = "%.3f" % (
on_chromatin_features / (lflank + tsize + rflank))
except:
print >> sys.stderr, "[WARN] bam file exception : %s:%d-%d" % (
chrom, tstart - lflank, tstop + rflank)
elif (options.chromatin != ""
and options.chromatinFormat == "bigwig"):
try:
bwsummary = bigwig.query(chrom, tstart - lflank,
tstop + rflank, 1)
on_chromatin_features = "%.3f" % (bwsummary[0]["mean"])
except:
print >> sys.stderr, "[WARN] bigwig file exception : %s:%d-%d" % (
chrom, tstart - lflank, tstop + rflank)
else:
on_chromatin_features = "-"
off_targets = {
} # hashing start positions (w/r/t on target) and encoding (grouping off-targets)
# branch point
if (processors == 1): # no need to multiprocess
for tfo_region in tfotargets[targetid].keys():
# process each error category
for tfo_error in tfotargets[targetid][tfo_region].keys():
processOfftarget(off_targets, targetid, tfo_region,
tfo_error, lflank, rflank, tsize,
tstrand, samfile, bigwig)
else: # contribute workload
# load up work queue
work_queue = multiprocessing.JoinableQueue(processors * 3)
# create a queue to pass to workers to store the results
result_queue = multiprocessing.Queue()
num_jobs = 0
for tfo_region in tfotargets[targetid].keys():
num_jobs += len(tfotargets[targetid][tfo_region])
# spawn workers
for i in range(min(num_jobs, processors)):
worker = Worker(work_queue, result_queue,
options.chromatin, options.chromatinFormat)
worker.daemon = True
worker.start()
# process off-targets w/r/t to on-target
# process each intersecting interval
for tfo_region in tfotargets[targetid].keys():
# process each error category
for tfo_error in tfotargets[targetid][tfo_region].keys():
work_queue.put(
Job(targetid, tfo_region, tfo_error, lflank,
rflank, tsize, tstrand))
work_queue.close()
work_queue.join()
for job in range(num_jobs):
result = result_queue.get()
for offset in result.keys():
if (not off_targets.has_key(offset)):
off_targets[offset] = {}
for encoding in result[offset].keys():
if (not off_targets[offset].has_key(encoding)):
off_targets[offset][encoding] = result[offset][
encoding]
# merge point
output_sequence_w = ""
output_sequence_c = ""
for i in range(len(targetStr)):
if (targetStr[i] == " "):
output_sequence_w += targetSeq.lower()[i]
output_sequence_c += targetSeq.complement().lower()[i]
else:
output_sequence_w += targetSeq.upper()[i]
output_sequence_c += targetSeq.complement().upper()[i]
# generate oaColumns (data table header)
output_offtargets = ""
for i in range(len(annotations)):
output_offtargets += (
',\n { "sTitle":"%s", "sToolTip":"number of off-targets intersecting with annotation data: %s" }'
% (annotations[i], annotations[i]))
output_offtargets += (
',\n { "sTitle":"","bSortable":false,"bSearchable":false,"sClass":"btn_details","sToolTip":"Toggle detailed off-target inspector" }\n ],\n "aaData":['
)
# generate aaData (data table content)
for offset in off_targets.keys():
for encoding in off_targets[offset]:
ots = off_targets[offset][encoding]
# format chromatin data if available
if (options.chromatin != ""):
ots["chromatin"] = "%.3f" % (ots["chromatin_max"])
else:
ots["chromatin"] = "-"
output_offtargets += '\n ["%s",0,%d,%d,"%d-%d",%d,"%s",%d,[' % (
encoding,
ots["errors"],
offset,
offset - lflank,
offset + len(encoding) - lflank,
ots["copies"],
ots["chromatin"],
ots["length"],
)
for ot in ots["data"]:
output_offtargets += '\n ["%s",%d,%d,"%s",%s],' % (
ot)
output_offtargets = output_offtargets[:-1] + "\n ]"
if (len(annotations) > 0):
output_offtargets += ",%s" % (",".join(
["%s" % el for el in ots["annotation"]]))
output_offtargets += ',""],'
# finalise off-target json object and write output
output_offtargets = ('''{
"bPaginate": true,
"bProcessing": true,
"bAutoWidth": false,
"bInfo": true,
"bLengthChange": true,
"bFilter": true,
"iDisplayLength": 10,
"aLengthMenu": [[10, 50, 100, -1], [10, 50, 100, "All"]],
"bJQueryUI": true,
"bDeferRender": true,
"aaSorting": [[2, "asc"]],
"aoColumns": [
{ "sTitle":"%s","sClass":"monospace left", "sToolTip":"shows the part of the primary target that is responsible for off-targets" },
{ "sTitle":"overlap", "sType": "numeric","sToolTip":"number of nucleotide positions off-targets overlap the primary target" },
{ "sTitle":"errors", "sType": "numeric","sToolTip":"expected number of mismatches between the triplex-forming molecule designed against the primary target and off-targets" },
{ "sTitle":"offset","sType": "numeric","bSearchable":false,"bVisible":false, "sToolTip":"the offset is used internally for computing the lefthand-side alignment" },
{ "sTitle":"sub region", "sToolTip":"region of the primary target this off-target spans" },
{ "sTitle":"copies", "sType": "numeric","sToolTip":"number of copies of this off-target category" },
{ "sTitle":"max. chromatin", "bVisible": %s, "sToolTip":"maximal chromatin score observed in any of the off-targets" },
{ "sTitle":"length", "sType": "numeric","sToolTip":"length of the off-target", "bVisible":false },
{ "sTitle":"offtargets","bVisible": false, "sToolTip":"detailed off-target list with location and annotation" } '''
+ output_offtargets[:-1] + "\n ]\n}") % (
output_sequence_w + "<br/>" +
output_sequence_c, withChromatin)
output = open(options.output_dir + targetid + "_off_targets.json",
"w")
output.write(output_offtargets)
output.close()
# pretty sequence encoding for on-target region
output_sequence_w = ""
output_sequence_c = ""
lastpos = " "
interruptions = 0
for i in range(len(targetStr)):
if (targetStr[i] != lastpos):
if (lastpos != " "):
output_sequence_w += "</span>"
output_sequence_c += "</span>"
if (targetStr[i] == "+"):
output_sequence_w += "<span class='valid_triad'>"
output_sequence_c += "<span class='valid_triad'>"
elif (targetStr[i] == "_"):
output_sequence_w += "<span class='invalid_triad'>"
output_sequence_c += "<span class='invalid_triad'>"
lastpos = targetStr[i]
if (targetStr[i] == "_"):
interruptions += 1
if (targetStr[i] == " "):
output_sequence_w += targetSeq.lower()[i]
output_sequence_c += targetSeq.complement().lower()[i]
else:
output_sequence_w += targetSeq.upper()[i]
output_sequence_c += targetSeq.complement().upper()[i]
if (lastpos == "+" or lastpos == "_"):
output_sequence_w += "</span>"
output_sequence_c += "</span>"
output_sequence = "<span class='monospace flank_triad'>5'-" + output_sequence_w + "-3'<br/>3'-" + output_sequence_c + "-5'</span>"
# count the number of off-targets
offtarget_counter = 0
for tfo_region in tfotargets[targetid].keys():
for tfo_errors in tfotargets[targetid][tfo_region].keys():
offtarget_counter += len(
tfotargets[targetid][tfo_region][tfo_errors])
output_ontargets += '\n\t\t["%s","%s",%d,%d,%d,"%s",%d,"%s",%d,"%s",%d,%d],' % (
targetid, chrom, tstart, tstop,
(tstop - tstart), on_chromatin_features, interruptions,
output_sequence, offtarget_counter, tstrand, lflank, rflank)
# finalizes on-target json object and write output
output_ontargets = '''{
"bPaginate": true,
"bProcessing": true,
"bAutoWidth": false,
"bInfo": true,
"bLengthChange": true,
"bFilter": false,
"iDisplayLength": 10,
"bJQueryUI": true,
"aLengthMenu": [[5, 10, 25, -1], [5, 10, 25, "All"]],
"aoColumns": [
{ "sTitle": "region Id", "sToolTip":"internal id used to identify regions of overlapping putative primary targets" },
{ "sTitle": "chr", "sToolTip":"chromosome this region is located in" },
{ "sTitle": "start", "sType": "numeric", "sToolTip":"chromosomal start position of this region" },
{ "sTitle": "end", "sType": "numeric", "sToolTip":"chromosomal end position of this region" },
{ "sTitle": "length", "sType": "numeric", "sToolTip":"number of nucleotides spanned by this target region" },
{ "sTitle": "chromatin", "bVisible": ''' + withChromatin + ''', "sToolTip":"chromatin score averaged over the shown region" },
{ "sTitle": "Y-interruptions", "sType": "numeric", "sToolTip":"number of pyrimdine interruptions in the polypurine/polypyrimidine tract of the region" },
{ "sTitle": "on-target region", "bSortable": false, "sClass": "left", "sToolTip":"sequence of the region (plus additional flanking positions if specified)" },
{ "sTitle": "off-targets", "bSearchable": false, "bVisible": false, "sToolTip":"total number of off-targets accumulated over all putative primary targets in this region" },
{ "sTitle": "strand", "bSearchable": false, "bVisible": false, "sToolTip":"strand on which the purine tract is located" },
{ "sTitle": "offset left", "sType": "numeric", "bSearchable": false, "bVisible": false, "sToolTip":"number of upstream flanking positions shown in the sequence" },
{ "sTitle": "offset right", "sType": "numeric", "bSearchable": false, "bVisible": false, "sToolTip":"number of downstream flanking positions shown in the sequence" }
],
"aaData": [''' + output_ontargets[:-1] + '\n ]\n}'
output = open(options.output_dir + "primary_target_regions.json", "w")
output.write(output_ontargets)
output.close()
# close file handle
if (options.chromatin != "" and options.chromatinFormat == "bam"):
try:
samfile.close()
except:
exit(1)
