def getFromENA(self, run_acc_list,
ftp_server_url = 'ftp.sra.ebi.ac.uk',
local_reads_path = ['reads']):
'''
Given a list of 'run' accession numbers for paired end short read analyses,
download the read files from the European Nucleotide Archive.
If using a mirror server, supply an alternative for 'ftp_server_url'.
'local_reads_path' can be a path string or list or folder names.
'''
if isinstance(local_reads_path, list):
local_reads_path = _os.path.sep.join(local_reads_path)
if not _os.path.exists(local_reads_path):
_os.makedirs(local_reads_path)
print('Logging in to %s' % ftp_server_url)
ftp = _FTP(ftp_server_url)
# anonymous login
print(ftp.login())
def check_connection(ftp):
try:
print('FTP: %s' % ftp.voidcmd("NOOP"))
# http://docs.python.org/2/library/ftplib.html
return(True)
except IOError as e:
print('Seems to be a problem with the connection to FTP server:')
print('I/O error({0}): {1}'.format(e.errno, e.strerror) )
return(False)
def calc_checksum(filepath):
hasher = _md5()
handle = open(filepath, 'rb')
buff = handle.read(65536)
while len(buff) > 0:
hasher.update(buff)
buff = handle.read(65536)
return(hasher.hexdigest())
downloaded_read_files = {}
start_time = _time.time()
failed = []
for cnum,run_acc in enumerate(run_acc_list):
query_url_base = 'http://www.ebi.ac.uk/ena/data/warehouse/search?query='
success = False
tries = 0
max_tries = 5
while not success:
rest_req = '"run_accession=%s"&result=read_run&fields=fastq_ftp,fastq_md5&display=report' % run_acc
print('Sending query to ENA:\n%s' % rest_req)
result = _urllib2.urlopen(query_url_base + rest_req).read()
print('ENA accession numbers query result:\n%s' % result)
if result.count('ERR') == 7:
success = True
else:
print('Query result from ENA was unexpected on attempt %s of %s' % (tries, max_tries))
_time.sleep(0.5)
tries += 1
if tries == max_tries:
print('Attempt %s failed. Try again later and if problem persists, report bug.' % tries)
failed += [run_acc]
break
#_sys.exit(1)
if not success:
continue
md5s = result.split('\n')[-2].split('\t')[-1][:-1].split(';')
ENA_paths = result.split('\n')[-2].split('\t')[-2][:-1].split(';')
ENA_reads_pair_paths = {}
ENA_reads_pair_paths[1] = ENA_paths[0].replace(ftp_server_url, '')
ENA_reads_pair_paths[2] = ENA_paths[1].replace(ftp_server_url, '')
local_reads_pair_paths = {}
local_reads_pair_paths[1] = local_reads_path + \
_os.path.sep + \
ENA_reads_pair_paths[1].split('/')[-1]
local_reads_pair_paths[2] = local_reads_path + \
_os.path.sep + \
ENA_reads_pair_paths[2].split('/')[-1]
downloaded_read_files[run_acc] = {}
for f in (1,2):
# ensure connection is still open
while not check_connection(ftp):
_sleep(0.5)
print('Attempting to re-establish connection . . .')
ftp = _FTP(ftp_server_url)
# anonymous login
print(ftp.login())
pass
expected_checksum = md5s[f - 1]
exists = _os.path.exists(local_reads_pair_paths[f])
if exists:
print('File %s for %s exists locally: %s' % (f, run_acc, local_reads_pair_paths[f]))
actual_checksum = calc_checksum(local_reads_pair_paths[f])
if actual_checksum == expected_checksum:
print('File checksum matches: %s. Skipping download' % (expected_checksum))
downloaded_read_files[run_acc][f] = local_reads_pair_paths[f]
continue
else:
print('Checksum mismatch')
print('Downloading via %s: %s' % (ftp_server_url, ENA_reads_pair_paths[f]))
res = ftp.retrbinary('RETR %s' % ENA_reads_pair_paths[f],
open(local_reads_pair_paths[f], 'wb').write)
print('FTP: %s' % res)
print('Calculating checksum . . .')
actual_checksum = calc_checksum(local_reads_pair_paths[f])
if actual_checksum == expected_checksum:
print('File checksum matches: %s.' % (expected_checksum))
downloaded_read_files[run_acc][f] = local_reads_pair_paths[f]
else:
print('Checksum mismatch for: %s')
if len(run_acc_list) > 1:
# report durations, time left etc
_report_time(start_time, cnum, len(run_acc_list))
if len(failed) > 0:
print('WARNING: some accession numbers did not return a result from ENA')
print('Try searching http://www.ebi.ac.uk/ena in a web-browser for:')
print(', '.join(failed))
self.read_files = downloaded_read_files
def getFromENA(self,
run_acc_list,
ftp_server_url='ftp.sra.ebi.ac.uk',
local_reads_path=['reads']):
'''
Given a list of 'run' accession numbers for paired end short read analyses,
download the read files from the European Nucleotide Archive.
If using a mirror server, supply an alternative for 'ftp_server_url'.
'local_reads_path' can be a path string or list or folder names.
'''
if isinstance(local_reads_path, list):
local_reads_path = _os.path.sep.join(local_reads_path)
if not _os.path.exists(local_reads_path):
_os.makedirs(local_reads_path)
print('Logging in to %s' % ftp_server_url)
ftp = _FTP(ftp_server_url)
# anonymous login
print(ftp.login())
def check_connection(ftp):
try:
print('FTP: %s' % ftp.voidcmd("NOOP"))
# http://docs.python.org/2/library/ftplib.html
return (True)
except IOError as e:
print(
'Seems to be a problem with the connection to FTP server:')
print('I/O error({0}): {1}'.format(e.errno, e.strerror))
return (False)
def calc_checksum(filepath):
hasher = _md5()
handle = open(filepath, 'rb')
buff = handle.read(65536)
while len(buff) > 0:
hasher.update(buff)
buff = handle.read(65536)
return (hasher.hexdigest())
downloaded_read_files = {}
start_time = _time.time()
failed = []
for cnum, run_acc in enumerate(run_acc_list):
query_url_base = 'http://www.ebi.ac.uk/ena/data/warehouse/search?query='
success = False
tries = 0
max_tries = 5
while not success:
rest_req = '"run_accession=%s"&result=read_run&fields=fastq_ftp,fastq_md5&display=report' % run_acc
print('Sending query to ENA:\n%s' % rest_req)
result = _urllib2.urlopen(query_url_base + rest_req).read()
print('ENA accession numbers query result:\n%s' % result)
if result.count('ERR') == 7:
success = True
else:
print(
'Query result from ENA was unexpected on attempt %s of %s'
% (tries, max_tries))
_time.sleep(0.5)
tries += 1
if tries == max_tries:
print(
'Attempt %s failed. Try again later and if problem persists, report bug.'
% tries)
failed += [run_acc]
break
#_sys.exit(1)
if not success:
continue
md5s = result.split('\n')[-2].split('\t')[-1][:-1].split(';')
ENA_paths = result.split('\n')[-2].split('\t')[-2][:-1].split(';')
ENA_reads_pair_paths = {}
ENA_reads_pair_paths[1] = ENA_paths[0].replace(ftp_server_url, '')
ENA_reads_pair_paths[2] = ENA_paths[1].replace(ftp_server_url, '')
local_reads_pair_paths = {}
local_reads_pair_paths[1] = local_reads_path + \
_os.path.sep + \
ENA_reads_pair_paths[1].split('/')[-1]
local_reads_pair_paths[2] = local_reads_path + \
_os.path.sep + \
ENA_reads_pair_paths[2].split('/')[-1]
downloaded_read_files[run_acc] = {}
for f in (1, 2):
# ensure connection is still open
while not check_connection(ftp):
_sleep(0.5)
print('Attempting to re-establish connection . . .')
ftp = _FTP(ftp_server_url)
# anonymous login
print(ftp.login())
pass
expected_checksum = md5s[f - 1]
exists = _os.path.exists(local_reads_pair_paths[f])
if exists:
print('File %s for %s exists locally: %s' %
(f, run_acc, local_reads_pair_paths[f]))
actual_checksum = calc_checksum(local_reads_pair_paths[f])
if actual_checksum == expected_checksum:
print('File checksum matches: %s. Skipping download' %
(expected_checksum))
downloaded_read_files[run_acc][
f] = local_reads_pair_paths[f]
continue
else:
print('Checksum mismatch')
print('Downloading via %s: %s' %
(ftp_server_url, ENA_reads_pair_paths[f]))
res = ftp.retrbinary(
'RETR %s' % ENA_reads_pair_paths[f],
open(local_reads_pair_paths[f], 'wb').write)
print('FTP: %s' % res)
print('Calculating checksum . . .')
actual_checksum = calc_checksum(local_reads_pair_paths[f])
if actual_checksum == expected_checksum:
print('File checksum matches: %s.' % (expected_checksum))
downloaded_read_files[run_acc][f] = local_reads_pair_paths[
f]
else:
print('Checksum mismatch for: %s')
if len(run_acc_list) > 1:
# report durations, time left etc
_report_time(start_time, cnum, len(run_acc_list))
if len(failed) > 0:
print(
'WARNING: some accession numbers did not return a result from ENA'
)
print(
'Try searching http://www.ebi.ac.uk/ena in a web-browser for:')
print(', '.join(failed))
self.read_files = downloaded_read_files
def SPAdes(self,
exe=[],
output_folder=['assemblies', 'SPAdes'],
mem_num_gigs=8,
max_cpus=-1,
single_assembly=False,
careful=True,
only_assembler=False):
'''
de novo assembly of short reads using SPAdes
By default, the provided short reads in dictionary: self.paths_to_reads
will be assembled separately, unless single_assembly set to True in
which case each set of paired read fastq files will be used in a
single assembly.
http://spades.bioinf.spbau.ru/release3.6.1/manual.html
relevent inputs:
-o <output_dir> Specify the output directory. Required option.
--sc required for MDA (single-cell) data.
--only-error-correction
--only-assembler
--careful reduce the number of mismatches and short indels. Run MismatchCorrector – a post processing tool. Recommended.
--continue from the specified output folder starting from the last available check-point
--restart-from <check_point>
ec start from error correction
as restart assembly module from the first iteration
k<int> restart from the iteration with specified k values, e.g. k55
mc restart mismatch correction
--pe1-12 <file_name> interlaced forward and reverse paired-end reads.
--pe1-1 <file_name> File with forward reads.
--pe1-2 <file_name> File with reverse reads.
--pe1-s <file_name> File with unpaired reads . . use --pe2-... for next library
--threads <int>
--memory <int> max memory in Gb
-k <int,int,...> Comma-separated list of odd ascending k-mers
If --sc is set the default value are 21,33,55, for multicell data sets it is auto
--cov-cutoff <float> positive float value, or 'auto', or 'off'. Default value is 'off'
'''
assert isinstance(
output_folder,
list), 'Provide output folder as list of folders forming path'
base_output_path = _os.path.sep.join(output_folder)
if not _os.path.exists(base_output_path):
_os.makedirs(base_output_path)
# max threads is slightly different to cpus
# . . can probably use more
max_processes = _decide_max_processes(max_cpus)
# if an exe is not provided, use that stored in Dependencies
if len(exe):
use_exe = _os.path.sep.join(exe)
else:
from baga import Dependencies
use_exe = _get_exe_path('spades')
def run_SPAdes(cmd):
proc = _subprocess.Popen(cmd,
stdout=_subprocess.PIPE,
stderr=_subprocess.PIPE)
# allow for failed SPAdes runs (possibly caused by small fastq files) <== but also check they were actually built properly
try:
stdout_value, stderr_value = proc.communicate()
checkthese = []
getline = False
for line in stdout_value.split('\n'):
if 'Warnings saved to' in line:
getline = False
if getline:
l = line.rstrip()
if len(l):
checkthese += [l]
if 'SPAdes pipeline finished WITH WARNINGS!' in line:
getline = True
if len(checkthese):
print('SPAdes completed with warnings:\n{}\n'.format(
'\n'.join(checkthese)))
else:
print('SPAdes completed without warnings')
# with open('___SPAdes_{}_good_{}.log'.format(cnum, thetime), 'w') as fout:
# fout.write(stdout_value)
path2contigs = _os.path.sep.join(
[this_output_path, 'contigs.fasta'])
except _subprocess.CalledProcessError as e:
print('SPAdes probably did not complete: error returned ({})'.
format(proc.returncode))
print('Error: {}'.format(e))
print(
'Writing some info relevent to SPAdes crash to ___SPAdes_{}_bad_{}.log'
.format(cnum, thetime))
with open('___SPAdes_{}_bad_{}.log'.format(cnum, thetime),
'w') as fout:
fout.write(dir(proc))
fout.write('\n' + str(e.returncode) + '\n')
fout.write(
_os.path.sep.join([this_output_path, 'contigs.fasta']))
path2contigs = None
return (path2contigs)
if isinstance(use_exe, list):
# allow for use of prepended executable with script to run
cmd = list(use_exe)
else:
# or just executable
cmd = [use_exe]
contigs = {}
if single_assembly:
print(
'Combining reads aligned at multiple regions into single assembly'
)
if isinstance(use_exe, list):
# allow for use of prepended executable with script to run
cmd = list(use_exe)
else:
# or just executable
cmd = [use_exe]
for cnum, (pairname, files) in enumerate(self.read_files.items()):
# allow use of tuples or dicts by converting dicts to lists
if isinstance(files, dict):
use_files = []
for k, v in sorted(files.items()):
use_files += [v]
else:
use_files = files
cmd += ['--pe{}-1'.format(cnum + 1), use_files[0]]
cmd += ['--pe{}-2'.format(cnum + 1), use_files[1]]
try:
# use unpaired reads if available
cmd += ['--pe{}-s'.format(cnum + 1), use_files[2]]
except IndexError:
pass
try:
# add a second library if provided
if isinstance(self.read_files2[pairname], dict):
# if a dict supplied, make it a list
use_files2 = []
for k, v in sorted(self.read_files2[pairname].items()):
use_files2 += [v]
else:
use_files2 = self.read_files2[pairname]
cmd += ['--pe{}-1'.format(cnum + 2), use_files2[0]]
cmd += ['--pe{}-2'.format(cnum + 2), use_files2[1]]
try:
cmd += ['--pe{}-s'.format(cnum + 2), use_files2[2]]
except IndexError:
pass
except AttributeError:
pass
## this isn't very flexible:
# retain <sample>__<genome> from pairname:
# pairname == <sample>__<genome>_<start>-<end>+<padding>
# and replace with multiregion
folder = '{}__{}_{}'.format(
pairname.split('__')[0],
pairname.split('__')[1].split('_')[0], 'multi_region')
this_output_path = _os.path.sep.join(output_folder + [folder])
if not _os.path.exists(this_output_path):
_os.makedirs(this_output_path)
cmd += ['-o', this_output_path]
cmd += ['--threads', str(max_processes)]
cmd += ['--memory', str(mem_num_gigs)]
if only_assembler:
cmd += ['--only-assembler']
if careful:
cmd += ['--careful']
thetime = _time.asctime(_time.localtime(_time.time()))
print('about to launch SPAdes . . . at {}'.format(thetime))
print(' '.join(cmd))
contigs['multi_region'] = run_SPAdes(cmd)
else:
start_time = _time.time()
# prepare commandline and launch each SPAdes assembly
contigs = {}
for cnum, (pairname,
files) in enumerate(sorted(self.read_files.items())):
if isinstance(use_exe, list):
# allow for use of prepended executable with script to run
cmd = list(use_exe)
else:
# or just executable
cmd = [use_exe]
# allow use of tuples or dicts by converting dicts to lists
if isinstance(files, dict):
use_files = []
for k, v in sorted(files.items()):
use_files += [v]
else:
use_files = files
cmd += ['--pe1-1', use_files[0]]
cmd += ['--pe1-2', use_files[1]]
try:
# use unpaired reads if available
cmd += ['--pe1-s', use_files[2]]
except IndexError:
pass
try:
# add a second library if provided
if isinstance(self.read_files2[pairname], dict):
# if a dict supplied, make it a list
use_files2 = []
for k, v in sorted(self.read_files2[pairname].items()):
use_files2 += [v]
else:
use_files2 = self.read_files2[pairname]
cmd += ['--pe2-1', use_files2[0]]
cmd += ['--pe2-2', use_files2[1]]
try:
cmd += ['--pe2-s', use_files2[2]]
except IndexError:
pass
except AttributeError:
pass
this_output_path = _os.path.sep.join(output_folder +
[pairname])
if not _os.path.exists(this_output_path):
_os.makedirs(this_output_path)
cmd += ['-o', this_output_path]
cmd += ['--threads', str(max_processes)]
cmd += ['--memory', str(mem_num_gigs)]
if only_assembler:
cmd += ['--only-assembler']
if careful:
cmd += ['--careful']
thetime = _time.asctime(_time.localtime(_time.time()))
print('about to launch SPAdes . . . at {}'.format(thetime))
print(' '.join(cmd))
contigs[pairname] = run_SPAdes(cmd)
if len(self.read_files) > 1:
# report durations, time left etc
_report_time(start_time, cnum, len(self.read_files))
self.paths_to_contigs = contigs
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 SPAdes(self,
exe = [],
output_folder = ['assemblies','SPAdes'],
mem_num_gigs = 8,
max_cpus = -1,
single_assembly = False,
careful = True,
only_assembler = False):
'''
de novo assembly of short reads using SPAdes
By default, the provided short reads in dictionary: self.paths_to_reads
will be assembled separately, unless single_assembly set to True in
which case each set of paired read fastq files will be used in a
single assembly.
http://spades.bioinf.spbau.ru/release3.6.1/manual.html
relevent inputs:
-o <output_dir> Specify the output directory. Required option.
--sc required for MDA (single-cell) data.
--only-error-correction
--only-assembler
--careful reduce the number of mismatches and short indels. Run MismatchCorrector – a post processing tool. Recommended.
--continue from the specified output folder starting from the last available check-point
--restart-from <check_point>
ec start from error correction
as restart assembly module from the first iteration
k<int> restart from the iteration with specified k values, e.g. k55
mc restart mismatch correction
--pe1-12 <file_name> interlaced forward and reverse paired-end reads.
--pe1-1 <file_name> File with forward reads.
--pe1-2 <file_name> File with reverse reads.
--pe1-s <file_name> File with unpaired reads . . use --pe2-... for next library
--threads <int>
--memory <int> max memory in Gb
-k <int,int,...> Comma-separated list of odd ascending k-mers
If --sc is set the default value are 21,33,55, for multicell data sets it is auto
--cov-cutoff <float> positive float value, or 'auto', or 'off'. Default value is 'off'
'''
assert isinstance(output_folder, list), 'Provide output folder as list of folders forming path'
base_output_path = _os.path.sep.join(output_folder)
if not _os.path.exists(base_output_path):
_os.makedirs(base_output_path)
# max threads is slightly different to cpus
# . . can probably use more
max_processes = _decide_max_processes( max_cpus )
# if an exe is not provided, use that stored in Dependencies
if len(exe):
use_exe = _os.path.sep.join(exe)
else:
from baga import Dependencies
use_exe = _get_exe_path('spades')
def run_SPAdes(cmd):
proc = _subprocess.Popen(cmd, stdout=_subprocess.PIPE, stderr=_subprocess.PIPE)
# allow for failed SPAdes runs (possibly caused by small fastq files) <== but also check they were actually built properly
try:
stdout_value, stderr_value = proc.communicate()
checkthese = []
getline = False
for line in stdout_value.split('\n'):
if 'Warnings saved to' in line:
getline = False
if getline:
l = line.rstrip()
if len(l):
checkthese += [l]
if 'SPAdes pipeline finished WITH WARNINGS!' in line:
getline = True
if len(checkthese):
print('SPAdes completed with warnings:\n{}\n'.format('\n'.join(checkthese)))
else:
print('SPAdes completed without warnings')
# with open('___SPAdes_{}_good_{}.log'.format(cnum, thetime), 'w') as fout:
# fout.write(stdout_value)
path2contigs = _os.path.sep.join([this_output_path,'contigs.fasta'])
except _subprocess.CalledProcessError as e:
print('SPAdes probably did not complete: error returned ({})'.format(proc.returncode))
print('Error: {}'.format(e))
print('Writing some info relevent to SPAdes crash to ___SPAdes_{}_bad_{}.log'.format(cnum, thetime))
with open('___SPAdes_{}_bad_{}.log'.format(cnum, thetime), 'w') as fout:
fout.write(dir(proc))
fout.write('\n' + str(e.returncode) + '\n')
fout.write(_os.path.sep.join([this_output_path,'contigs.fasta']))
path2contigs = None
return(path2contigs)
if isinstance(use_exe, list):
# allow for use of prepended executable with script to run
cmd = list(use_exe)
else:
# or just executable
cmd = [use_exe]
contigs = {}
if single_assembly:
print('Combining reads aligned at multiple regions into single assembly')
if isinstance(use_exe, list):
# allow for use of prepended executable with script to run
cmd = list(use_exe)
else:
# or just executable
cmd = [use_exe]
for cnum, (pairname, files) in enumerate(self.read_files.items()):
# allow use of tuples or dicts by converting dicts to lists
if isinstance(files, dict):
use_files = []
for k,v in sorted(files.items()):
use_files += [v]
else:
use_files = files
cmd += ['--pe{}-1'.format(cnum+1), use_files[0]]
cmd += ['--pe{}-2'.format(cnum+1), use_files[1]]
try:
# use unpaired reads if available
cmd += ['--pe{}-s'.format(cnum+1), use_files[2]]
except IndexError:
pass
try:
# add a second library if provided
if isinstance(self.read_files2[pairname], dict):
# if a dict supplied, make it a list
use_files2 = []
for k,v in sorted(self.read_files2[pairname].items()):
use_files2 += [v]
else:
use_files2 = self.read_files2[pairname]
cmd += ['--pe{}-1'.format(cnum+2), use_files2[0]]
cmd += ['--pe{}-2'.format(cnum+2), use_files2[1]]
try:
cmd += ['--pe{}-s'.format(cnum+2), use_files2[2]]
except IndexError:
pass
except AttributeError:
pass
## this isn't very flexible:
# retain <sample>__<genome> from pairname:
# pairname == <sample>__<genome>_<start>-<end>+<padding>
# and replace with multiregion
folder = '{}__{}_{}'.format(pairname.split('__')[0],
pairname.split('__')[1].split('_')[0],
'multi_region')
this_output_path = _os.path.sep.join(output_folder + [folder])
if not _os.path.exists(this_output_path):
_os.makedirs(this_output_path)
cmd += ['-o', this_output_path]
cmd += ['--threads', str(max_processes)]
cmd += ['--memory', str(mem_num_gigs)]
if only_assembler:
cmd += ['--only-assembler']
if careful:
cmd += ['--careful']
thetime = _time.asctime( _time.localtime(_time.time()) )
print('about to launch SPAdes . . . at {}'.format(thetime))
print(' '.join(cmd))
contigs['multi_region'] = run_SPAdes(cmd)
else:
start_time = _time.time()
# prepare commandline and launch each SPAdes assembly
contigs = {}
for cnum, (pairname, files) in enumerate(sorted(self.read_files.items())):
if isinstance(use_exe, list):
# allow for use of prepended executable with script to run
cmd = list(use_exe)
else:
# or just executable
cmd = [use_exe]
# allow use of tuples or dicts by converting dicts to lists
if isinstance(files, dict):
use_files = []
for k,v in sorted(files.items()):
use_files += [v]
else:
use_files = files
cmd += ['--pe1-1', use_files[0]]
cmd += ['--pe1-2', use_files[1]]
try:
# use unpaired reads if available
cmd += ['--pe1-s', use_files[2]]
except IndexError:
pass
try:
# add a second library if provided
if isinstance(self.read_files2[pairname], dict):
# if a dict supplied, make it a list
use_files2 = []
for k,v in sorted(self.read_files2[pairname].items()):
use_files2 += [v]
else:
use_files2 = self.read_files2[pairname]
cmd += ['--pe2-1', use_files2[0]]
cmd += ['--pe2-2', use_files2[1]]
try:
cmd += ['--pe2-s', use_files2[2]]
except IndexError:
pass
except AttributeError:
pass
this_output_path = _os.path.sep.join(output_folder + [pairname])
if not _os.path.exists(this_output_path):
_os.makedirs(this_output_path)
cmd += ['-o', this_output_path]
cmd += ['--threads', str(max_processes)]
cmd += ['--memory', str(mem_num_gigs)]
if only_assembler:
cmd += ['--only-assembler']
if careful:
cmd += ['--careful']
thetime = _time.asctime( _time.localtime(_time.time()) )
print('about to launch SPAdes . . . at {}'.format(thetime))
print(' '.join(cmd))
contigs[pairname] = run_SPAdes(cmd)
if len(self.read_files) > 1:
# report durations, time left etc
_report_time(start_time, cnum, len(self.read_files))
self.paths_to_contigs = contigs
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
