def getEnrichment_worker(arglist):
"""
This is the worker function of plotEnrichment.
In short, given a region, iterate over all reads **starting** in it.
Filter/extend them as requested and check each for an overlap with
findOverlaps. For each overlap, increment the counter for that feature.
"""
chrom, start, end, args, defaultFragmentLength = arglist
if args.verbose:
sys.stderr.write("Processing {}:{}-{}\n".format(chrom, start, end))
gtf = Enrichment(args.BED, keepExons=args.keepExons, labels=args.regionLabels)
olist = []
total = [0] * len(args.bamfiles)
for idx, f in enumerate(args.bamfiles):
odict = dict()
for x in gtf.features:
odict[x] = 0
fh = openBam(f)
chrom = mungeChromosome(chrom, fh.references)
prev_start_pos = None # to store the start positions
for read in fh.fetch(chrom, start, end):
# Filter
if read.pos < start:
# Ensure that a given alignment is processed only once
continue
if read.flag & 4:
continue
if args.minMappingQuality and read.mapq < args.minMappingQuality:
continue
if args.samFlagInclude and read.flag & args.samFlagInclude != args.samFlagInclude:
continue
if args.samFlagExclude and read.flag & args.samFlagExclude != 0:
continue
tLen = getTLen(read)
if args.minFragmentLength > 0 and tLen < args.minFragmentLength:
continue
if args.maxFragmentLength > 0 and tLen > args.maxFragmentLength:
continue
if args.ignoreDuplicates and prev_start_pos \
and prev_start_pos == (read.reference_start, read.pnext, read.is_reverse):
continue
prev_start_pos = (read.reference_start, read.pnext, read.is_reverse)
total[idx] += 1
# Get blocks, possibly extending
features = gtf.findOverlaps(chrom, getBAMBlocks(read, defaultFragmentLength, args.centerReads, args.Offset))
if features is not None and len(features) > 0:
for x in features:
odict[x] += 1
olist.append(odict)
return olist, gtf.features, total
def getEnrichment_worker(arglist):
"""
This is the worker function of plotEnrichment.
In short, given a region, iterate over all reads **starting** in it.
Filter/extend them as requested and check each for an overlap with
findOverlaps. For each overlap, increment the counter for that feature.
"""
chrom, start, end, args, defaultFragmentLength = arglist
if args.verbose:
sys.stderr.write("Processing {}:{}-{}\n".format(chrom, start, end))
gtf = Enrichment(args.BED, keepExons=args.keepExons, labels=args.regionLabels)
olist = []
for f in args.bamfiles:
odict = dict()
for x in gtf.features:
odict[x] = 0
fh = openBam(f)
chrom = mungeChromosome(chrom, fh.references)
prev_start_pos = None # to store the start positions
for read in fh.fetch(chrom, start, end):
# Filter
if read.pos < start:
# Ensure that a given alignment is processed only once
continue
if read.flag & 4:
continue
if args.minMappingQuality and read.mapq < args.minMappingQuality:
continue
if args.samFlagInclude and read.flag & args.samFlagInclude != args.samFlagInclude:
continue
if args.samFlagExclude and read.flag & args.samFlagExclude != 0:
continue
if args.minFragmentLength > 0 and abs(read.template_length) < args.minFragmentLength:
continue
if args.maxFragmentLength > 0 and abs(read.template_length) > args.maxFragmentLength:
continue
if args.ignoreDuplicates and prev_start_pos \
and prev_start_pos == (read.reference_start, read.pnext, read.is_reverse):
continue
prev_start_pos = (read.reference_start, read.pnext, read.is_reverse)
# Get blocks, possibly extending
features = gtf.findOverlaps(chrom, getBAMBlocks(read, defaultFragmentLength, args.centerReads))
if features is not None and len(features) > 0:
for x in features:
odict[x] += 1
olist.append(odict)
return olist, gtf.features
def preload(self, regions, tmpDir=None):
"""
Given a sample and a set of regions, write a bigWig file containing the underlying signal.
This function returns the file name, which needs to be deleted by the calling function at some point.
This sends queries one chromosome at a time, due to memory limits on deepBlue
"""
startTime = datetime.datetime.now()
regions2 = mergeRegions(regions)
# Make a temporary file
f = tempfile.NamedTemporaryFile(delete=False, dir=tmpDir)
fname = f.name
f.close()
# Start with the bigWig file
bw = pyBigWig.open(fname, "w")
bw.addHeader(self.chromsTuple, maxZooms=0) # This won't work in IGV!
# Make a string out of everything in a resonable order
for k, v in self.chromsTuple:
# Munge chromosome names as appropriate
chrom = mungeChromosome(k, regions2.keys())
if not chrom:
continue
if chrom not in regions2 or len(regions2) == 0:
continue
regionsStr = "\n".join([
"{}\t{}\t{}".format(k, reg[0], reg[1])
for reg in regions2[chrom]
])
regionsStr += "\n"
# Send the regions
(status,
regionsID) = self.server.input_regions(self.genome, regionsStr,
self.userKey)
if status != "okay":
raise RuntimeError(
"Received the following error while sending regions for '{}': {}"
.format(regionsID, self.sample))
# Get the experiment information
(status,
queryID) = self.server.select_experiments(self.sample, k, None,
None, self.userKey)
if status != "okay":
raise RuntimeError(
"Received the following error while running select_experiments on file '{}': {}"
.format(self.sample, queryID))
if not queryID:
raise RuntimeError(
"Somehow, we received None as a query ID (file '{}')".
format(self.sample))
# Intersect
(status,
intersectID) = self.server.intersection(queryID, regionsID,
self.userKey)
if status != "okay":
raise RuntimeError(
"Received the following error while running intersection on file '{}': {}"
.format(self.sample, intersectID))
if not intersectID:
raise RuntimeError(
"Somehow, we received None as an intersect ID (file '{}')".
format(self.sample))
# Query the regions
(status, reqID) = self.server.get_regions(intersectID,
"START,END,VALUE",
self.userKey)
if status != "okay":
raise RuntimeError(
"Received the following error while fetching regions in file '{}': {}"
.format(self.sample, reqID))
# Wait for the server to process the data
(status, info) = self.server.info(reqID, self.userKey)
request_status = info[0]["state"]
while request_status != "done" and request_status != "failed":
time.sleep(0.1)
(status, info) = self.server.info(reqID, self.userKey)
request_status = info[0]["state"]
# Get the actual data
(status, resp) = self.server.get_request_data(reqID, self.userKey)
if status != "okay":
raise RuntimeError(
"Received the following error while fetching data in file '{}': {}"
.format(self.sample, resp))
for intervals in resp.split("\n"):
interval = intervals.split("\t")
if interval[0] == '':
continue
bw.addEntries([k], [int(interval[0])],
ends=[int(interval[1])],
values=[float(interval[2])])
bw.close()
sys.stderr.write("{} done (took {})\n".format(
self.sample,
datetime.datetime.now() - startTime))
sys.stderr.flush()
return fname
def preload(self, regions, tmpDir=None):
"""
Given a sample and a set of regions, write a bigWig file containing the underlying signal.
This function returns the file name, which needs to be deleted by the calling function at some point.
This sends queries one chromosome at a time, due to memory limits on deepBlue
"""
startTime = datetime.datetime.now()
regions2 = mergeRegions(regions)
# Make a temporary file
f = tempfile.NamedTemporaryFile(delete=False, dir=tmpDir)
fname = f.name
f.close()
# Start with the bigWig file
bw = pyBigWig.open(fname, "w")
bw.addHeader(self.chromsTuple, maxZooms=0) # This won't work in IGV!
# Make a string out of everything in a resonable order
for k, v in self.chromsTuple:
# Munge chromosome names as appropriate
chrom = mungeChromosome(k, regions2.keys())
if not chrom:
continue
if chrom not in regions2 or len(regions2) == 0:
continue
regionsStr = "\n".join(["{}\t{}\t{}".format(k, reg[0], reg[1]) for reg in regions2[chrom]])
regionsStr += "\n"
# Send the regions
(status, regionsID) = self.server.input_regions(self.genome, regionsStr, self.userKey)
if status != "okay":
raise RuntimeError("Received the following error while sending regions for '{}': {}".format(regionsID, self.sample))
# Get the experiment information
(status, queryID) = self.server.select_experiments(self.sample, k, None, None, self.userKey)
if status != "okay":
raise RuntimeError("Received the following error while running select_experiments on file '{}': {}".format(self.sample, queryID))
if not queryID:
raise RuntimeError("Somehow, we received None as a query ID (file '{}')".format(self.sample))
# Intersect
(status, intersectID) = self.server.intersection(queryID, regionsID, self.userKey)
if status != "okay":
raise RuntimeError("Received the following error while running intersection on file '{}': {}".format(self.sample, intersectID))
if not intersectID:
raise RuntimeError("Somehow, we received None as an intersect ID (file '{}')".format(self.sample))
# Query the regions
(status, reqID) = self.server.get_regions(intersectID, "START,END,VALUE", self.userKey)
if status != "okay":
raise RuntimeError("Received the following error while fetching regions in file '{}': {}".format(self.sample, reqID))
# Wait for the server to process the data
(status, info) = self.server.info(reqID, self.userKey)
request_status = info[0]["state"]
while request_status != "done" and request_status != "failed":
time.sleep(0.1)
(status, info) = self.server.info(reqID, self.userKey)
request_status = info[0]["state"]
# Get the actual data
(status, resp) = self.server.get_request_data(reqID, self.userKey)
if status != "okay":
raise RuntimeError("Received the following error while fetching data in file '{}': {}".format(self.sample, resp))
for intervals in resp.split("\n"):
interval = intervals.split("\t")
if interval[0] == '':
continue
bw.addEntries([k], [int(interval[0])], ends=[int(interval[1])], values=[float(interval[2])])
bw.close()
sys.stderr.write("{} done (took {})\n".format(self.sample, datetime.datetime.now() - startTime))
sys.stderr.flush()
return fname
def getFiltered_worker(arglist):
chrom, start, end, args = arglist
# Fix the bounds
if end - start > args.binSize and end - start > args.distanceBetweenBins:
end -= args.distanceBetweenBins
if end <= start:
end = start + 1
o = []
for fname in args.bamfiles:
fh = bamHandler.openBam(fname)
chromUse = utilities.mungeChromosome(chrom, fh.references)
prev_pos = set()
lpos = None
minMapq = 0
samFlagInclude = 0
samFlagExclude = 0
internalDupes = 0
externalDupes = 0
singletons = 0
filterRNAstrand = 0
nFiltered = 0
total = 0 # This is only used to estimate the percentage affected
for read in fh.fetch(chromUse, start, end):
filtered = 0
if read.pos < start:
# ensure that we never double count (in case distanceBetweenBins == 0)
continue
if read.flag & 4:
# Ignore unmapped reads, they were counted already
continue
if args.minMappingQuality and read.mapq < args.minMappingQuality:
filtered = 1
minMapq += 1
if args.samFlagInclude and read.flag & args.samFlagInclude != args.samFlagInclude:
filtered = 1
samFlagInclude += 1
if args.samFlagExclude and read.flag & args.samFlagExclude != 0:
filtered = 1
samFlagExclude += 1
if args.ignoreDuplicates:
# Assuming more or less concordant reads, use the fragment bounds, otherwise the start positions
if read.tlen >= 0:
s = read.pos
e = s + read.tlen
else:
s = read.pnext
e = s - read.tlen
if read.reference_id != read.next_reference_id:
e = read.pnext
if lpos is not None and lpos == read.reference_start \
and (s, e, read.next_reference_id, read.is_reverse) in prev_pos:
filtered = 1
internalDupes += 1
if lpos != read.reference_start:
prev_pos.clear()
lpos = read.reference_start
prev_pos.add((s, e, read.next_reference_id, read.is_reverse))
if read.is_duplicate:
filtered = 1
externalDupes += 1
if read.is_paired and read.mate_is_unmapped:
filtered = 1
singletons += 1
# filterRNAstrand
if args.filterRNAstrand:
if read.is_paired:
if args.filterRNAstrand == 'forward':
if read.flag & 144 == 128 or read.flag & 96 == 64:
pass
else:
filtered = 1
filterRNAstrand += 1
elif args.filterRNAstrand == 'reverse':
if read.flag & 144 == 144 or read.flag & 96 == 96:
pass
else:
filtered = 1
filterRNAstrand += 1
else:
if args.filterRNAstrand == 'forward':
if read.flag & 16 == 16:
pass
else:
filtered = 1
filterRNAstrand += 1
elif args.filterRNAstrand == 'reverse':
if read.flag & 16 == 0:
pass
else:
filtered = 1
filterRNAstrand += 1
total += 1
nFiltered += filtered
fh.close()
# Append a tuple to the output
tup = (total, nFiltered, minMapq, samFlagInclude, samFlagExclude,
internalDupes, externalDupes, singletons, filterRNAstrand)
o.append(tup)
return o
def getEnrichment_worker(arglist):
"""
This is the worker function of plotEnrichment.
In short, given a region, iterate over all reads **starting** in it.
Filter/extend them as requested and check each for an overlap with
findOverlaps. For each overlap, increment the counter for that feature.
"""
chrom, start, end, args, defaultFragmentLength = arglist
if args.verbose:
sys.stderr.write("Processing {}:{}-{}\n".format(chrom, start, end))
olist = []
total = [0] * len(args.bamfiles)
for idx, f in enumerate(args.bamfiles):
odict = dict()
for x in gtf.features:
odict[x] = 0
fh = openBam(f)
chrom = mungeChromosome(chrom, fh.references)
lpos = None
prev_pos = set()
for read in fh.fetch(chrom, start, end):
# Filter
if read.pos < start:
# Ensure that a given alignment is processed only once
continue
if read.flag & 4:
continue
if args.minMappingQuality and read.mapq < args.minMappingQuality:
continue
if args.samFlagInclude and read.flag & args.samFlagInclude != args.samFlagInclude:
continue
if args.samFlagExclude and read.flag & args.samFlagExclude != 0:
continue
tLen = getTLen(read)
if args.minFragmentLength > 0 and tLen < args.minFragmentLength:
continue
if args.maxFragmentLength > 0 and tLen > args.maxFragmentLength:
continue
if args.ignoreDuplicates:
# Assuming more or less concordant reads, use the fragment bounds, otherwise the start positions
if tLen >= 0:
s = read.pos
e = s + tLen
else:
s = read.pnext
e = s - tLen
if read.reference_id != read.next_reference_id:
e = read.pnext
if lpos is not None and lpos == read.reference_start \
and (s, e, read.next_reference_id, read.is_reverse) in prev_pos:
continue
if lpos != read.reference_start:
prev_pos.clear()
lpos = read.reference_start
prev_pos.add((s, e, read.next_reference_id, read.is_reverse))
total[idx] += 1
# Get blocks, possibly extending
features = gtf.findOverlaps(
chrom,
getBAMBlocks(read, defaultFragmentLength, args.centerReads,
args.Offset))
if features is not None and len(features) > 0:
for x in features:
odict[x] += 1
olist.append(odict)
return olist, gtf.features, total
def getFiltered_worker(arglist):
chrom, start, end, args = arglist
# Fix the bounds
if end - start > args.binSize and end - start > args.distanceBetweenBins:
end -= args.distanceBetweenBins
if end <= start:
end = start + 1
o = []
for fname in args.bamfiles:
fh = bamHandler.openBam(fname)
chromUse = utilities.mungeChromosome(chrom, fh.references)
prev_pos = set()
lpos = None
minMapq = 0
samFlagInclude = 0
samFlagExclude = 0
internalDupes = 0
externalDupes = 0
singletons = 0
filterRNAstrand = 0
nFiltered = 0
total = 0 # This is only used to estimate the percentage affected
for read in fh.fetch(chromUse, start, end):
filtered = 0
if read.pos < start:
# ensure that we never double count (in case distanceBetweenBins == 0)
continue
if read.flag & 4:
# Ignore unmapped reads, they were counted already
continue
if args.minMappingQuality and read.mapq < args.minMappingQuality:
filtered = 1
minMapq += 1
if args.samFlagInclude and read.flag & args.samFlagInclude != args.samFlagInclude:
filtered = 1
samFlagInclude += 1
if args.samFlagExclude and read.flag & args.samFlagExclude != 0:
filtered = 1
samFlagExclude += 1
if args.ignoreDuplicates:
# Assuming more or less concordant reads, use the fragment bounds, otherwise the start positions
if read.tlen >= 0:
s = read.pos
e = s + read.tlen
else:
s = read.pnext
e = s - read.tlen
if read.reference_id != read.next_reference_id:
e = read.pnext
if lpos is not None and lpos == read.reference_start \
and (s, e, read.next_reference_id, read.is_reverse) in prev_pos:
filtered = 1
internalDupes += 1
if lpos != read.reference_start:
prev_pos.clear()
lpos = read.reference_start
prev_pos.add((s, e, read.next_reference_id, read.is_reverse))
if read.is_duplicate:
filtered = 1
externalDupes += 1
if read.is_paired and read.mate_is_unmapped:
filtered = 1
singletons += 1
# filterRNAstrand
if args.filterRNAstrand:
if read.is_paired:
if args.filterRNAstrand == 'forward':
if read.flag & 144 == 128 or read.flag & 96 == 64:
pass
else:
filtered = 1
filterRNAstrand += 1
elif args.filterRNAstrand == 'reverse':
if read.flag & 144 == 144 or read.flag & 96 == 96:
pass
else:
filtered = 1
filterRNAstrand += 1
else:
if args.filterRNAstrand == 'forward':
if read.flag & 16 == 16:
pass
else:
filtered = 1
filterRNAstrand += 1
elif args.filterRNAstrand == 'reverse':
if read.flag & 16 == 0:
pass
else:
filtered = 1
filterRNAstrand += 1
total += 1
nFiltered += filtered
fh.close()
# Append a tuple to the output
tup = (total, nFiltered, minMapq, samFlagInclude, samFlagExclude, internalDupes, externalDupes, singletons, filterRNAstrand)
o.append(tup)
return o
def getEnrichment_worker(arglist):
"""
This is the worker function of plotEnrichment.
In short, given a region, iterate over all reads **starting** in it.
Filter/extend them as requested and check each for an overlap with
findOverlaps. For each overlap, increment the counter for that feature.
"""
chrom, start, end, args, defaultFragmentLength = arglist
if args.verbose:
sys.stderr.write("Processing {}:{}-{}\n".format(chrom, start, end))
olist = []
total = [0] * len(args.bamfiles)
for idx, f in enumerate(args.bamfiles):
odict = dict()
for x in gtf.features:
odict[x] = 0
fh = openBam(f)
chrom = mungeChromosome(chrom, fh.references)
lpos = None
prev_pos = set()
for read in fh.fetch(chrom, start, end):
# Filter
if read.pos < start:
# Ensure that a given alignment is processed only once
continue
if read.flag & 4:
continue
if args.minMappingQuality and read.mapq < args.minMappingQuality:
continue
if args.samFlagInclude and read.flag & args.samFlagInclude != args.samFlagInclude:
continue
if args.samFlagExclude and read.flag & args.samFlagExclude != 0:
continue
tLen = getTLen(read)
if args.minFragmentLength > 0 and tLen < args.minFragmentLength:
continue
if args.maxFragmentLength > 0 and tLen > args.maxFragmentLength:
continue
if args.ignoreDuplicates:
# Assuming more or less concordant reads, use the fragment bounds, otherwise the start positions
if tLen >= 0:
s = read.pos
e = s + tLen
else:
s = read.pnext
e = s - tLen
if read.reference_id != read.next_reference_id:
e = read.pnext
if lpos is not None and lpos == read.reference_start \
and (s, e, read.next_reference_id, read.is_reverse) in prev_pos:
continue
if lpos != read.reference_start:
prev_pos.clear()
lpos = read.reference_start
prev_pos.add((s, e, read.next_reference_id, read.is_reverse))
total[idx] += 1
# Get blocks, possibly extending
features = gtf.findOverlaps(chrom, getBAMBlocks(read, defaultFragmentLength, args.centerReads, args.Offset))
if features is not None and len(features) > 0:
for x in features:
odict[x] += 1
olist.append(odict)
return olist, gtf.features, total
