def set_format_scatterplot(axScatter, **kwargs):
min_x, max_x = None, None
min_y, max_y = None, None
if kwargs['plot'] == 'blobplot':
min_x, max_x = 0, 1
major_xticks = MultipleLocator(0.2)
minor_xticks = AutoMinorLocator(20)
min_y, max_y = kwargs['min_cov']*0.1, kwargs['max_cov']+1000
axScatter.set_yscale('log')
axScatter.set_xscale('linear')
axScatter.xaxis.set_major_locator(major_xticks)
axScatter.xaxis.set_minor_locator(minor_xticks)
elif kwargs['plot'] == 'covplot':
min_x, max_x = kwargs['min_cov']*0.1, kwargs['max_cov']+1000
min_y, max_y = kwargs['min_cov']*0.1, kwargs['max_cov']+1000
axScatter.set_yscale('log')
axScatter.set_xscale('log')
else:
BtLog.error('34' % kwargs['plot'])
axScatter.set_xlim( (min_x, max_x) )
axScatter.set_ylim( (min_y, max_y) ) # This sets the max-Coverage so that all libraries + sum are at the same scale
axScatter.grid(True, which="major", lw=2., color=WHITE, linestyle='-')
axScatter.set_axisbelow(True)
axScatter.xaxis.labelpad = 20
axScatter.yaxis.labelpad = 20
axScatter.yaxis.get_major_ticks()[0].label1.set_visible(False)
axScatter.tick_params(axis='both', which='both', direction='out')
return axScatter
def parseCas(infile, order_of_blobs):
if not isfile(infile):
BtLog.error('0', infile)
seqs_total, reads_total, reads_mapped = checkCas(infile)
progress_unit = int(len(order_of_blobs)/100)
cas_line_re = re.compile(r"\s+(\d+)\s+(\d+)\s+(\d+)\s+(\d+.\d{2})\s+(\d+)\s+(\d+.\d{2})")
command = "clc_mapping_info -n " + infile
cov_dict = {}
read_cov_dict = {}
seqs_parsed = 0
if (runCmd(command=command)):
for line in runCmd(command=command):
cas_line_match = cas_line_re.search(line)
if cas_line_match:
idx = int(cas_line_match.group(1)) - 1 # -1 because index of contig list starts with zero
try:
name = order_of_blobs[idx]
reads = int(cas_line_match.group(3))
cov = float(cas_line_match.group(6))
cov_dict[name] = cov
read_cov_dict[name] = reads
seqs_parsed += 1
except:
pass
BtLog.progress(seqs_parsed, progress_unit, seqs_total)
return cov_dict, reads_total, reads_mapped, read_cov_dict
def parseCovFromHeader(fasta_type, header):
'''
Returns the coverage from the header of a FASTA
sequence depending on the assembly type
'''
ASSEMBLY_TYPES = [None, 'spades', 'velvet', 'platanus']
if not fasta_type in ASSEMBLY_TYPES:
BtLog.error('2', ",".join(ASSEMBLY_TYPES[1:]))
if fasta_type == 'spades':
spades_match_re = re.compile(r"_cov_(\d+\.*\d*)")
cov = re.findall(r"_cov_(\d+\.*\d*)", header)
return float(spades_match_re.findall(header)[0])
elif fasta_type == 'velvet':
return float(header.split("_")[-1])
#elif fasta_type == 'abyss' or fasta_type == 'soap':
# temp = header.split(" ")
# return float(temp[2]/(temp[1]+1-75))
elif fasta_type == 'platanus':
temp = header.rstrip("\n").split("_")
if len(temp) >= 3:
return float(temp[2].replace("cov", "")) # scaffold/scaffoldBubble/contig
else:
return float(temp[1].replace("cov", "")) # gapClosed
else:
pass
def parseJson(infile):
'''http://artem.krylysov.com/blog/2015/09/29/benchmark-python-json-libraries/'''
if not isfile(infile):
BtLog.error('0', infile)
import time
start = time.time()
json_parser = ''
with open(infile, 'r') as fh:
print BtLog.status_d['15']
json_string = fh.read()
try:
import ujson as json # fastest
json_parser = 'ujson'
print BtLog.status_d['16'] % json_parser
except ImportError:
try:
import simplejson as json # fast
json_parser = 'simplejson'
except ImportError:
import json # default
json_parser = 'json'
print BtLog.status_d['17'] % json_parser
try:
obj = json.loads(json_string.decode("ascii"))
except ValueError:
BtLog.error('37', infile, "BlobDB")
data = byteify(obj)
print BtLog.status_d['20'] % (time.time() - start)
return data
def main():
#main_dir = dirname(__file__)
args = docopt(__doc__)
fasta_f = args['--infile']
fasta_type = args['--type']
sam_fs = args['--sam']
bam_fs = args['--bam']
cov_fs = args['--cov']
cas_fs = args['--cas']
hit_fs = args['--hitsfile']
prefix = args['--out']
nodesDB_f = args['--db']
names_f = args['--names']
nodes_f = args['--nodes']
taxrules = args['--taxrule']
min_bitscore_diff = float(args['--min_diff'])
tax_collision_random = args['--tax_collision_random']
title = args['--title']
# outfile
out_f = BtIO.getOutFile("blobDB", prefix, "json")
if not (title):
title = out_f
# coverage
if not (fasta_type) and not bam_fs and not sam_fs and not cov_fs and not cas_fs:
BtLog.error('1')
cov_libs = [BtCore.CovLibObj('bam' + str(idx), 'bam', lib_f) for idx, lib_f in enumerate(bam_fs)] + \
[BtCore.CovLibObj('sam' + str(idx), 'sam', lib_f) for idx, lib_f in enumerate(sam_fs)] + \
[BtCore.CovLibObj('cas' + str(idx), 'cas', lib_f) for idx, lib_f in enumerate(cas_fs)] + \
[BtCore.CovLibObj('cov' + str(idx), 'cov', lib_f) for idx, lib_f in enumerate(cov_fs)]
# taxonomy
hit_libs = [BtCore.HitLibObj('tax' + str(idx), 'tax', lib_f) for idx, lib_f in enumerate(hit_fs)]
# Create BlobDB object
blobDb = BtCore.BlobDb(title)
blobDb.version = blobtools.__version__
# Parse FASTA
blobDb.parseFasta(fasta_f, fasta_type)
# Parse nodesDB OR names.dmp, nodes.dmp
nodesDB_default = join(blobtools.DATADIR, "nodesDB.txt")
nodesDB, nodesDB_f = BtIO.parseNodesDB(nodes=nodes_f, names=names_f, nodesDB=nodesDB_f, nodesDBdefault=nodesDB_default)
blobDb.nodesDB_f = nodesDB_f
# Parse similarity hits
if (hit_libs):
blobDb.parseHits(hit_libs)
blobDb.computeTaxonomy(taxrules, nodesDB, min_bitscore_diff, tax_collision_random)
else:
print BtLog.warn_d['0']
# Parse coverage
blobDb.parseCoverage(covLibObjs=cov_libs, no_base_cov=None)
# Generating BlobDB and writing to file
print BtLog.status_d['7'] % out_f
BtIO.writeJson(blobDb.dump(), out_f)
def parseCovFromHeader(fasta_type, header):
'''
Returns the coverage from the header of a FASTA
sequence depending on the assembly type
'''
ASSEMBLY_TYPES = [None, 'spades', 'velvet', 'platanus']
if not fasta_type in ASSEMBLY_TYPES:
BtLog.error('2', ",".join(ASSEMBLY_TYPES[1:]))
if fasta_type == 'spades':
spades_match_re = re.compile(r"_cov_(\d+\.*\d*)")
cov = re.findall(r"_cov_(\d+\.*\d*)", header)
return float(spades_match_re.findall(header)[0])
elif fasta_type == 'velvet':
return float(header.split("_")[-1])
#elif fasta_type == 'abyss' or fasta_type == 'soap':
# temp = header.split(" ")
# return float(temp[2]/(temp[1]+1-75))
elif fasta_type == 'platanus':
temp = header.rstrip("\n").split("_")
if len(temp) >= 3:
return float(temp[2].replace("cov",
"")) # scaffold/scaffoldBubble/contig
else:
return float(temp[1].replace("cov", "")) # gapClosed
else:
pass
def main():
args = docopt(__doc__)
bam_f = args['--bam']
include_f = args['--include']
exclude_f = args['--exclude']
out_prefix = args['--out']
gzip = args['--gzip']
do_sort = args['--sort']
keep_sorted = args['--keep']
sort_threads = int(args['--threads'])
print BtLog.status_d['22'] % bam_f
out_f = BtIO.getOutFile(bam_f, out_prefix, None)
if include_f and exclude_f:
print BtLog.error('43')
elif include_f:
sequence_list = BtIO.parseList(include_f)
BtIO.parseBamForFilter(bam_f, out_f, sequence_list, None, gzip,
do_sort, keep_sorted, sort_threads)
elif exclude_f:
sequence_list = BtIO.parseList(exclude_f)
BtIO.parseBamForFilter(bam_f, out_f, None, sequence_list, gzip,
do_sort, keep_sorted, sort_threads)
else:
BtIO.parseBamForFilter(bam_f, out_f, None, None, gzip, do_sort,
keep_sorted, sort_threads)
def set_format_scatterplot(axScatter, **kwargs):
min_x, max_x = None, None
min_y, max_y = None, None
if kwargs['plot'] == 'blobplot':
min_x, max_x = 0, 1
major_xticks = MultipleLocator(0.2)
minor_xticks = AutoMinorLocator(20)
min_y, max_y = kwargs['min_cov']*0.1, kwargs['max_cov']+1000
axScatter.set_yscale('log')
axScatter.set_xscale('linear')
axScatter.xaxis.set_major_locator(major_xticks)
axScatter.xaxis.set_minor_locator(minor_xticks)
elif kwargs['plot'] == 'covplot':
min_x, max_x = kwargs['min_cov']*0.1, kwargs['max_cov']+1000
min_y, max_y = kwargs['min_cov']*0.1, kwargs['max_cov']+1000
axScatter.set_yscale('log')
axScatter.set_xscale('log')
else:
BtLog.error('34' % kwargs['plot'])
axScatter.set_xlim( (min_x, max_x) )
axScatter.set_ylim( (min_y, max_y) ) # This sets the max-Coverage so that all libraries + sum are at the same scale
axScatter.grid(True, which="major", lw=2., color=WHITE, linestyle='-')
axScatter.set_axisbelow(True)
axScatter.xaxis.labelpad = 20
axScatter.yaxis.labelpad = 20
axScatter.yaxis.get_major_ticks()[0].label1.set_visible(False)
axScatter.tick_params(axis='both', which='both', direction='out')
return axScatter
def subselect_cov_libs(self, cov_lib_dict, cov_lib_selection):
selected_cov_libs = []
cov_lib_selection_error = 0
if (cov_lib_selection):
if cov_lib_selection == 'covsum':
selected_cov_libs.append('covsum')
elif "," in cov_lib_selection:
selected_cov_libs = cov_lib_selection.split(",")
if not set(selected_cov_libs).issubset(set(cov_lib_dict.keys())):
cov_lib_selection_error = 1
else:
selected_cov_libs.append(cov_lib_selection)
if not cov_lib_selection in cov_lib_dict:
cov_lib_selection_error = 1
else:
selected_cov_libs = cov_lib_dict.keys()
if cov_lib_selection_error:
covlib_string = []
for covlib in cov_lib_dict:
cov_lib_f = cov_lib_dict[covlib]['f']
if not cov_lib_f:
cov_lib_f = "sum of coverages from all covlibs"
covlib_string.append("\t\t%s : %s" % (covlib, cov_lib_f))
BtLog.error('33', "\n".join(covlib_string))
return selected_cov_libs
def subselect_cov_libs(self, cov_lib_dict, cov_lib_selection):
selected_cov_libs = []
cov_lib_selection_error = 0
if (cov_lib_selection):
if cov_lib_selection == 'covsum':
selected_cov_libs.append('covsum')
elif "," in cov_lib_selection:
selected_cov_libs = cov_lib_selection.split(",")
if not set(selected_cov_libs).issubset(set(cov_lib_dict.keys())):
cov_lib_selection_error = 1
else:
selected_cov_libs.append(cov_lib_selection)
if not cov_lib_selection in cov_lib_dict:
cov_lib_selection_error = 1
else:
selected_cov_libs = cov_lib_dict.keys()
if cov_lib_selection_error:
covlib_string = []
for covlib in cov_lib_dict:
cov_lib_f = cov_lib_dict[covlib]['f']
if not cov_lib_f:
cov_lib_f = "sum of coverages from all covlibs"
covlib_string.append("\t\t%s : %s" % (covlib, cov_lib_f))
BtLog.error('33', "\n".join(covlib_string))
return selected_cov_libs
def parseFasta(self, fasta_f, fasta_type):
print BtLog.status_d["1"] % ("FASTA", fasta_f)
self.assembly_f = abspath(fasta_f)
if fasta_type:
# Set up CovLibObj for coverage in assembly header
self.covLibs[fasta_type] = CovLibObj(fasta_type, fasta_type, fasta_f)
for name, seq in BtIO.readFasta(fasta_f):
blObj = BlObj(name, seq)
if not blObj.name in self.dict_of_blobs:
self.seqs += 1
self.length += blObj.length
self.n_count += blObj.n_count
if fasta_type:
cov = BtIO.parseCovFromHeader(fasta_type, blObj.name)
self.covLibs[fasta_type].cov_sum += cov
blObj.addCov(fasta_type, cov)
self.order_of_blobs.append(blObj.name)
self.dict_of_blobs[blObj.name] = blObj
else:
BtLog.error("5", blObj.name)
if self.seqs == 0 or self.length == 0:
BtLog.error("1")
def parseCas(infile, order_of_blobs):
if not isfile(infile):
BtLog.error('0', infile)
seqs_total, reads_total, reads_mapped = checkCas(infile)
progress_unit = int(len(order_of_blobs) / 100)
cas_line_re = re.compile(
r"\s+(\d+)\s+(\d+)\s+(\d+)\s+(\d+.\d{2})\s+(\d+)\s+(\d+.\d{2})")
command = "clc_mapping_info -n " + infile
cov_dict = {}
read_cov_dict = {}
seqs_parsed = 0
if (runCmd(command=command)):
for line in runCmd(command=command):
cas_line_match = cas_line_re.search(line)
if cas_line_match:
idx = int(cas_line_match.group(
1)) - 1 # -1 because index of contig list starts with zero
try:
name = order_of_blobs[idx]
reads = int(cas_line_match.group(3))
cov = float(cas_line_match.group(6))
cov_dict[name] = cov
read_cov_dict[name] = reads
seqs_parsed += 1
except:
pass
BtLog.progress(seqs_parsed, progress_unit, seqs_total)
return cov_dict, reads_total, reads_mapped, read_cov_dict
def parseFasta(self, fasta_f, fasta_type):
print BtLog.status_d['1'] % ('FASTA', fasta_f)
self.assembly_f = abspath(fasta_f)
if (fasta_type):
# Set up CovLibObj for coverage in assembly header
self.covLibs[fasta_type] = CovLibObj(fasta_type, fasta_type, fasta_f)
for name, seq in BtIO.readFasta(fasta_f):
blObj = BlObj(name, seq)
if not blObj.name in self.dict_of_blobs:
self.seqs += 1
self.length += blObj.length
self.n_count += blObj.n_count
if (fasta_type):
cov = BtIO.parseCovFromHeader(fasta_type, blObj.name)
self.covLibs[fasta_type].cov_sum += cov
blObj.addCov(fasta_type, cov)
self.order_of_blobs.append(blObj.name)
self.dict_of_blobs[blObj.name] = blObj
else:
BtLog.error('5', blObj.name)
if self.seqs == 0 or self.length == 0:
BtLog.error('1')
def parseJson(infile):
'''http://artem.krylysov.com/blog/2015/09/29/benchmark-python-json-libraries/'''
if not isfile(infile):
BtLog.error('0', infile)
import time
start = time.time()
json_parser = ''
with open(infile, 'r') as fh:
print BtLog.status_d['15']
json_string = fh.read()
try:
import ujson as json # fastest
json_parser = 'ujson'
print BtLog.status_d['16'] % json_parser
except ImportError:
try:
import simplejson as json # fast
json_parser = 'simplejson'
except ImportError:
import json # default
json_parser = 'json'
print BtLog.status_d['17'] % json_parser
try:
obj = json.loads(json_string.decode("ascii"))
except ValueError:
BtLog.error('37', infile, "BlobDB")
data = byteify(obj)
print BtLog.status_d['20'] % (time.time() - start)
return data
def parseSet(infile):
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
items = set()
for l in fh:
items.add(l.rstrip("\n").lstrip(">"))
return items
def parseList(infile):
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
items = []
for l in fh:
items.append(l.rstrip("\n"))
return items
def parseList(infile):
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
items = []
for l in fh:
items.append(l.rstrip("\n"))
return items
def parseSet(infile):
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
items = set()
for l in fh:
items.add(l.rstrip("\n").lstrip(">"))
return items
def parseColours(infile):
items = {}
if infile:
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
for l in fh:
temp = l.rstrip("\n").split(",")
items[temp[0]] = temp[1]
return items
def parseColours(infile):
items = {}
if infile:
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
for l in fh:
temp = l.rstrip("\n").split(",")
items[temp[0]] = temp[1]
return items
def parseBam(infile, set_of_blobs, no_base_cov_flag):
'''
checkBam returns reads_total and reads_mapped
base_cov_dict is list of coverages for each contigs, since list appending should be faster
'''
if not isfile(infile):
BtLog.error('0', infile)
reads_total, reads_mapped = checkBam(infile)
progress_unit = int(reads_mapped / 1000)
base_cov_dict = {blob: [] for blob in set_of_blobs}
#base_cov_dict = {blob : 0 for blob in set_of_blobs}
read_cov_dict = {blob: 0 for blob in set_of_blobs}
cigar_match_re = re.compile(
r"(\d+)M|X|=") # only gets digits before M,X,='s
# execute samtools to get only mapped reads (no optial duplicates, no 2nd-ary alignment)
command = "samtools view -F 1024 -F 4 -F 256 " + infile
seen_reads = 0
#import time
#start = time.time()
if not (no_base_cov_flag):
for line in runCmd(command=command):
seen_reads += 1
match = line.split()
try:
base_cov_dict[match[2]].append(
sum([
int(matching)
for matching in cigar_match_re.findall(match[5])
]))
#base_cov_dict[match[2]] += sum([int(matching) for matching in cigar_match_re.findall(match[5])])
read_cov_dict[match[2]] += 1
except:
print BtLog.warn_d['2'] % (match[2])
BtLog.progress(seen_reads, progress_unit, reads_mapped)
else:
for line in runCmd(command=command):
seen_reads += 1
match = line.split()
try:
read_cov_dict[match[2]] += 1
except:
print BtLog.warn_d['2'] % (match[2])
BtLog.progress(seen_reads, progress_unit, reads_mapped)
if not int(reads_mapped) == int(seen_reads):
print BtLog.warn_d['3'] % (reads_mapped, seen_reads)
base_cov_dict = {
seq_name: sum(base_covs)
for seq_name, base_covs in base_cov_dict.items()
}
#end = time.time()
#print (end-start)
return base_cov_dict, reads_total, reads_mapped, read_cov_dict
def parseCov(infile, set_of_blobs):
if not isfile(infile):
BtLog.error('0', infile)
old_cov_line_re = re.compile(r"^(\S+)\t(\d+\.*\d*)")
base_cov_dict = {}
cov_line_re = re.compile(r"^(\S+)\t(\d+\.*\d*)\t(\d+\.*\d*)")
reads_total = 0
reads_mapped = 0
reads_unmapped = 0
read_cov_dict = {}
seqs_parsed = 0
progress_unit = 1
old_format = 1
with open(infile) as fh:
for line in fh:
if line.startswith("#"):
old_format = 0
if old_format == 0:
if line.startswith('#'):
if line.startswith("## Total Reads"):
reads_total = int(line.split(" = ")[1])
elif line.startswith("## Mapped Reads"):
reads_mapped = int(line.split(" = ")[1])
elif line.startswith("## Unmapped Reads"):
reads_unmapped = int(line.split(" = ")[1])
else:
pass
else:
match = cov_line_re.search(line)
if match:
seqs_parsed += 1
name, read_cov, base_cov = match.group(1), int(
match.group(2)), float(match.group(3))
if name not in set_of_blobs:
print BtLog.warn_d['2'] % (name, infile)
else:
read_cov_dict[name] = read_cov
base_cov_dict[name] = base_cov
else:
match = old_cov_line_re.search(line)
if match:
seqs_parsed += 1
name, base_cov = match.group(1), float(match.group(2))
if name not in set_of_blobs:
print BtLog.warn_d['2'] % (name)
else:
base_cov_dict[name] = base_cov
BtLog.progress(seqs_parsed, progress_unit, len(set_of_blobs))
#BtLog.progress(len(set_of_blobs), progress_unit, len(set_of_blobs))
return base_cov_dict, reads_total, reads_mapped, reads_unmapped, read_cov_dict
def parseDict(infile, key, value):
items = {}
if infile:
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
items = {}
k_idx = int(key)
v_idx = int(value)
for l in fh:
temp = l.rstrip("\n").split()
items[temp[k_idx]] = temp[v_idx]
return items
def readFasta(infile):
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
header, seqs = '', []
for l in fh:
if l[0] == '>':
if header:
yield header, ''.join(seqs)
header, seqs = l[1:-1].split()[0], [] # Header is split at first whitespace
else:
seqs.append(l[:-1])
yield header, ''.join(seqs)
def parseDict(infile, key, value):
items = {}
if infile:
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
items = {}
k_idx = int(key)
v_idx = int(value)
for l in fh:
temp = l.rstrip("\n").split()
items[temp[k_idx]] = temp[v_idx]
return items
def parseCatColour(infile):
catcolour_dict = {}
if infile:
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
for l in fh:
try:
seq_name, category = l.rstrip("\n").split(",")
catcolour_dict[seq_name] = category
except:
BtLog.error('23', infile)
return catcolour_dict
def parseCatColour(infile):
catcolour_dict = {}
if infile:
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
for l in fh:
try:
seq_name, category = l.rstrip("\n").split(",")
catcolour_dict[seq_name] = category
except:
BtLog.error('23', infile)
return catcolour_dict
def readFasta(infile):
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
header, seqs = '', []
for l in fh:
if l[0] == '>':
if header:
yield header, ''.join(seqs)
header, seqs = l[1:-1].split()[0], [] # Header is split at first whitespace
else:
seqs.append(l[:-1])
yield header, ''.join(seqs)
def parseCov(infile, set_of_blobs):
if not isfile(infile):
BtLog.error('0', infile)
old_cov_line_re = re.compile(r"^(\S+)\t(\d+\.*\d*)")
base_cov_dict = {}
cov_line_re = re.compile(r"^(\S+)\t(\d+\.*\d*)\t(\d+\.*\d*)")
reads_total = 0
reads_mapped = 0
reads_unmapped = 0
read_cov_dict = {}
seqs_parsed = 0
progress_unit = 1
old_format = 1
with open(infile) as fh:
for line in fh:
if line.startswith("#"):
old_format = 0
if old_format == 0:
if line.startswith('#'):
if line.startswith("## Total Reads"):
reads_total = int(line.split(" = ")[1])
elif line.startswith("## Mapped Reads"):
reads_mapped = int(line.split(" = ")[1])
elif line.startswith("## Unmapped Reads"):
reads_unmapped = int(line.split(" = ")[1])
else:
pass
else:
match = cov_line_re.search(line)
if match:
seqs_parsed += 1
name, read_cov, base_cov = match.group(1), int(match.group(2)), float(match.group(3))
if name not in set_of_blobs:
print BtLog.warn_d['2'] % (name, infile)
else:
read_cov_dict[name] = read_cov
base_cov_dict[name] = base_cov
else:
match = old_cov_line_re.search(line)
if match:
seqs_parsed += 1
name, base_cov = match.group(1), float(match.group(2))
if name not in set_of_blobs:
print BtLog.warn_d['2'] % (name)
else:
base_cov_dict[name] = base_cov
BtLog.progress(seqs_parsed, progress_unit, len(set_of_blobs))
#BtLog.progress(len(set_of_blobs), progress_unit, len(set_of_blobs))
return base_cov_dict, reads_total, reads_mapped, reads_unmapped, read_cov_dict
def plotBar(self, cov_lib, out_f):
fig, ax_main, ax_group, x_pos_main, x_pos_group = self.setupPlot('readcov')
ax_main_data = {'labels' : [], 'values' : [], 'colours' : [] }
ax_group_data = {'labels' : [], 'values' : [], 'colours' : [] }
reads_total = self.cov_libs_total_reads_dict[cov_lib]
reads_mapped = self.stats['all']['reads_mapped'][cov_lib]
reads_unmapped = reads_total - self.stats['all']['reads_mapped'][cov_lib]
ax_main_data['labels'].append('Unmapped (assembly)')
ax_main_data['values'].append(reads_unmapped/reads_total)
ax_main_data['colours'].append(DGREY)
ax_main_data['labels'].append('Mapped (assembly)')
ax_main_data['values'].append(reads_mapped/reads_total)
ax_main_data['colours'].append(DGREY)
if (self.refcov_dict):
if cov_lib in self.refcov_dict:
reads_total_ref = self.refcov_dict[cov_lib]['reads_total']
reads_mapped_ref = self.refcov_dict[cov_lib]['reads_mapped']
reads_unmapped_ref = reads_total_ref - reads_mapped_ref
ax_main_data['labels'].append('Unmapped (ref)')
ax_main_data['values'].append(reads_unmapped_ref/reads_total_ref)
ax_main_data['colours'].append(DGREY)
ax_main_data['labels'].append('Mapped (ref)')
ax_main_data['values'].append(reads_mapped_ref/reads_total_ref)
ax_main_data['colours'].append(DGREY)
else:
BtLog.error('40', cov_lib)
# mapped plotted groups
for group in self.plot_order:
ax_group_data['labels'].append(group)
ax_group_data['values'].append(self.stats[group]['reads_mapped_perc'][cov_lib])
ax_group_data['colours'].append(self.colours[group])
rect_group = ax_group.bar(x_pos_group, ax_group_data['values'], width = 0.5, tick_label=ax_group_data['labels'], align='center', color = ax_group_data['colours'])
for rect_g in rect_group:
height_g = float(rect_g.get_height())
ax_group.text(rect_g.get_x() + rect_g.get_width()/2., 0.005 + height_g, '{:.2f}%'.format(height_g*100), ha='center', va='bottom', fontsize=LEGEND_FONTSIZE)
rect_main = ax_main.bar(x_pos_main, ax_main_data['values'], width = 0.5, tick_label=ax_main_data['labels'], align='center', color = ax_main_data['colours'])
for rect_m in rect_main:
height_m = float(rect_m.get_height())
ax_main.text(rect_m.get_x() + rect_m.get_width()/2., 0.005 + height_m, '{:.2f}%'.format(height_m*100), ha='center', va='bottom', fontsize=LEGEND_FONTSIZE)
ax_main.set_xticklabels(ax_main_data['labels'], rotation=45, ha='center', fontsize=LEGEND_FONTSIZE)
ax_group.set_xticklabels(ax_group_data['labels'], rotation=45, ha='center', fontsize=LEGEND_FONTSIZE)
#figsuptitle = fig.suptitle(out_f, verticalalignment='top')
out_f = "%s.read_cov.%s" % (out_f, cov_lib)
print BtLog.status_d['8'] % "%s.%s" % (out_f, self.format)
fig.tight_layout()
#fig.savefig("%s.%s" % (out_f, self.format), format=self.format, bbox_extra_artists=(figsuptitle,))
fig.savefig("%s.%s" % (out_f, self.format), format=self.format)
plt.close(fig)
def parseReferenceCov(infile):
refcov_dict = {}
if infile:
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
for l in fh:
try:
cov_lib, reads_total_ref, reads_mapped_ref = l.split(",")
refcov_dict[cov_lib] = {'reads_total' : int(reads_total_ref),
'reads_mapped' : int(reads_mapped_ref)}
except:
BtLog.error('21', infile)
return refcov_dict
def plotBar(self, cov_lib, out_f):
fig, ax_main, ax_group, x_pos_main, x_pos_group = self.setupPlot('readcov')
ax_main_data = {'labels' : [], 'values' : [], 'colours' : [] }
ax_group_data = {'labels' : [], 'values' : [], 'colours' : [] }
reads_total = self.cov_libs_total_reads_dict[cov_lib]
reads_mapped = self.stats['all']['reads_mapped'][cov_lib]
reads_unmapped = reads_total - self.stats['all']['reads_mapped'][cov_lib]
ax_main_data['labels'].append('Unmapped (assembly)')
ax_main_data['values'].append(reads_unmapped/reads_total)
ax_main_data['colours'].append(DGREY)
ax_main_data['labels'].append('Mapped (assembly)')
ax_main_data['values'].append(reads_mapped/reads_total)
ax_main_data['colours'].append(DGREY)
if (self.refcov_dict):
if cov_lib in self.refcov_dict:
reads_total_ref = self.refcov_dict[cov_lib]['reads_total']
reads_mapped_ref = self.refcov_dict[cov_lib]['reads_mapped']
reads_unmapped_ref = reads_total_ref - reads_mapped_ref
ax_main_data['labels'].append('Unmapped (ref)')
ax_main_data['values'].append(reads_unmapped_ref/reads_total_ref)
ax_main_data['colours'].append(DGREY)
ax_main_data['labels'].append('Mapped (ref)')
ax_main_data['values'].append(reads_mapped_ref/reads_total_ref)
ax_main_data['colours'].append(DGREY)
else:
BtLog.error('40', cov_lib)
# mapped plotted groups
for group in self.plot_order:
ax_group_data['labels'].append(group)
ax_group_data['values'].append(self.stats[group]['reads_mapped_perc'][cov_lib])
ax_group_data['colours'].append(self.colours[group])
rect_group = ax_group.bar(x_pos_group, ax_group_data['values'], width = 0.5, tick_label=ax_group_data['labels'], align='center', color = ax_group_data['colours'])
for rect_g in rect_group:
height_g = float(rect_g.get_height())
ax_group.text(rect_g.get_x() + rect_g.get_width()/2., 0.005 + height_g, '{:.2f}%'.format(height_g*100), ha='center', va='bottom', fontsize=LEGEND_FONTSIZE)
rect_main = ax_main.bar(x_pos_main, ax_main_data['values'], width = 0.5, tick_label=ax_main_data['labels'], align='center', color = ax_main_data['colours'])
for rect_m in rect_main:
height_m = float(rect_m.get_height())
ax_main.text(rect_m.get_x() + rect_m.get_width()/2., 0.005 + height_m, '{:.2f}%'.format(height_m*100), ha='center', va='bottom', fontsize=LEGEND_FONTSIZE)
ax_main.set_xticklabels(ax_main_data['labels'], rotation=45, ha='center', fontsize=LEGEND_FONTSIZE)
ax_group.set_xticklabels(ax_group_data['labels'], rotation=45, ha='center', fontsize=LEGEND_FONTSIZE)
#figsuptitle = fig.suptitle(out_f, verticalalignment='top')
out_f = "%s.read_cov.%s" % (out_f, cov_lib)
print BtLog.status_d['8'] % "%s.%s" % (out_f, self.format)
fig.tight_layout()
#fig.savefig("%s.%s" % (out_f, self.format), format=self.format, bbox_extra_artists=(figsuptitle,))
fig.savefig("%s.%s" % (out_f, self.format), format=self.format)
plt.close(fig)
def parseReferenceCov(infile):
refcov_dict = {}
if infile:
if not isfile(infile):
BtLog.error('0', infile)
with open(infile) as fh:
for l in fh:
try:
cov_lib, reads_total_ref, reads_mapped_ref = l.split(",")
refcov_dict[cov_lib] = {'reads_total' : int(reads_total_ref),
'reads_mapped' : int(reads_mapped_ref)}
except:
BtLog.error('21', infile)
return refcov_dict
def parseCmdLabels(labels):
label_d = {}
name, groups = '', ''
if labels:
try:
for label in labels:
name, groups = str(label).split("=")
if "," in groups:
for group in groups.split(","):
label_d[group] = name
else:
label_d[groups] = name
except:
BtLog.error('17', labels)
return label_d
def parseCmdLabels(labels):
label_d = {}
name, groups = '', ''
if labels:
try:
for label in labels:
name, groups = str(label).split("=")
if "," in groups:
for group in groups.split(","):
label_d[group] = name
else:
label_d[groups] = name
except:
BtLog.error('17', labels)
return label_d
def parseSam(infile, set_of_blobs, no_base_cov_flag):
if not isfile(infile):
BtLog.error('0', infile)
base_cov_dict = {blob: [] for blob in set_of_blobs}
read_cov_dict = {blob: 0 for blob in set_of_blobs}
cigar_match_re = re.compile(
r"(\d+)M|X|=") # only gets digits before M,X,='s
reads_total = 0
reads_mapped = 0
if not (no_base_cov_flag):
with open(infile) as fh:
for line in fh:
if line.startswith("@"):
pass
else:
reads_total += 1
match = line.split()
if not match[2] == '*':
reads_mapped += 1
try:
base_cov_dict[match[2]].append(
sum([
int(matching) for matching in
cigar_match_re.findall(match[5])
]))
read_cov_dict[match[2]] += 1
except:
print BtLog.warn_d['2'] % (match[2])
else:
with open(infile) as fh:
for line in fh:
if line.startswith("@"):
pass
else:
reads_total += 1
match = line.split()
if not match[2] == '*':
reads_mapped += 1
try:
read_cov_dict[match[2]] += 1
except:
print BtLog.warn_d['2'] % (match[2])
base_cov_dict = {
seq_name: sum(base_covs)
for seq_name, base_covs in base_cov_dict.items()
}
return base_cov_dict, reads_total, reads_mapped, read_cov_dict
def parseBam(infile, set_of_blobs, no_base_cov_flag):
'''
checkBam returns reads_total and reads_mapped
base_cov_dict is list of coverages for each contigs, since list appending should be faster
'''
if not isfile(infile):
BtLog.error('0', infile)
reads_total, reads_mapped = checkBam(infile)
progress_unit = int(reads_mapped/1000)
base_cov_dict = {blob : [] for blob in set_of_blobs}
#base_cov_dict = {blob : 0 for blob in set_of_blobs}
read_cov_dict = {blob : 0 for blob in set_of_blobs}
cigar_match_re = re.compile(r"(\d+)M|X|=") # only gets digits before M,X,='s
# execute samtools to get only mapped reads (no optial duplicates, no 2nd-ary alignment)
command = "samtools view -F 1024 -F 4 -F 256 " + infile
seen_reads = 0
#import time
#start = time.time()
if not (no_base_cov_flag):
for line in runCmd(command=command):
seen_reads += 1
match = line.split()
try:
base_cov_dict[match[2]].append(sum([int(matching) for matching in cigar_match_re.findall(match[5])]))
#base_cov_dict[match[2]] += sum([int(matching) for matching in cigar_match_re.findall(match[5])])
read_cov_dict[match[2]] += 1
except:
print BtLog.warn_d['2'] % (match[2])
BtLog.progress(seen_reads, progress_unit, reads_mapped)
else:
for line in runCmd(command=command):
seen_reads += 1
match = line.split()
try:
read_cov_dict[match[2]] += 1
except:
print BtLog.warn_d['2'] % (match[2])
BtLog.progress(seen_reads, progress_unit, reads_mapped)
if not int(reads_mapped) == int(seen_reads):
print BtLog.warn_d['3'] % (reads_mapped, seen_reads)
base_cov_dict = {seq_name: sum(base_covs) for seq_name, base_covs in base_cov_dict.items()}
#end = time.time()
#print (end-start)
return base_cov_dict, reads_total, reads_mapped, read_cov_dict
def checkCas(infile):
print BtLog.status_d['12']
if not isfile(infile):
BtLog.error('0', infile)
if not (which('clc_mapping_info')):
BtLog.error('20')
seqs_total_re = re.compile(r"\s+Contigs\s+(\d+)")
reads_total_re = re.compile(r"\s+Reads\s+(\d+)")
reads_mapping_re = re.compile(r"\s+Mapped reads\s+(\d+)\s+(\d+.\d+)\s+\%")
seqs_total, reads_total, reads_mapping, mapping_rate = 0, 0, 0, 0.0
output = ''
command = "clc_mapping_info -s " + infile
for line in runCmd(command=command):
output += line
seqs_total = int(seqs_total_re.search(output).group(1))
reads_mapped = int(reads_mapping_re.search(output).group(1))
reads_total = int(reads_total_re.search(output).group(1))
print BtLog.status_d['11'] % ('{:,}'.format(reads_mapped), '{:,}'.format(reads_total), '{0:.1%}'.format(reads_mapped/reads_total))
return seqs_total, reads_total, reads_mapped
def checkCas(infile):
print BtLog.status_d['12']
if not isfile(infile):
BtLog.error('0', infile)
if not (which('clc_mapping_info')):
BtLog.error('20')
seqs_total_re = re.compile(r"\s+Contigs\s+(\d+)")
reads_total_re = re.compile(r"\s+Reads\s+(\d+)")
reads_mapping_re = re.compile(r"\s+Mapped reads\s+(\d+)\s+(\d+.\d+)\s+\%")
seqs_total, reads_total, reads_mapping, mapping_rate = 0, 0, 0, 0.0
output = ''
command = "clc_mapping_info -s " + infile
for line in runCmd(command=command):
output += line
seqs_total = int(seqs_total_re.search(output).group(1))
reads_mapped = int(reads_mapping_re.search(output).group(1))
reads_total = int(reads_total_re.search(output).group(1))
print BtLog.status_d['11'] % ('{:,}'.format(reads_mapped), '{:,}'.format(reads_total), '{0:.1%}'.format(reads_mapped/reads_total))
return seqs_total, reads_total, reads_mapped
def main():
args = docopt(__doc__)
fasta_f = args['--infile']
bam_fs = args['--bam']
cas_fs = args['--cas']
sam_fs = args['--sam']
prefix = args['--output']
no_base_cov_flag = args['--no_base_cov']
# Make covLibs
cov_libs = [BtCore.CovLibObj('bam' + str(idx), 'bam', lib_f) for idx, lib_f in enumerate(bam_fs)] + \
[BtCore.CovLibObj('sam' + str(idx), 'sam', lib_f) for idx, lib_f in enumerate(sam_fs)] + \
[BtCore.CovLibObj('cas' + str(idx), 'cas', lib_f) for idx, lib_f in enumerate(cas_fs)]
if not (cov_libs):
BtLog.error('31')
blobDb = BtCore.BlobDb('cov')
blobDb.version = blobtools.__version__
blobDb.parseFasta(fasta_f, None)
blobDb.parseCoverage(covLibObjs=cov_libs, no_base_cov=no_base_cov_flag)
def checkBam(infile):
print BtLog.status_d['10']
if not isfile(infile):
BtLog.error('0', infile)
if not which('samtools'):
BtLog.error('7')
reads_mapped_re = re.compile(r"(\d+)\s\+\s\d+\smapped")
reads_secondary_re = re.compile(r"(\d+)\s\+\s\d+\ssecondary")
reads_supplementary_re = re.compile(r"(\d+)\s\+\s\d+\ssupplementary")
reads_total_re = re.compile(r"(\d+)\s\+\s\d+\sin total")
reads_total, reads_mapped = 0, 0
output = ''
command = "samtools flagstat " + infile
for line in runCmd(command=command):
output += line
reads_mapped = int(reads_mapped_re.search(output).group(1))
reads_secondary = int(reads_secondary_re.search(output).group(1))
reads_supplementary = int(reads_supplementary_re.search(output).group(1))
reads_mapped = reads_mapped - reads_secondary - reads_secondary
reads_total = int(reads_total_re.search(output).group(
1)) - reads_secondary - reads_supplementary
# check whether there are reads in BAM
if not reads_total or not reads_mapped:
BtLog.error('29' % infile)
print BtLog.status_d['11'] % ('{:,}'.format(reads_mapped), \
'{:,}'.format(reads_total), '{0:.1%}'.format(reads_mapped/reads_total))
return reads_total, reads_mapped
def readTax(infile, set_of_blobs):
'''
If more fields need to be parsed:
- change hit_line_re
- catch matches in variables
- add as key-value pairs to hitDict
'''
if not isfile(infile):
BtLog.error('0', infile)
hit_line_re = re.compile(r"^(\S+)\s+(\d+)[\;?\d+]*\s+(\d+\.*\d*)"
) # TEST TEST , if not split it afterwards
with open(infile) as fh:
for line in fh:
match = hit_line_re.search(line)
if match:
hitDict = {
'name': match.group(1),
'taxId': match.group(
2
), # string because if int, conversion is a nightmare ...
'score': float(match.group(3))
}
if hitDict['name'] not in set_of_blobs:
BtLog.error('19', hitDict['name'], infile)
if hitDict['taxId'] == 'N/A':
BtLog.error('22', infile)
yield hitDict
def checkBam(infile):
print BtLog.status_d['10']
if not isfile(infile):
BtLog.error('0', infile)
if not which('samtools'):
BtLog.error('7')
reads_mapped_re = re.compile(r"(\d+)\s\+\s\d+\smapped")
reads_secondary_re = re.compile(r"(\d+)\s\+\s\d+\ssecondary")
reads_supplementary_re = re.compile(r"(\d+)\s\+\s\d+\ssupplementary")
reads_total_re = re.compile(r"(\d+)\s\+\s\d+\sin total")
reads_total, reads_mapped = 0, 0
output = ''
command = "samtools flagstat " + infile
for line in runCmd(command=command):
output += line
reads_mapped = int(reads_mapped_re.search(output).group(1))
reads_secondary = int(reads_secondary_re.search(output).group(1))
reads_supplementary = int(reads_supplementary_re.search(output).group(1))
reads_mapped = reads_mapped - reads_secondary - reads_secondary
reads_total = int(reads_total_re.search(output).group(1)) - reads_secondary - reads_supplementary
# check whether there are reads in BAM
if not reads_total or not reads_mapped:
BtLog.error('29' % infile)
print BtLog.status_d['11'] % ('{:,}'.format(reads_mapped), \
'{:,}'.format(reads_total), '{0:.1%}'.format(reads_mapped/reads_total))
return reads_total, reads_mapped
def parseSam(infile, set_of_blobs, no_base_cov_flag):
if not isfile(infile):
BtLog.error('0', infile)
base_cov_dict = {blob : [] for blob in set_of_blobs}
read_cov_dict = {blob : 0 for blob in set_of_blobs}
cigar_match_re = re.compile(r"(\d+)M|X|=") # only gets digits before M,X,='s
reads_total = 0
reads_mapped = 0
if not (no_base_cov_flag):
with open(infile) as fh:
for line in fh:
if line.startswith("@"):
pass
else:
reads_total += 1
match = line.split()
if not match[2] == '*':
reads_mapped += 1
try:
base_cov_dict[match[2]].append(sum([int(matching) for matching in cigar_match_re.findall(match[5])]))
read_cov_dict[match[2]] += 1
except:
print BtLog.warn_d['2'] % (match[2])
else:
with open(infile) as fh:
for line in fh:
if line.startswith("@"):
pass
else:
reads_total += 1
match = line.split()
if not match[2] == '*':
reads_mapped += 1
try:
read_cov_dict[match[2]] += 1
except:
print BtLog.warn_d['2'] % (match[2])
base_cov_dict = {seq_name: sum(base_covs) for seq_name, base_covs in base_cov_dict.items()}
return base_cov_dict, reads_total, reads_mapped, read_cov_dict
def main():
args = docopt(__doc__)
bam_f = args['--bam']
include_f = args['--include']
exclude_f = args['--exclude']
out_prefix = args['--out']
gzip = args['--gzip']
do_sort = args['--sort']
keep_sorted = args['--keep']
sort_threads = int(args['--threads'])
print BtLog.status_d['22'] % bam_f
out_f = BtIO.getOutFile(bam_f, out_prefix, None)
if include_f and exclude_f:
print BtLog.error('43')
elif include_f:
sequence_list = BtIO.parseList(include_f)
BtIO.parseBamForFilter(bam_f, out_f, sequence_list, None, gzip, do_sort, keep_sorted, sort_threads)
elif exclude_f:
sequence_list = BtIO.parseList(exclude_f)
BtIO.parseBamForFilter(bam_f, out_f, None, sequence_list, gzip, do_sort, keep_sorted, sort_threads)
else:
BtIO.parseBamForFilter(bam_f, out_f, None, None, gzip, do_sort, keep_sorted, sort_threads)
def readTax(infile, set_of_blobs):
'''
If more fields need to be parsed:
- change hit_line_re
- catch matches in variables
- add as key-value pairs to hitDict
'''
if not isfile(infile):
BtLog.error('0', infile)
hit_line_re = re.compile(r"^(\S+)\s+(\d+)[\;?\d+]*\s+(\d+\.*\d*)") # TEST TEST , if not split it afterwards
with open(infile) as fh:
for line in fh:
match = hit_line_re.search(line)
if match:
hitDict = {
'name' : match.group(1),
'taxId' : match.group(2), # string because if int, conversion is a nightmare ...
'score' : float(match.group(3))
}
if hitDict['name'] not in set_of_blobs:
BtLog.error('19', hitDict['name'], infile)
if hitDict['taxId'] == 'N/A':
BtLog.error('22', infile)
yield hitDict
def getPlotData(self, rank, min_length, hide_nohits, taxrule, c_index, catcolour_dict):
data_dict = {}
read_cov_dict = {}
max_cov = 0.0
min_cov = 1000.0
cov_lib_dict = self.covLibs
cov_lib_names_l = self.covLibs.keys() # does not include cov_sum
if len(cov_lib_names_l) > 1:
# more than one cov_lib, cov_sum_lib has to be created
cov_lib_dict['covsum'] = CovLibObj('covsum', 'covsum', 'Sum of cov in %s' % basename(self.title)).__dict__ # ugly
cov_lib_dict['covsum']['reads_total'] = sum([self.covLibs[x]['reads_total'] for x in self.covLibs])
cov_lib_dict['covsum']['reads_mapped'] = sum([self.covLibs[x]['reads_mapped'] for x in self.covLibs])
cov_lib_dict['covsum']['cov_sum'] = sum([self.covLibs[x]['cov_sum'] for x in self.covLibs])
cov_lib_dict['covsum']['mean_cov'] = cov_lib_dict['covsum']['cov_sum']/self.seqs
for blob in self.dict_of_blobs.values():
name, gc, length, group = blob['name'], blob['gc'], blob['length'], ''
if (catcolour_dict): # annotation with categories specified in catcolour
group = str(catcolour_dict[name])
elif (c_index): # annotation with c_index instead of taxonomic group
if taxrule not in self.taxrules:
BtLog.error('11', taxrule, self.taxrules)
else:
group = str(blob['taxonomy'][taxrule][rank]['c_index'])
else: # annotation with taxonomic group
if not (taxrule) or taxrule not in self.taxrules:
BtLog.warn_d['9'] % (taxrule, self.taxrules)
if taxrule in blob['taxonomy']:
group = str(blob['taxonomy'][taxrule][rank]['tax'])
if not group in data_dict:
data_dict[group] = {
'name' : [],
'length' : [],
'gc' : [],
'covs' : {covLib : [] for covLib in cov_lib_dict.keys()}, # includes cov_sum if it exists
'reads_mapped' : {covLib : 0 for covLib in cov_lib_dict.keys()}, # includes cov_sum if it exists
'count' : 0,
'count_hidden' : 0,
'count_visible' : 0,
'span': 0,
'span_hidden' : 0,
'span_visible' : 0,
}
data_dict[group]['count'] = data_dict[group].get('count', 0) + 1
data_dict[group]['span'] = data_dict[group].get('span', 0) + int(length)
if ((hide_nohits) and group == 'no-hit') or length < min_length: # hidden
data_dict[group]['count_hidden'] = data_dict[group].get('count_hidden', 0) + 1
data_dict[group]['span_hidden'] = data_dict[group].get('span_hidden', 0) + int(length)
else: # visible
data_dict[group]['count_visible'] = data_dict[group].get('count_visible', 0) + 1
data_dict[group]['span_visible'] = data_dict[group].get('span_visible', 0) + int(length)
data_dict[group]['name'].append(name)
data_dict[group]['length'].append(length)
data_dict[group]['gc'].append(gc)
cov_sum = 0.0
reads_mapped_sum = 0
for cov_lib in sorted(cov_lib_names_l):
cov = float(blob['covs'][cov_lib])
if cov < 0.02:
cov = 0.02
if cov < min_cov:
min_cov = cov
# increase max_cov
if cov > max_cov:
max_cov = cov
# add cov of blob to group
data_dict[group]['covs'][cov_lib].append(cov)
cov_sum += cov
# add readcov
if cov_lib in blob['read_cov']:
reads_mapped = blob['read_cov'][cov_lib]
data_dict[group]['reads_mapped'][cov_lib] += reads_mapped
reads_mapped_sum += reads_mapped
if len(cov_lib_names_l) > 1:
if cov_sum < 0.02 :
cov_sum = 0.02
data_dict[group]['covs']['covsum'].append(cov_sum)
if cov_sum > max_cov:
max_cov = cov_sum
if (reads_mapped_sum):
data_dict[group]['reads_mapped']['covsum'] += reads_mapped_sum
return data_dict, min_cov, max_cov, cov_lib_dict
def plotScatter(self, cov_lib, info_flag, out_f):
fig, axScatter, axHistx, axHisty, axLegend, top_bins, right_bins = self.setupPlot(self.plot)
# empty handles for big legend
legend_handles = []
legend_labels = []
# marker size scaled by biggest blob (size in points^2)
max_length = max(array(self.stats['all']['length'])) # length of biggest blob
max_marker_size = 12500 # marker size for biggest blob, i.e. area of 12500^2 pixel
for idx, group in enumerate(self.plot_order):
idx += 1
lw, alpha = 0.5, 0.8
if group == 'no-hit':
alpha = 0.5
group_length_array = array(self.stats[group]['length'])
if len(group_length_array) > 0 and group not in self.exclude_groups:
colour = self.colours[group]
group_x_array = ''
group_y_array = ''
if self.plot == 'blobplot':
group_x_array = array(self.stats[group]['gc'])
group_y_array = array(self.stats[group]['covs'][cov_lib])
elif self.plot == 'covplot':
group_x_array = array(self.stats[group]['covs'][cov_lib])
group_y_array = array([self.cov_y_dict.get(name, 0.02) for name in self.stats[group]['name']])
else:
BtLog.error('34', self.plot)
marker_size_array = []
if (self.ignore_contig_length): # no scaling
if group == "no-hit":
s = 20
else:
s = 100
marker_size_array = [s for length in group_length_array]
else: # scaling by max_length
marker_size_array = [(length/max_length)*max_marker_size for length in group_length_array]
# generate label for legend
group_span_in_mb = round(self.stats[group]['span_visible']/1000000, 2)
group_number_of_seqs = self.stats[group]['count_visible']
group_n50 = self.stats[group]['n50']
fmt_seqs = "{:,}".format(group_number_of_seqs)
fmt_span = "{:,}".format(group_span_in_mb)
fmt_n50 = "{:,}".format(group_n50)
label = "%s (%s;%sMB;%snt)" % (group, fmt_seqs, fmt_span, fmt_n50)
if (info_flag):
print BtLog.info_d['0'] % (group, fmt_seqs, fmt_span, fmt_n50)
legend_handles.append(Line2D([0], [0], linewidth = 0.5, linestyle="none", marker="o", alpha=1, markersize=24, markerfacecolor=colour))
legend_labels.append(label)
weights_array = None
if self.hist_type == "span":
weights_array = group_length_array/1000
axHistx.hist(group_x_array, weights=weights_array, color = colour, bins = top_bins, histtype='step', lw = 3)
axHisty.hist(group_y_array, weights=weights_array, color = colour, bins = right_bins, histtype='step', orientation='horizontal', lw = 3)
axScatter.scatter(group_x_array, group_y_array, color = colour, s = marker_size_array, lw = lw, alpha=alpha, edgecolor=BLACK, label=label)
axLegend.axis('off')
if (self.multiplot):
fig_m, axScatter_m, axHistx_m, axHisty_m, axLegend_m, top_bins, right_bins = self.setupPlot(self.plot)
legend_handles_m = []
legend_labels_m = []
legend_handles_m.append(Line2D([0], [0], linewidth = 0.5, linestyle="none", marker="o", alpha=1, markersize=24, markerfacecolor=colour))
legend_labels_m.append(label)
axHistx_m.hist(group_x_array, weights=weights_array, color = colour, bins = top_bins, histtype='step', lw = 3)
axHisty_m.hist(group_y_array, weights=weights_array, color = colour, bins = right_bins, histtype='step', orientation='horizontal', lw = 3)
axScatter_m.scatter(group_x_array, group_y_array, color = colour, s = marker_size_array, lw = lw, alpha=alpha, edgecolor=BLACK, label=label)
axLegend_m.axis('off')
axLegend_m.legend(legend_handles_m, legend_labels_m, loc=6, numpoints=1, fontsize=LEGEND_FONTSIZE, frameon=True)
plot_ref_legend(axScatter_m, max_length, max_marker_size, self.ignore_contig_length)
m_out_f = "%s.%s.%s.%s" % (out_f, cov_lib, idx, group.replace("/", "_").replace(" ", "_"))
fig_m = plot_legend(fig_m, axLegend_m, m_out_f, self.legend_flag, self.format, self.cumulative_flag)
print BtLog.status_d['8'] % "%s.%s" % (m_out_f, self.format)
fig_m.savefig("%s.%s" % (m_out_f, self.format), format=self.format)
plt.close(fig_m)
elif (self.cumulative_flag):
axLegend.legend(legend_handles, legend_labels, loc=6, numpoints=1, fontsize=LEGEND_FONTSIZE, frameon=True)
plot_ref_legend(axScatter, max_length, max_marker_size, self.ignore_contig_length)
m_out_f = "%s.%s.%s.%s" % (out_f, cov_lib, idx, group.replace("/", "_").replace(" ", "_"))
fig.add_axes(axLegend)
fig = plot_legend(fig, axLegend, m_out_f, self.legend_flag, self.format, self.cumulative_flag)
if not (self.no_title):
fig.suptitle(out_f, fontsize=35, verticalalignment='top')
print BtLog.status_d['8'] % "%s.%s" % (m_out_f, self.format)
fig.savefig("%s.%s" % (m_out_f, self.format), format=self.format)
else:
pass
plot_ref_legend(axScatter, max_length, max_marker_size, self.ignore_contig_length)
axLegend.legend(legend_handles, legend_labels, numpoints=1, fontsize=LEGEND_FONTSIZE, frameon=True, loc=6 )
out_f = "%s.%s" % (out_f, cov_lib)
fig.add_axes(axLegend)
fig = plot_legend(fig, axLegend, out_f, self.legend_flag, self.format, self.cumulative_flag)
if not (self.no_title):
fig.suptitle(out_f, fontsize=35, verticalalignment='top')
print BtLog.status_d['8'] % "%s.%s" % (out_f, self.format)
fig.savefig("%s.%s" % (out_f, self.format), format=self.format)
plt.close(fig)
def parseNodesDB(**kwargs):
'''
Parsing names.dmp and nodes.dmp into the 'nodes_db' dict of dicts that
gets JSON'ed into blobtools/data/nodes_db.json if this file
does not exist. Nodes_db.json is used if neither "--names" and "--nodes"
nor "--db" is specified.
'''
nodesDB = {}
names_f = kwargs['names']
nodes_f = kwargs['nodes']
nodesDB_f = kwargs['nodesDB']
nodesDB_default = kwargs['nodesDBdefault']
if (nodes_f and names_f):
if not isfile(names_f):
BtLog.error('0', names_f)
if not isfile(nodes_f):
BtLog.error('0', nodes_f)
print BtLog.status_d['3'] % (nodes_f, names_f)
try:
nodesDB = readNamesNodes(names_f, nodes_f)
except:
BtLog.error('3', nodes_f, names_f)
elif (nodesDB_f):
if not isfile(nodesDB_f):
BtLog.error('0', nodesDB_f)
print BtLog.status_d['4'] % (nodesDB_f)
try:
nodesDB = readNodesDB(nodesDB_f)
except:
BtLog.error('27', nodesDB_f)
elif (nodesDB_default):
if not isfile(nodesDB_default):
BtLog.error('28')
print BtLog.status_d['4'] % (nodesDB_default)
try:
nodesDB = readNodesDB(nodesDB_default)
except:
BtLog.error('27', nodesDB_default)
nodesDB_f = nodesDB_default
# Write nodesDB if not available
if not isfile(nodesDB_default):
writeNodesDB(nodesDB, nodesDB_default)
return nodesDB, nodesDB_f
def plotScatter(self, cov_lib, info_flag, out_f):
fig, axScatter, axHistx, axHisty, axLegend, top_bins, right_bins = self.setupPlot(self.plot)
# empty handles for big legend
legend_handles = []
legend_labels = []
# marker size scaled by biggest blob (size in points^2)
max_length = max(array(self.stats['all']['length'])) # length of biggest blob
max_marker_size = 12500 # marker size for biggest blob, i.e. area of 12500^2 pixel
for idx, group in enumerate(self.plot_order):
idx += 1
lw, alpha = 0.5, 0.8
if group == 'no-hit':
alpha = 0.5
group_length_array = array(self.stats[group]['length'])
if len(group_length_array) > 0 and group not in self.exclude_groups:
colour = self.colours[group]
group_x_array = ''
group_y_array = ''
if self.plot == 'blobplot':
group_x_array = array(self.stats[group]['gc'])
group_y_array = array(self.stats[group]['covs'][cov_lib])
elif self.plot == 'covplot':
group_x_array = array(self.stats[group]['covs'][cov_lib])
group_y_array = array([self.cov_y_dict.get(name, 0.02) for name in self.stats[group]['name']])
else:
BtLog.error('34', self.plot)
marker_size_array = []
if (self.ignore_contig_length): # no scaling
if group == "no-hit":
s = 20
else:
s = 100
marker_size_array = [s for length in group_length_array]
else: # scaling by max_length
marker_size_array = [(length/max_length)*max_marker_size for length in group_length_array]
# generate label for legend
group_span_in_mb = round(self.stats[group]['span_visible']/1000000, 2)
group_number_of_seqs = self.stats[group]['count_visible']
group_n50 = self.stats[group]['n50']
fmt_seqs = "{:,}".format(group_number_of_seqs)
fmt_span = "{:,}".format(group_span_in_mb)
fmt_n50 = "{:,}".format(group_n50)
label = "%s (%s;%sMB;%snt)" % (group, fmt_seqs, fmt_span, fmt_n50)
if (info_flag):
print BtLog.info_d['0'] % (group, fmt_seqs, fmt_span, fmt_n50)
legend_handles.append(Line2D([0], [0], linewidth = 0.5, linestyle="none", marker="o", alpha=1, markersize=24, markeredgecolor=BLACK, markerfacecolor=colour))
legend_labels.append(label)
weights_array = None
if self.hist_type == "span":
weights_array = group_length_array/1000
axHistx.hist(group_x_array, weights=weights_array, color = colour, bins = top_bins, histtype='step', lw = 3)
axHisty.hist(group_y_array, weights=weights_array, color = colour, bins = right_bins, histtype='step', orientation='horizontal', lw = 3)
axScatter.scatter(group_x_array, group_y_array, color = colour, s = marker_size_array, lw = lw, alpha=alpha, edgecolor=BLACK, label=label)
axLegend.axis('off')
if (self.multiplot):
fig_m, axScatter_m, axHistx_m, axHisty_m, axLegend_m, top_bins, right_bins = self.setupPlot(self.plot)
legend_handles_m = []
legend_labels_m = []
legend_handles_m.append(Line2D([0], [0], linewidth = 0.5, linestyle="none", marker="o", alpha=1, markersize=24, markeredgecolor=BLACK, markerfacecolor=colour))
legend_labels_m.append(label)
axHistx_m.hist(group_x_array, weights=weights_array, color = colour, bins = top_bins, histtype='step', lw = 3)
axHisty_m.hist(group_y_array, weights=weights_array, color = colour, bins = right_bins, histtype='step', orientation='horizontal', lw = 3)
axScatter_m.scatter(group_x_array, group_y_array, color = colour, s = marker_size_array, lw = lw, alpha=alpha, edgecolor=BLACK, label=label)
axLegend_m.axis('off')
axLegend_m.legend(legend_handles_m, legend_labels_m, loc=6, numpoints=1, fontsize=LEGEND_FONTSIZE, frameon=True)
plot_ref_legend(axScatter_m, max_length, max_marker_size, self.ignore_contig_length)
m_out_f = "%s.%s.%s.%s" % (out_f, cov_lib, idx, group.replace("/", "_").replace(" ", "_"))
fig_m = plot_legend(fig_m, axLegend_m, m_out_f, self.legend_flag, self.format, self.cumulative_flag)
print BtLog.status_d['8'] % "%s.%s" % (m_out_f, self.format)
fig_m.savefig("%s.%s" % (m_out_f, self.format), format=self.format)
plt.close(fig_m)
elif (self.cumulative_flag):
axLegend.legend(legend_handles, legend_labels, loc=6, numpoints=1, fontsize=LEGEND_FONTSIZE, frameon=True)
plot_ref_legend(axScatter, max_length, max_marker_size, self.ignore_contig_length)
m_out_f = "%s.%s.%s.%s" % (out_f, cov_lib, idx, group.replace("/", "_").replace(" ", "_"))
fig.add_axes(axLegend)
fig = plot_legend(fig, axLegend, m_out_f, self.legend_flag, self.format, self.cumulative_flag)
if not (self.no_title):
fig.suptitle(out_f, fontsize=35, verticalalignment='top')
print BtLog.status_d['8'] % "%s.%s" % (m_out_f, self.format)
fig.savefig("%s.%s" % (m_out_f, self.format), format=self.format)
else:
pass
plot_ref_legend(axScatter, max_length, max_marker_size, self.ignore_contig_length)
axLegend.legend(legend_handles, legend_labels, numpoints=1, fontsize=LEGEND_FONTSIZE, frameon=True, loc=6 )
out_f = "%s.%s" % (out_f, cov_lib)
fig.add_axes(axLegend)
fig = plot_legend(fig, axLegend, out_f, self.legend_flag, self.format, self.cumulative_flag)
if not (self.no_title):
fig.suptitle(out_f, fontsize=35, verticalalignment='top')
print BtLog.status_d['8'] % "%s.%s" % (out_f, self.format)
fig.savefig("%s.%s" % (out_f, self.format), format=self.format)
plt.close(fig)
def main():
#main_dir = dirname(__file__)
args = docopt(__doc__)
fasta_f = args['--infile']
fasta_type = args['--type']
sam_fs = args['--sam']
bam_fs = args['--bam']
cov_fs = args['--cov']
cas_fs = args['--cas']
hit_fs = args['--hitsfile']
prefix = args['--out']
nodesDB_f = args['--db']
names_f = args['--names']
nodes_f = args['--nodes']
taxrules = args['--taxrule']
min_bitscore_diff = float(args['--min_diff'])
tax_collision_random = args['--tax_collision_random']
title = args['--title']
# outfile
out_f = BtIO.getOutFile("blobDB", prefix, "json")
if not (title):
title = out_f
# coverage
if not (fasta_type
) and not bam_fs and not sam_fs and not cov_fs and not cas_fs:
BtLog.error('1')
cov_libs = [BtCore.CovLibObj('bam' + str(idx), 'bam', lib_f) for idx, lib_f in enumerate(bam_fs)] + \
[BtCore.CovLibObj('sam' + str(idx), 'sam', lib_f) for idx, lib_f in enumerate(sam_fs)] + \
[BtCore.CovLibObj('cas' + str(idx), 'cas', lib_f) for idx, lib_f in enumerate(cas_fs)] + \
[BtCore.CovLibObj('cov' + str(idx), 'cov', lib_f) for idx, lib_f in enumerate(cov_fs)]
# taxonomy
hit_libs = [
BtCore.HitLibObj('tax' + str(idx), 'tax', lib_f)
for idx, lib_f in enumerate(hit_fs)
]
# Create BlobDB object
blobDb = BtCore.BlobDb(title)
blobDb.version = blobtools.__version__
# Parse FASTA
blobDb.parseFasta(fasta_f, fasta_type)
# Parse nodesDB OR names.dmp, nodes.dmp
nodesDB_default = join(blobtools.DATADIR, "nodesDB.txt")
nodesDB, nodesDB_f = BtIO.parseNodesDB(nodes=nodes_f,
names=names_f,
nodesDB=nodesDB_f,
nodesDBdefault=nodesDB_default)
blobDb.nodesDB_f = nodesDB_f
# Parse similarity hits
if (hit_libs):
blobDb.parseHits(hit_libs)
blobDb.computeTaxonomy(taxrules, nodesDB, min_bitscore_diff,
tax_collision_random)
else:
print BtLog.warn_d['0']
# Parse coverage
blobDb.parseCoverage(covLibObjs=cov_libs, no_base_cov=None)
# Generating BlobDB and writing to file
print BtLog.status_d['7'] % out_f
BtIO.writeJson(blobDb.dump(), out_f)
def parseNodesDB(**kwargs):
'''
Parsing names.dmp and nodes.dmp into the 'nodes_db' dict of dicts that
gets JSON'ed into blobtools/data/nodes_db.json if this file
does not exist. Nodes_db.json is used if neither "--names" and "--nodes"
nor "--db" is specified.
'''
nodesDB = {}
names_f = kwargs['names']
nodes_f = kwargs['nodes']
nodesDB_f = kwargs['nodesDB']
nodesDB_default = kwargs['nodesDBdefault']
if (nodes_f and names_f):
if not isfile(names_f):
BtLog.error('0', names_f)
if not isfile(nodes_f):
BtLog.error('0', nodes_f)
print BtLog.status_d['3'] % (nodes_f, names_f)
try:
nodesDB = readNamesNodes(names_f, nodes_f)
except:
BtLog.error('3', nodes_f, names_f)
elif (nodesDB_f):
if not isfile(nodesDB_f):
BtLog.error('0', nodesDB_f)
print BtLog.status_d['4'] % (nodesDB_f)
try:
nodesDB = readNodesDB(nodesDB_f)
except:
BtLog.error('27', nodesDB_f)
elif (nodesDB_default):
if not isfile(nodesDB_default):
BtLog.error('28')
print BtLog.status_d['4'] % (nodesDB_default)
try:
nodesDB = readNodesDB(nodesDB_default)
except:
BtLog.error('27', nodesDB_default)
nodesDB_f = nodesDB_default
# Write nodesDB if not available
if not isfile(nodesDB_default):
writeNodesDB(nodesDB, nodesDB_default)
return nodesDB, nodesDB_f
def main():
args = docopt(__doc__)
blast_f = args['--blast']
diamond_f = args['--diamond']
uniref_f = args['--uniref']
rnacentral_f = args['--rnacentral']
swissprot_f = args['--swissprot']
taxid = args['--taxid']
force = args['--force']
prefix = args['--out']
out_f, hit_f, map_f, taxid_d = None, None, None, {}
# Check if blast_f OR diamond_f is speciefied
if not (bool(blast_f) + bool(diamond_f) == 1):
BtLog.error('26')
elif blast_f:
hit_f = blast_f
elif diamond_f:
hit_f = diamond_f
else:
pass
# Check if taxID or Mapping file is supplied
if (taxid):
try:
taxid = int(taxid)
except TypeError:
BtLog.error('26')
out_f = BtIO.getOutFile(hit_f, prefix, "tax_%s.out" % taxid)
taxid_d = defaultdict(lambda: taxid)
elif (bool(uniref_f) + bool(rnacentral_f) + bool(swissprot_f) == 1):
if uniref_f:
print BtLog.status_d['1'] % ("ID-to-taxID Mapping file", uniref_f)
taxid_d = BtIO.parseDict(uniref_f, 0, 1)
out_f = BtIO.getOutFile(hit_f, prefix, "uniref.out")
map_f = uniref_f
elif rnacentral_f:
print BtLog.status_d['1'] % ("ID-to-taxID Mapping file",
rnacentral_f)
taxid_d = BtIO.parseDict(rnacentral_f, 0, 3)
out_f = BtIO.getOutFile(hit_f, prefix, "rnacentral.out")
map_f = rnacentral_f
elif swissprot_f:
print BtLog.status_d['1'] % ("ID-to-taxID Mapping file",
swissprot_f)
taxid_d = BtIO.parseDict(swissprot_f, 0, 1)
out_f = BtIO.getOutFile(hit_f, prefix, "swissprot.out")
map_f = swissprot_f
else:
pass
else:
BtLog.error('41')
output = []
print BtLog.status_d['1'] % ("hits file", hit_f)
with open(hit_f) as fh:
for idx, l in enumerate(fh):
query_id, bitscore, tax_id, subject_id, rest = None, None, None, None, None
line = l.rstrip("\n").split()
query_id = line[0]
if blast_f:
bitscore = line[2]
tax_id = line[1]
subject_id = line[4]
rest = "\t".join(line[2:])
elif diamond_f:
bitscore = line[11]
subject_id = line[1]
rest = "\t".join(line[1:])
if swissprot_f:
subject_id = subject_id.split("|")[1]
if blast_f and not tax_id == "N/A" and not force: # so that it does not overwrite existing taxIDs
print BtLog.warn_d['10'] % (idx + 1, line[0], line[1])
output.append("%s\t%s\t%s\t%s" %
(query_id, tax_id, bitscore, rest))
else:
try:
tax_id = taxid_d[subject_id]
except KeyError:
BtLog.warn_d['12'] % (subject_id, map_f)
tax_id = "N/A"
output.append("%s\t%s\t%s\t%s" %
(query_id, tax_id, bitscore, rest))
if output:
with open(out_f, "w") as fh:
print BtLog.status_d['24'] % out_f
fh.write("\n".join(output))
def main():
#print data_dir
args = docopt(__doc__)
blobdb_f = args['--input']
prefix = args['--out']
ranks = args['--rank']
taxrule = args['--taxrule']
hits_flag = args['--hits']
seq_list_f = args['--list']
concoct = args['--concoct']
cov = args['--cov']
notable = args['--notable']
experimental = args['--experimental']
# Does blobdb_f exist ?
if not isfile(blobdb_f):
BtLog.error('0', blobdb_f)
out_f = BtIO.getOutFile(blobdb_f, prefix, None)
# Are ranks sane ?
if 'all' in ranks:
temp_ranks = RANKS[0:-1]
ranks = temp_ranks[::-1]
else:
for rank in ranks:
if rank not in RANKS:
BtLog.error('9', rank)
# Does seq_list file exist?
seqs = []
if (seq_list_f):
if isfile(seq_list_f):
seqs = BtIO.parseList(seq_list_f)
else:
BtLog.error('0', seq_list_f)
# Load BlobDb
blobDb = BtCore.BlobDb('new')
print BtLog.status_d['9'] % (blobdb_f)
blobDb.load(blobdb_f)
blobDb.version = blobtools.__version__
# Is taxrule sane and was it computed?
if (blobDb.hitLibs) and taxrule not in blobDb.taxrules:
BtLog.error('11', taxrule, blobDb.taxrules)
# view(s)
viewObjs = []
print BtLog.status_d['14']
if not (notable):
tableView = BtCore.ViewObj(name="table", out_f=out_f, suffix="table.txt", body=[])
viewObjs.append(tableView)
if (experimental):
experimentalView = BtCore.ExperimentalViewObj(name = "experimental", view_dir=out_f)
viewObjs.append(experimentalView)
if (concoct):
concoctTaxView = BtCore.ViewObj(name="concoct_tax", out_f=out_f, suffix="concoct_taxonomy_info.csv", body=dict())
viewObjs.append(concoctTaxView)
concoctCovView = BtCore.ViewObj(name="concoct_cov", out_f=out_f, suffix="concoct_coverage_info.tsv", body=[])
viewObjs.append(concoctCovView)
if (cov):
for cov_lib_name, covLibDict in blobDb.covLibs.items():
out_f = BtIO.getOutFile(covLibDict['f'], prefix, None)
covView = BtCore.ViewObj(name="covlib", out_f=out_f, suffix="cov", body=[])
blobDb.view(viewObjs=[covView], ranks=None, taxrule=None, hits_flag=None, seqs=None, cov_libs=[cov_lib_name], progressbar=True)
if (viewObjs):
blobDb.view(viewObjs=viewObjs, ranks=ranks, taxrule=taxrule, hits_flag=hits_flag, seqs=seqs, cov_libs=[], progressbar=True)
print BtLog.status_d['19']
def getPlotData(self, rank, min_length, hide_nohits, taxrule, c_index, catcolour_dict):
data_dict = {}
read_cov_dict = {}
max_cov = 0.0
min_cov = 1000.0
cov_lib_dict = self.covLibs
cov_lib_names_l = self.covLibs.keys() # does not include cov_sum
if len(cov_lib_names_l) > 1:
# more than one cov_lib, cov_sum_lib has to be created
cov_lib_dict["covsum"] = CovLibObj(
"covsum", "covsum", "Sum of cov in %s" % basename(self.title)
).__dict__ # ugly
cov_lib_dict["covsum"]["reads_total"] = sum([self.covLibs[x]["reads_total"] for x in self.covLibs])
cov_lib_dict["covsum"]["reads_mapped"] = sum([self.covLibs[x]["reads_mapped"] for x in self.covLibs])
cov_lib_dict["covsum"]["cov_sum"] = sum([self.covLibs[x]["cov_sum"] for x in self.covLibs])
cov_lib_dict["covsum"]["mean_cov"] = cov_lib_dict["covsum"]["cov_sum"] / self.seqs
for blob in self.dict_of_blobs.values():
name, gc, length, group = blob["name"], blob["gc"], blob["length"], ""
if catcolour_dict: # annotation with categories specified in catcolour
group = str(catcolour_dict[name])
elif c_index: # annotation with c_index instead of taxonomic group
if taxrule not in self.taxrules:
BtLog.error("11", taxrule, self.taxrules)
else:
group = str(blob["taxonomy"][taxrule][rank]["c_index"])
else: # annotation with taxonomic group
if not (taxrule) or taxrule not in self.taxrules:
BtLog.warn_d["9"] % (taxrule, self.taxrules)
if taxrule in blob["taxonomy"]:
group = str(blob["taxonomy"][taxrule][rank]["tax"])
if not group in data_dict:
data_dict[group] = {
"name": [],
"length": [],
"gc": [],
"covs": {covLib: [] for covLib in cov_lib_dict.keys()}, # includes cov_sum if it exists
"reads_mapped": {covLib: 0 for covLib in cov_lib_dict.keys()}, # includes cov_sum if it exists
"count": 0,
"count_hidden": 0,
"count_visible": 0,
"span": 0,
"span_hidden": 0,
"span_visible": 0,
}
data_dict[group]["count"] = data_dict[group].get("count", 0) + 1
data_dict[group]["span"] = data_dict[group].get("span", 0) + int(length)
if ((hide_nohits) and group == "no-hit") or length < min_length: # hidden
data_dict[group]["count_hidden"] = data_dict[group].get("count_hidden", 0) + 1
data_dict[group]["span_hidden"] = data_dict[group].get("span_hidden", 0) + int(length)
else: # visible
data_dict[group]["count_visible"] = data_dict[group].get("count_visible", 0) + 1
data_dict[group]["span_visible"] = data_dict[group].get("span_visible", 0) + int(length)
data_dict[group]["name"].append(name)
data_dict[group]["length"].append(length)
data_dict[group]["gc"].append(gc)
cov_sum = 0.0
reads_mapped_sum = 0
for cov_lib in sorted(cov_lib_names_l):
cov = float(blob["covs"][cov_lib])
if cov < 0.02:
cov = 0.02
if cov < min_cov:
min_cov = cov
# increase max_cov
if cov > max_cov:
max_cov = cov
# add cov of blob to group
data_dict[group]["covs"][cov_lib].append(cov)
cov_sum += cov
# add readcov
if cov_lib in blob["read_cov"]:
reads_mapped = blob["read_cov"][cov_lib]
data_dict[group]["reads_mapped"][cov_lib] += reads_mapped
reads_mapped_sum += reads_mapped
if len(cov_lib_names_l) > 1:
if cov_sum < 0.02:
cov_sum = 0.02
data_dict[group]["covs"]["covsum"].append(cov_sum)
if cov_sum > max_cov:
max_cov = cov_sum
if reads_mapped_sum:
data_dict[group]["reads_mapped"]["covsum"] += reads_mapped_sum
return data_dict, min_cov, max_cov, cov_lib_dict
def parseBamForFilter(infile, outfile, include, exclude, gzip, do_sort, keep_sorted, sort_threads):
'''
checkBam returns reads_total and reads_mapped
parse BAM to extract readpairs
'''
if not isfile(infile):
BtLog.error('0', infile)
if do_sort:
command = 'samtools sort -@ sort_threads -n -O bam -T temp -o %s.readsorted.bam %s' % (infile, infile)
runCmd(command=command, wait=True)
infile = "%s.readsorted.bam" % infile
reads_total, reads_mapped = checkBam(infile)
progress_unit = int(reads_mapped/1000)
command = "samtools view -f 1 -F 1024 -F 256 -F 2048 %s" % infile
seen_reads = 0
read_pair_count, read_pair_seqs, read_pair_out_fs = init_read_pairs(outfile, include, exclude)
read_pair_out_fhs = []
used_fhs = {}
iterator = runCmd(command=command)
read_pair_type = None
if include:
sequence_to_type_dict = defaultdict(lambda: 'Ex')
for incl in include:
sequence_to_type_dict[incl] = 'In'
sequence_to_type_dict['*'] = 'Un'
elif exclude:
sequence_to_type_dict = defaultdict(lambda: 'In')
for excl in exclude:
sequence_to_type_dict[excl] = 'Ex'
sequence_to_type_dict['*'] = 'Un'
else:
sequence_to_type_dict = defaultdict(lambda: 'In')
sequence_to_type_dict['*'] = 'Un'
for l in iterator:
read1 = l.split()
try:
seen_reads += 2
read2 = next(iterator).split()
read_pair_type = "".join(sorted([sequence_to_type_dict[read1[2]], sequence_to_type_dict[read2[2]]]))
print_bam(read_pair_out_fs, read_pair_type, read1, read2)
read_pair_seqs[read_pair_type] += get_read_pair_seqs(read1, read2)
read_pair_count[read_pair_type] += 1
BtLog.progress(seen_reads, progress_unit, reads_total)
if seen_reads % progress_unit == 0:
used_fhs = write_read_pair_seqs(used_fhs, read_pair_out_fs, read_pair_seqs)
read_pair_seqs = {read_pair_type : tuple() for read_pair_type in read_pair_count}
except StopIteration:
print BtLog.warn_d['11']
used_fhs = write_read_pair_seqs(used_fhs, read_pair_out_fs, read_pair_seqs)
close_fhs(used_fhs)
# info log
info_string = []
info_string.append(('Total pairs', "{:,}".format(int(seen_reads/2)), '{0:.1%}'.format(1.00)))
for read_pair_type, count in read_pair_count.items():
info_string.append((read_pair_type + ' pairs', "{:,}".format(count), '{0:.1%}'.format(count/int(seen_reads/2))))
info_out_f = getOutFile(outfile, None, "info.txt")
with open(info_out_f, 'w') as info_fh:
print BtLog.status_d['24'] % info_out_f
info_fh.write(get_table(info_string))
# gzip
if gzip:
if not which('gzip'):
BtLog.error('43')
for out_f in used_fhs:
print BtLog.status_d['25'] % out_f
runCmd(command="gzip -f " + out_f, wait=True)
if not int(reads_total) == int(seen_reads):
print BtLog.warn_d['3'] % (reads_total, seen_reads)
if do_sort and not keep_sorted:
os.remove(infile)
return 1
def check_input(args):
blobdb_f = args['--infile']
rank = args['--rank']
c_index = args['--cindex']
min_length = int(args['--length'])
multiplot = args['--multiplot']
hide_nohits = args['--nohit']
out_prefix = args['--out']
max_group_plot = int(args['--plotgroups'])
sort_order = args['--sort']
taxrule = args['--taxrule']
hist_type = args['--hist']
no_title = args['--notitle']
ignore_contig_length = args['--noscale']
labels = args['--label']
colour_f = args['--colours']
exclude_groups = args['--exclude']
format = args['--format']
no_plot_blobs = args['--noblobs']
no_plot_reads = args['--noreads']
refcov_f = args['--refcov']
catcolour_f = args['--catcolour']
legend_flag = args['--legend']
cumulative_flag = args['--cumulative']
cov_lib_selection = args['--lib']
if 'blobplot' in args or 'covplot' in args:
# Are ranks sane ?
if rank not in BtTax.RANKS:
BtLog.error('9', rank)
# is taxrule provided?
if taxrule not in BtTax.TAXRULES:
BtLog.error('8', taxrule)
# Are sort_order and hist_type sane?
if not sort_order in ['span', 'count']:
BtLog.error('14', sort_order)
if not hist_type in ['span', 'count']:
BtLog.error('15', hist_type)
if (catcolour_f) and (c_index):
BtLog.error('24')
if (cumulative_flag) and (multiplot):
BtLog.error('32')
return args
def check_input(args):
blobdb_f = args['--infile']
rank = args['--rank']
c_index = args['--cindex']
min_length = int(args['--length'])
multiplot = args['--multiplot']
hide_nohits = args['--nohit']
out_prefix = args['--out']
max_group_plot = int(args['--plotgroups'])
sort_order = args['--sort']
sort_first = args['--sort_first']
taxrule = args['--taxrule']
hist_type = args['--hist']
no_title = args['--notitle']
ignore_contig_length = args['--noscale']
labels = args['--label']
colour_f = args['--colours']
exclude_groups = args['--exclude']
format = args['--format']
no_plot_blobs = args['--noblobs']
no_plot_reads = args['--noreads']
refcov_f = args['--refcov']
catcolour_f = args['--catcolour']
legend_flag = args['--legend']
cumulative_flag = args['--cumulative']
cov_lib_selection = args['--lib']
#Convert sort_first to a list
if sort_first:
args['--sort_first'] = sort_first.split(',')
else:
args['--sort_first'] = ()
if 'blobplot' in args or 'covplot' in args:
# Are ranks sane ?
if rank not in BtTax.RANKS:
BtLog.error('9', rank)
# is taxrule provided?
if taxrule not in BtTax.TAXRULES:
BtLog.error('8', taxrule)
# Are sort_order and hist_type sane?
if not sort_order in ['span', 'count']:
BtLog.error('14', sort_order)
if not hist_type in ['span', 'count']:
BtLog.error('15', hist_type)
if (catcolour_f) and (c_index):
BtLog.error('24')
if (cumulative_flag) and (multiplot):
BtLog.error('32')
return args
def parseBamForFilter(infile, outfile, include, exclude, gzip, do_sort,
keep_sorted, sort_threads):
'''
checkBam returns reads_total and reads_mapped
parse BAM to extract readpairs
'''
if not isfile(infile):
BtLog.error('0', infile)
if do_sort:
command = 'samtools sort -@ sort_threads -n -O bam -T temp -o %s.readsorted.bam %s' % (
infile, infile)
runCmd(command=command, wait=True)
infile = "%s.readsorted.bam" % infile
reads_total, reads_mapped = checkBam(infile)
progress_unit = int(reads_mapped / 1000)
command = "samtools view -f 1 -F 1024 -F 256 -F 2048 %s" % infile
seen_reads = 0
read_pair_count, read_pair_seqs, read_pair_out_fs = init_read_pairs(
outfile, include, exclude)
read_pair_out_fhs = []
used_fhs = {}
iterator = runCmd(command=command)
read_pair_type = None
if include:
sequence_to_type_dict = defaultdict(lambda: 'Ex')
for incl in include:
sequence_to_type_dict[incl] = 'In'
sequence_to_type_dict['*'] = 'Un'
elif exclude:
sequence_to_type_dict = defaultdict(lambda: 'In')
for excl in exclude:
sequence_to_type_dict[excl] = 'Ex'
sequence_to_type_dict['*'] = 'Un'
else:
sequence_to_type_dict = defaultdict(lambda: 'In')
sequence_to_type_dict['*'] = 'Un'
for l in iterator:
read1 = l.split()
try:
seen_reads += 2
read2 = next(iterator).split()
read_pair_type = "".join(
sorted([
sequence_to_type_dict[read1[2]],
sequence_to_type_dict[read2[2]]
]))
print_bam(read_pair_out_fs, read_pair_type, read1, read2)
read_pair_seqs[read_pair_type] += get_read_pair_seqs(read1, read2)
read_pair_count[read_pair_type] += 1
BtLog.progress(seen_reads, progress_unit, reads_total)
if seen_reads % progress_unit == 0:
used_fhs = write_read_pair_seqs(used_fhs, read_pair_out_fs,
read_pair_seqs)
read_pair_seqs = {
read_pair_type: tuple()
for read_pair_type in read_pair_count
}
except StopIteration:
print BtLog.warn_d['11']
used_fhs = write_read_pair_seqs(used_fhs, read_pair_out_fs, read_pair_seqs)
close_fhs(used_fhs)
# info log
info_string = []
info_string.append(('Total pairs', "{:,}".format(int(seen_reads / 2)),
'{0:.1%}'.format(1.00)))
for read_pair_type, count in read_pair_count.items():
info_string.append((read_pair_type + ' pairs', "{:,}".format(count),
'{0:.1%}'.format(count / int(seen_reads / 2))))
info_out_f = getOutFile(outfile, None, "info.txt")
with open(info_out_f, 'w') as info_fh:
print BtLog.status_d['24'] % info_out_f
info_fh.write(get_table(info_string))
# gzip
if gzip:
if not which('gzip'):
BtLog.error('43')
for out_f in used_fhs:
print BtLog.status_d['25'] % out_f
runCmd(command="gzip -f " + out_f, wait=True)
if not int(reads_total) == int(seen_reads):
print BtLog.warn_d['3'] % (reads_total, seen_reads)
if do_sort and not keep_sorted:
os.remove(infile)
return 1
