def __init__(self, reads=False, path_to_baga=False):
'''
Initialise with a baga.CollectData.Reads object
or
path to a previously save baga object of this class
'''
assert bool(reads) ^ bool(path_to_baga), 'Instantiate with baga.CollectData.Reads or '\
'the path to a previously saved baga.PreparedReads.Reads object' # xor
if reads:
try:
self.read_files = reads.read_files
except AttributeError:
print('baga.PrepareReads.Reads needs a baga.CollectData.Reads object '\
'with a "read_files" attribute. This can be obtained with the '\
'"getFromENA()" or "getFromPath()" methods.')
else:
try:
loaded_baga = _cPickle.load(_gzip.open(path_to_baga, 'rb'))
for attribute_name in dir(loaded_baga):
if attribute_name[0] != '_':
setattr(self, attribute_name,
getattr(loaded_baga, attribute_name))
except IOError:
print('Could not access {}'.format(file_name))
def saveLocal(self, name):
'''
Save a downloaded read info to a local compressed pickle file.
'name' can exclude extension: .baga will be added
'''
fileout = 'baga.CollectData.Reads-%s.baga' % name
print('Saving to %s' % fileout)
_cPickle.dump(self, _gzip.open(fileout, 'wb'))
def saveLocal(self, name):
'''
Save a downloaded read info to a local compressed pickle file.
'name' can exclude extension: .baga will be added
'''
fileout = 'baga.CollectData.Reads-%s.baga' % name
print('Saving to %s' % fileout)
_cPickle.dump(self, _gzip.open(fileout, 'wb'))
def __init__(self, reads = False, path_to_baga = False):
'''
Initialise with a baga.CollectData.Reads object
or
path to a previously save baga object of this class
'''
assert bool(reads) ^ bool(path_to_baga), 'Instantiate with baga.CollectData.Reads or '\
'the path to a previously saved baga.PreparedReads.Reads object' # xor
if reads:
try:
self.read_files = reads.read_files
except AttributeError:
print('baga.PrepareReads.Reads needs a baga.CollectData.Reads object '\
'with a "read_files" attribute. This can be obtained with the '\
'"getFromENA()" or "getFromPath()" methods.')
else:
try:
loaded_baga = _cPickle.load(_gzip.open(path_to_baga,'rb'))
for attribute_name in dir(loaded_baga):
if attribute_name[0] != '_':
setattr(self, attribute_name, getattr(loaded_baga, attribute_name))
except IOError:
print('Could not access {}'.format(file_name))
def subsample(self, genome_size = 6601757,
read_cov_depth = 80,
pc_loss = 0.2,
force = False,
cov_closeness = 5):
'''
Given the size in basepairs of a genome sequence, downsample fastq files to a
desired average read coverage depth predicted after read alignment. Read lengths
are taken from the file. By default, 20% are assumed to be lost at downstream
quality control stages (e.g. quality score based trimming). The percent loss is
used in coverage depth estimation. cov_closeness, which defaults to 5, will prevent
subsampling if within 5x coverage: avoids time consuming subsampling that will only
make a small difference.
'''
subsampled_read_files = {}
start_time = _time.time()
for cnum,(pairname,files) in enumerate(self.read_files.items()):
processed_path_1 = insert_suffix(files[1], '_subsmp')
processed_path_2 = insert_suffix(files[2], '_subsmp')
if not all([_os.path.exists(processed_path_1),
_os.path.exists(processed_path_2)]) \
or force:
if files[1][-2:] == 'gz':
fh1 = _gzip.open(files[1])
else:
fh1 = open(files[1])
aread = _SeqIO.parse(fh1, 'fastq').next()
read_len = len(aread.seq)
print('Counting reads in %s' % files[1])
fh1.seek(0)
lines = 0
# report per half million reads
interval = 2000000
nextreport = interval
for line in fh1:
lines += 1
if lines == nextreport:
print('{:,} reads'.format(lines/4))
nextreport += interval
totalreads = lines / 4.0
print('Found %s reads' % totalreads)
full_depth_coverage = read_len * 2 * totalreads * (1 - pc_loss) / genome_size
print('These paired read files would provide approximately {:.1f}x coverage depth'.format(full_depth_coverage))
numreads2keep = int( round(genome_size * read_cov_depth / (read_len * 2) / (1 - pc_loss), 0) )
if numreads2keep >= totalreads:
print('This pair of read files is estimated to provide only {:.1f}x coverage, but {}x requested.'.format(full_depth_coverage, read_cov_depth))
print('No sampling performed. Original files will be used')
# pass original files over with subsampled
subsampled_read_files[pairname] = {}
subsampled_read_files[pairname][1] = files[1]
subsampled_read_files[pairname][2] = files[2]
fh1.close()
if len(self.read_files) > 1:
# report durations, time left etc
_report_time(start_time, cnum, len(self.read_files))
continue
elif full_depth_coverage < read_cov_depth + cov_closeness:
print('This pair of read files is estimated to provide {:.1f}x coverage which is within {}x of {}x requested.'.format(full_depth_coverage, cov_closeness, read_cov_depth))
print('No sampling performed. Original files will be used')
# pass original files over with subsampled
subsampled_read_files[pairname] = {}
subsampled_read_files[pairname][1] = files[1]
subsampled_read_files[pairname][2] = files[2]
fh1.close()
if len(self.read_files) > 1:
# report durations, time left etc
_report_time(start_time, cnum, len(self.read_files))
continue
else:
print('For approximately {}x read coverage, will retain {} of {} {}bp read pairs'.format(
read_cov_depth, numreads2keep, totalreads, read_len))
fh1.seek(0)
if files[2][-2:] == 'gz':
fh2 = _gzip.open(files[2])
else:
fh2 = open(files[2])
fout1 = _gzip.open(processed_path_1, 'wb')
fout2 = _gzip.open(processed_path_2, 'wb')
batch_size = 200000
keep_per_pop = int(numreads2keep / float(totalreads) * batch_size) + 1
nextwrite = batch_size
written = 0
n1 = 0
n2 = 0
these_lines1 = []
these_lines2 = []
reportfreq = 10
thisreport = 0
print('Subsampling . . .')
for line in fh1:
these_lines1 += [line]
if len(these_lines1) % 4 == 0:
n1 += 1
if n1 == nextwrite:
keep_indices = sorted(_sample(xrange(batch_size), keep_per_pop))
keep_these = []
for i in keep_indices:
i1 = i * 4
i2 = i * 4 + 4
keep_these += these_lines1[i1:i2]
# try parsing a read for QC
assert _SeqIO.read(_StringIO(''.join(keep_these[:4])), 'fastq')
fout1.write(''.join(keep_these))
these_lines1 = []
written += keep_per_pop
thisreport += 1
if thisreport == reportfreq or written == keep_per_pop:
# report first time and at intevals
print('Written {:,} reads ({:.1%}) to {}'.format(written,
written/float(numreads2keep),
processed_path_1))
for line2 in fh2:
these_lines2 += [line2]
if len(these_lines2) % 4 == 0:
n2 += 1
if n2 == nextwrite:
keep_these = []
for i in keep_indices:
i1 = i * 4
i2 = i * 4 + 4
keep_these += these_lines2[i1:i2]
assert _SeqIO.read(_StringIO(''.join(keep_these[:4])), 'fastq')
fout2.write(''.join(keep_these))
these_lines2 = []
if thisreport == reportfreq or written == keep_per_pop:
thisreport = 0
print('Written {:,} reads ({:.1%}) to {}'.format(written,
written/float(numreads2keep),
processed_path_2))
nextwrite += batch_size
break
# write remainder
remainder = nextwrite - n1
keep_in_remainder = int(keep_per_pop * (remainder / float(batch_size))) + 1
keep_indices = sorted(_sample(xrange(remainder), keep_in_remainder))
keep_these = []
for i in keep_indices:
i1 = i * 4
i2 = i * 4 + 4
keep_these += these_lines1[i1:i2]
# try parsing a read for QC
assert _SeqIO.read(_StringIO(''.join(keep_these[:4])), 'fastq')
fout1.write(''.join(keep_these))
written += keep_in_remainder
print('Written {:,} reads ({:.1%}) to {}'.format(written,
written/float(numreads2keep),
processed_path_1))
# get remainder
for line2 in fh2:
these_lines2 += [line2]
# write remainder
keep_these = []
for i in keep_indices:
i1 = i * 4
i2 = i * 4 + 4
keep_these += these_lines2[i1:i2]
assert _SeqIO.read(_StringIO(''.join(keep_these[:4])), 'fastq') ###### check why keep_these was empty
fout2.write(''.join(keep_these))
print('Written {:,} reads ({:.1%}) to {}'.format(written,
written/float(numreads2keep),
processed_path_2))
# not sure if this is quicker/slower (more calls to .join())
# this_read = []
# for line in fh1:
# this_read += [line]
# if len(this_read) == 4:
# these_reads1 += [''.join(this_read)]
# #these_reads1 += this_read
# this_read = []
# n1 += 1
# if n1 == nextwrite:
# keep_indices = sorted(_sample(xrange(batch_size), keep_per_pop))
# # try parsing a read for QC
# assert _SeqIO.read(_StringIO(these_reads1[0]), 'fastq')
# fout1.write(''.join([these_reads1[i] for i in keep_indices]))
# these_reads1 = []
# written += keep_per_pop
# print('Written {:,} reads ({:.2%}) to {}'.format(written,
# written/float(numreads2keep),
# processed_path_1))
# for line2 in fh2:
# this_read += [line2]
# if len(this_read) == 4:
# these_reads2 += [''.join(this_read)]
# this_read = []
# n2 += 1
# if n2 == nextwrite:
# assert _SeqIO.read(_StringIO(these_reads2[0]), 'fastq')
# fout2.write(''.join([these_reads2[i] for i in keep_indices]))
# these_reads2 = []
# print('Written {:,} reads ({:.2%}) to {}'.format(written,
# written/float(numreads2keep),
# processed_path_2))
# nextwrite += batch_size
# break
fout1.close()
fout2.close()
fh1.close()
fh2.close()
else:
print('Found:')
print(processed_path_1)
print(processed_path_2)
print('use "force = True" to overwrite')
if len(self.read_files) > 1:
# report durations, time left etc
_report_time(start_time, cnum, len(self.read_files))
subsampled_read_files[pairname] = {}
subsampled_read_files[pairname][1] = processed_path_1
subsampled_read_files[pairname][2] = processed_path_2
# replace here as this step is optional
self.fullsized_read_files = list(self.read_files)
self.read_files = subsampled_read_files
def subsample(self,
genome_size=6601757,
read_cov_depth=80,
pc_loss=0.2,
force=False,
cov_closeness=5):
'''
Given the size in basepairs of a genome sequence, downsample fastq files to a
desired average read coverage depth predicted after read alignment. Read lengths
are taken from the file. By default, 20% are assumed to be lost at downstream
quality control stages (e.g. quality score based trimming). The percent loss is
used in coverage depth estimation. cov_closeness, which defaults to 5, will prevent
subsampling if within 5x coverage: avoids time consuming subsampling that will only
make a small difference.
'''
subsampled_read_files = {}
start_time = _time.time()
for cnum, (pairname, files) in enumerate(self.read_files.items()):
processed_path_1 = insert_suffix(files[1], '_subsmp')
processed_path_2 = insert_suffix(files[2], '_subsmp')
if not all([_os.path.exists(processed_path_1),
_os.path.exists(processed_path_2)]) \
or force:
if files[1][-2:] == 'gz':
fh1 = _gzip.open(files[1])
else:
fh1 = open(files[1])
aread = _SeqIO.parse(fh1, 'fastq').next()
read_len = len(aread.seq)
print('Counting reads in %s' % files[1])
fh1.seek(0)
lines = 0
# report per half million reads
interval = 2000000
nextreport = interval
for line in fh1:
lines += 1
if lines == nextreport:
print('{:,} reads'.format(lines / 4))
nextreport += interval
totalreads = lines / 4.0
print('Found %s reads' % totalreads)
full_depth_coverage = read_len * 2 * totalreads * (
1 - pc_loss) / genome_size
print(
'These paired read files would provide approximately {:.1f}x coverage depth'
.format(full_depth_coverage))
numreads2keep = int(
round(
genome_size * read_cov_depth / (read_len * 2) /
(1 - pc_loss), 0))
if numreads2keep >= totalreads:
print(
'This pair of read files is estimated to provide only {:.1f}x coverage, but {}x requested.'
.format(full_depth_coverage, read_cov_depth))
print('No sampling performed. Original files will be used')
# pass original files over with subsampled
subsampled_read_files[pairname] = {}
subsampled_read_files[pairname][1] = files[1]
subsampled_read_files[pairname][2] = files[2]
fh1.close()
if len(self.read_files) > 1:
# report durations, time left etc
_report_time(start_time, cnum, len(self.read_files))
continue
elif full_depth_coverage < read_cov_depth + cov_closeness:
print(
'This pair of read files is estimated to provide {:.1f}x coverage which is within {}x of {}x requested.'
.format(full_depth_coverage, cov_closeness,
read_cov_depth))
print('No sampling performed. Original files will be used')
# pass original files over with subsampled
subsampled_read_files[pairname] = {}
subsampled_read_files[pairname][1] = files[1]
subsampled_read_files[pairname][2] = files[2]
fh1.close()
if len(self.read_files) > 1:
# report durations, time left etc
_report_time(start_time, cnum, len(self.read_files))
continue
else:
print(
'For approximately {}x read coverage, will retain {} of {} {}bp read pairs'
.format(read_cov_depth, numreads2keep, totalreads,
read_len))
fh1.seek(0)
if files[2][-2:] == 'gz':
fh2 = _gzip.open(files[2])
else:
fh2 = open(files[2])
fout1 = _gzip.open(processed_path_1, 'wb')
fout2 = _gzip.open(processed_path_2, 'wb')
batch_size = 200000
keep_per_pop = int(
numreads2keep / float(totalreads) * batch_size) + 1
nextwrite = batch_size
written = 0
n1 = 0
n2 = 0
these_lines1 = []
these_lines2 = []
reportfreq = 10
thisreport = 0
print('Subsampling . . .')
for line in fh1:
these_lines1 += [line]
if len(these_lines1) % 4 == 0:
n1 += 1
if n1 == nextwrite:
keep_indices = sorted(
_sample(xrange(batch_size), keep_per_pop))
keep_these = []
for i in keep_indices:
i1 = i * 4
i2 = i * 4 + 4
keep_these += these_lines1[i1:i2]
# try parsing a read for QC
assert _SeqIO.read(
_StringIO(''.join(keep_these[:4])), 'fastq')
fout1.write(''.join(keep_these))
these_lines1 = []
written += keep_per_pop
thisreport += 1
if thisreport == reportfreq or written == keep_per_pop:
# report first time and at intevals
print(
'Written {:,} reads ({:.1%}) to {}'.format(
written,
written / float(numreads2keep),
processed_path_1))
for line2 in fh2:
these_lines2 += [line2]
if len(these_lines2) % 4 == 0:
n2 += 1
if n2 == nextwrite:
keep_these = []
for i in keep_indices:
i1 = i * 4
i2 = i * 4 + 4
keep_these += these_lines2[i1:i2]
assert _SeqIO.read(
_StringIO(''.join(keep_these[:4])),
'fastq')
fout2.write(''.join(keep_these))
these_lines2 = []
if thisreport == reportfreq or written == keep_per_pop:
thisreport = 0
print(
'Written {:,} reads ({:.1%}) to {}'
.format(
written,
written / float(numreads2keep),
processed_path_2))
nextwrite += batch_size
break
# write remainder
remainder = nextwrite - n1
keep_in_remainder = int(
keep_per_pop * (remainder / float(batch_size))) + 1
keep_indices = sorted(
_sample(xrange(remainder), keep_in_remainder))
keep_these = []
for i in keep_indices:
i1 = i * 4
i2 = i * 4 + 4
keep_these += these_lines1[i1:i2]
# try parsing a read for QC
assert _SeqIO.read(_StringIO(''.join(keep_these[:4])),
'fastq')
fout1.write(''.join(keep_these))
written += keep_in_remainder
print('Written {:,} reads ({:.1%}) to {}'.format(
written, written / float(numreads2keep),
processed_path_1))
# get remainder
for line2 in fh2:
these_lines2 += [line2]
# write remainder
keep_these = []
for i in keep_indices:
i1 = i * 4
i2 = i * 4 + 4
keep_these += these_lines2[i1:i2]
assert _SeqIO.read(
_StringIO(''.join(keep_these[:4])),
'fastq') ###### check why keep_these was empty
fout2.write(''.join(keep_these))
print('Written {:,} reads ({:.1%}) to {}'.format(
written, written / float(numreads2keep),
processed_path_2))
# not sure if this is quicker/slower (more calls to .join())
# this_read = []
# for line in fh1:
# this_read += [line]
# if len(this_read) == 4:
# these_reads1 += [''.join(this_read)]
# #these_reads1 += this_read
# this_read = []
# n1 += 1
# if n1 == nextwrite:
# keep_indices = sorted(_sample(xrange(batch_size), keep_per_pop))
# # try parsing a read for QC
# assert _SeqIO.read(_StringIO(these_reads1[0]), 'fastq')
# fout1.write(''.join([these_reads1[i] for i in keep_indices]))
# these_reads1 = []
# written += keep_per_pop
# print('Written {:,} reads ({:.2%}) to {}'.format(written,
# written/float(numreads2keep),
# processed_path_1))
# for line2 in fh2:
# this_read += [line2]
# if len(this_read) == 4:
# these_reads2 += [''.join(this_read)]
# this_read = []
# n2 += 1
# if n2 == nextwrite:
# assert _SeqIO.read(_StringIO(these_reads2[0]), 'fastq')
# fout2.write(''.join([these_reads2[i] for i in keep_indices]))
# these_reads2 = []
# print('Written {:,} reads ({:.2%}) to {}'.format(written,
# written/float(numreads2keep),
# processed_path_2))
# nextwrite += batch_size
# break
fout1.close()
fout2.close()
fh1.close()
fh2.close()
else:
print('Found:')
print(processed_path_1)
print(processed_path_2)
print('use "force = True" to overwrite')
if len(self.read_files) > 1:
# report durations, time left etc
_report_time(start_time, cnum, len(self.read_files))
subsampled_read_files[pairname] = {}
subsampled_read_files[pairname][1] = processed_path_1
subsampled_read_files[pairname][2] = processed_path_2
# replace here as this step is optional
self.fullsized_read_files = list(self.read_files)
self.read_files = subsampled_read_files
