def find_bams(args):
bam_by_sample = OrderedDict()
bad_bam_fpaths = []
good_args = []
for arg in args:
# /ngs/oncology/Analysis/bioscience/Bio_0038_KudosCellLinesExomes/Bio_0038_150521_D00443_0159_AHK2KTADXX/bcbio,Kudos159 /ngs/oncology/Analysis/bioscience/Bio_0038_KudosCellLinesExomes/Bio_0038_150521_D00443_0160_BHKWMNADXX/bcbio,Kudos160
fpath = arg.split(',')[0]
fname, ext = splitext(fpath)
if ext == '.bam':
bam_fpath = verify_bam(fpath)
if not bam_fpath:
bad_bam_fpaths.append(fpath)
else:
if len(arg.split(',')) > 1:
sname = arg.split(',')[1]
else:
sname = basename(splitext(bam_fpath)[0])
bam_by_sample[sname] = bam_fpath
good_args.append(arg)
if bad_bam_fpaths:
critical('BAM files cannot be found, empty or not BAMs: ' +
', '.join(bad_bam_fpaths))
for arg in good_args:
args.remove(arg)
return bam_by_sample
def read_samples(args):
bam_by_sample = find_bams(args)
if bam_by_sample:
info('Found ' + str(len(bam_by_sample)) + ' BAM file' +
('s' if len(bam_by_sample) > 1 else ''))
input_not_bam = [
verify_file(fpath) for fpath in args
if adjust_path(fpath) not in bam_by_sample
]
input_not_bam = [fpath for fpath in input_not_bam if fpath]
fastqs_by_sample = dict()
if not input_not_bam and not bam_by_sample:
critical('No correct input files')
if input_not_bam:
info('Input ' + str(len(input_not_bam)) +
' correct input non-BAM files')
fastqs_by_sample = find_fastq_pairs(input_not_bam)
if fastqs_by_sample:
info('Found ' + str(len(fastqs_by_sample)) + ' FastQ pairs')
intersection = set(fastqs_by_sample.keys()) & set(bam_by_sample.keys())
if intersection:
critical('The following samples both had input BAMs and FastQ: ' +
', '.join(list(intersection)))
return fastqs_by_sample, bam_by_sample
def get_chrom_lengths(genome=None, fai_fpath=None):
assert genome or fai_fpath, f'One of genome or fai_fpath should be not None: genome={genome}, fai_fpath={fai_fpath}'
if not fai_fpath:
check_genome(genome)
fai_fpath = get_fai(genome)
else:
fai_fpath = verify_file(fai_fpath, is_critical=True)
if not fai_fpath.endswith('.fai') and not fai_fpath.endswith('.fa'):
critical('Error: .fai or .fa is accepted.')
chr_lengths = []
if fai_fpath.endswith('.fa'):
debug('Reading genome sequence (.fa) to get chromosome lengths')
with open(fai_fpath, 'r') as handle:
from Bio import SeqIO
reference_records = SeqIO.parse(handle, 'fasta')
for record in reference_records:
chrom = record.id
chr_lengths.append((chrom, len(record.seq)))
else:
debug('Reading genome index file (.fai) to get chromosome lengths')
with open(fai_fpath, 'r') as handle:
for line in handle:
line = line.strip()
if line:
chrom, length = line.split()[0], int(line.split()[1])
chr_lengths.append((chrom, length))
return chr_lengths
def safe_mkdir(dirpath, descriptive_name=''):
""" Multiprocessing-safely and recursively creates a directory
"""
if not dirpath:
critical(f'Path is empty: {descriptive_name if descriptive_name else ""}')
if isdir(dirpath):
return dirpath
if isfile(dirpath):
critical(descriptive_name + ' ' + dirpath + ' is a file.')
num_tries = 0
max_tries = 10
while not exists(dirpath):
# we could get an error here if multiple processes are creating
# the directory at the same time. Grr, concurrency.
try:
os.makedirs(dirpath)
except OSError as e:
if num_tries > max_tries:
raise
num_tries += 1
time.sleep(2)
return dirpath
def _set_date_dir(bcbio_cnf, final_dir, date_dir, create_dir=False, silent=False):
if not date_dir:
fc_date = bcbio_cnf.get('fc_date')
fc_name = bcbio_cnf.get('fc_name') or 'project'
if fc_date:
# Date dirpath is from bcbio and named after fc_name, not our own project name
date_dir = join(final_dir, fc_date + '_' + fc_name)
if not create_dir and not verify_dir(date_dir, silent=True):
critical('Error: no project directory of format {fc_date}_{fc_name} or {fc_name}_{fc_date}')
else:
if isdir(join(final_dir, 'project')): # bcbio-CWL?
date_dir = join(final_dir, 'project')
if not silent: info('Using the datestamp dir from bcbio-CWL: ' + date_dir)
else:
regexs = [fr'^\d\d\d\d-[01][0-9]-[0-3][0-9]_{fc_name}']
date_dirs = [join(final_dir, dirpath)
for dirpath in listdir(final_dir)
if any(re.match(regex, dirpath) for regex in regexs)]
if len(date_dirs) == 0:
raise NoDateStampsException('Error: no datestamp directory!')
elif len(date_dirs) == 1:
date_dir = date_dirs[0]
else:
dates = [(tuple(map(int, basename(d).split('_')[0].split('-'))), d) for d in date_dirs]
newest_date, newest_dir = sorted(dates, reverse=True)[0]
newest_dirs = [d_dir for d_dir in date_dirs if d_dir == newest_dir]
if len(newest_dirs) > 1:
raise MultipleDateStampsException(f'Error: multiple datestamp directory found, '
f'and can\'t select the most recent one because there are multiple latest dirs: {newest_dirs}')
date_dir = newest_dirs[0]
if not silent: info('Using the datestamp dir: ' + date_dir)
if create_dir:
safe_mkdir(date_dir)
return date_dir
def main():
options = [
(['-g', '--genome'], dict(
dest='genome',
help='Genome build. Accepted values: ' + ', '.join(ba.SUPPORTED_GENOMES),
)),
]
parser = OptionParser()
for args, kwargs in options:
parser.add_option(*args, **kwargs)
opts, args = parser.parse_args()
if not opts.genome:
critical('Error: please, specify genome build name with -g (e.g. `-g hg19`)')
genome = opts.genome
debug('Getting features from storage')
features_bed = ba.get_all_features(genome)
if features_bed is None:
critical('Genome ' + genome + ' is not supported. Supported: ' + ', '.join(ba.SUPPORTED_GENOMES))
info('Extracting features from Ensembl GTF')
features_bed = features_bed.filter(lambda x: x[ba.BedCols.FEATURE] == 'CDS')
features_bed = features_bed.filter(ba.get_only_canonical_filter(genome))
info('Saving CDS regions...')
output_fpath = adjust_path(join(dirname(__file__), pardir, genome, 'bed', 'CDS-canonical.bed'))
with file_transaction(None, output_fpath) as tx:
features_bed.cut(range(6)).saveas(tx)
info('Done, saved to ' + output_fpath)
def extract_features(output_file, genome, only_canonical, high_confidence, coding_only,
feature_types):
""" For debug purposes
"""
debug('Getting features from storage')
features_bed = ba.get_all_features(genome)
if features_bed is None:
critical('Genome ' + genome + ' is not supported. Supported: ' + ', '.join(ba.SUPPORTED_GENOMES))
if high_confidence:
features_bed = features_bed.filter(ba.high_confidence_filter)
if only_canonical:
features_bed = features_bed.filter(ba.get_only_canonical_filter(genome))
if coding_only:
features_bed = features_bed.filter(ba.protein_coding_filter)
# unique_tx_by_gene = find_best_tx_by_gene(features_bed)
info('Extracting features from Ensembl GTF')
feature_types = feature_types or ['exon', 'CDS', 'stop_codon', 'transcript']
features_bed = features_bed.filter(lambda x: x[ba.BedCols.FEATURE] in feature_types)
# x[ebl.BedCols.ENSEMBL_ID] == unique_tx_by_gene[x[ebl.BedCols.GENE]])
info('Overlapping regions with Ensembl data')
features_bed.saveas(output_file)
debug(f'Saved features to {output_file}')
def _set_date_dir(bcbio_cnf, final_dir, date_dir, create_dir=False, silent=False):
if not date_dir:
fc_date = bcbio_cnf.get('fc_date')
fc_name = bcbio_cnf.get('fc_name') or 'project'
if fc_date:
# Date dirpath is from bcbio and named after fc_name, not our own project name
date_dir = join(final_dir, fc_date + '_' + fc_name)
if not create_dir and not verify_dir(date_dir, silent=True):
critical('Error: no project directory of format {fc_date}_{fc_name} or {fc_name}_{fc_date}')
else:
if isdir(join(final_dir, 'project')): # bcbio-CWL?
date_dir = join(final_dir, 'project')
if not silent: info('Using the datestamp dir from bcbio-CWL: ' + date_dir)
else:
regexs = [fr'^\d\d\d\d-[01][0-9]-[0-3][0-9]_{fc_name}']
date_dirs = [join(final_dir, dirpath)
for dirpath in listdir(final_dir)
if any(re.match(regex, dirpath) for regex in regexs)]
if len(date_dirs) == 0:
raise NoDateStampsException('Error: no datestamp directory!')
elif len(date_dirs) == 1:
date_dir = date_dirs[0]
else:
dates = [(tuple(map(int, basename(d).split('_')[0].split('-'))), d) for d in date_dirs]
newest_date, newest_dir = sorted(dates, reverse=True)[0]
newest_dirs = [d_dir for d_dir in date_dirs if d_dir == newest_dir]
if len(newest_dirs) > 1:
raise MultipleDateStampsException(f'Error: multiple datestamp directory found, '
f'and can\'t select the most recent one because there are multiple latest dirs: {newest_dirs}')
date_dir = newest_dirs[0]
if not silent: info('Using the datestamp dir: ' + date_dir)
if create_dir:
safe_mkdir(date_dir)
return date_dir
def safe_mkdir(dirpath, descriptive_name=''):
""" Multiprocessing-safely and recursively creates a directory
"""
if not dirpath:
critical(f'Path is empty: {descriptive_name if descriptive_name else ""}')
if isdir(dirpath):
return dirpath
if isfile(dirpath):
critical(descriptive_name + ' ' + dirpath + ' is a file.')
num_tries = 0
max_tries = 10
while not exists(dirpath):
# we could get an error here if multiple processes are creating
# the directory at the same time. Grr, concurrency.
try:
os.makedirs(dirpath)
except OSError as e:
if num_tries > max_tries:
raise
num_tries += 1
time.sleep(2)
return dirpath
def get_or_create_run(projects, parall_view=None):
genomes = set([p.genome for p in projects])
if len(genomes) > 1:
log.critical('Error: multiple genomes in projects: ' + str(genomes))
run = Run.find_by_projects(projects)
if run and run.rerun_on_usercall:
log.info()
log.info('Rebuilding tree on usercall')
build_tree(run)
run.rerun_on_usercall = False
db.session.commit()
return run
if run and not Run.is_ready(run):
log.debug('Tree files do not exist, recreating run for projects ' +
', '.join(p.name for p in projects))
db.session.delete(run)
db.session.commit()
run = None
if run:
log.debug('Found run for ' + ', '.join([p.name for p in projects]) +
' with ID ' + str(run.id))
else:
log.debug('Creating new run for projects ' +
', '.join(p.name for p in projects))
run = Run.create(projects, parall_view)
log.debug('Done creating new run with ID ' + str(run.id))
return run
def tx_tmpdir(base_dir, rollback_dirpath):
"""Context manager to create and remove a transactional temporary directory.
"""
# tmp_dir_base = join(base_dir, 'tx', str(uuid.uuid4()))
# unique_attempts = 0
# while os.path.exists(tmp_dir_base):
# if unique_attempts > 5:
# break
# tmp_dir_base = join(base_dir, 'tx', str(uuid.uuid4()))
# time.sleep(1)
# unique_attempts += 1
# if base_dir is not None:
# tmp_dir_base = os.path.join(base_dir, "tx")
# else:
# tmp_dir_base = os.path.join(os.getcwd(), "tx")
if exists(rollback_dirpath):
critical(rollback_dirpath + ' already exists')
tmp_dir = tempfile.mkdtemp(dir=base_dir)
safe_mkdir(tmp_dir)
try:
yield tmp_dir
finally:
if tmp_dir and exists(tmp_dir):
os.rename(tmp_dir, rollback_dirpath)
def get_ref_fasta(genome):
if is_az():
path = '/ngs/reference_data/genomes/Hsapiens/' + genome + '/seq/' + genome + '.fa'
if isfile(path):
logger.info('Found genome fasta at ' + path)
return path
if isdir(join(DATA_DIR, 'genomes', genome)):
genome_dir = safe_mkdir(join(DATA_DIR, 'genomes'))
else:
genome_dir = safe_mkdir(join(DATA_DIR, '..', 'genomes'))
if genome not in genomepy.list_installed_genomes(genome_dir):
genome_rec = [
rec for rec in genomepy.list_available_genomes()
if rec[1] == genome
]
if genome_rec:
genome_rec = genome_rec[0]
else:
logger.critical('Error: genome ' + genome + ' is not available')
logger.info('Downloading genome ' + genome + ' from ' + genome_rec[1] +
' and installing into ' + genome_dir)
genomepy.install_genome(genome, 'UCSC', genome_dir=genome_dir)
genome_fasta_file = genomepy.Genome(genome, genome_dir=genome_dir).filename
return genome_fasta_file
def _set_name_and_paths(self, name, variantcallers_data, ensemble=False, silent=False):
self.raw_name = name
self.name = self.raw_name.replace('.', '_')
self.dirpath = verify_dir(join(self.bcbio_project.final_dir, self.name))
if not verify_dir(self.dirpath, silent=silent):
if not silent:
critical(f'Sample "{self.name}" specified in bcbio YAML is not found in the final directory '
f'{self.bcbio_project.final_dir}. Please check consistency between the YAML '
f'{self.bcbio_project.bcbio_yaml_fpath} and the directories in `final`: '
f'to every "description" value in YAML, there should be a corresponding folder with the '
f'same name in `final`. You can use `-e` option to exclude samples (comma-separated) '
f'from consideration, if you are sure that missing folders are expected.')
else:
return False
self.var_dirpath = join(self.dirpath, BcbioProject.var_dir)
self.bam = self.find_bam(silent=silent)
if self.is_rnaseq:
gene_counts = adjust_path(join(self.dirpath, self.get_name_for_files() + '-ready.counts'))
if isfile(gene_counts) and verify_file(gene_counts):
self.counts_file = gene_counts
else:
if not silent: warn('Counts for ' + self.name + ' not found')
else:
if variantcallers_data:
self._set_variant_files(variantcallers_data, ensemble=ensemble)
else:
if not silent: warn('No variant callers set in config, skipping finding VCF files')
return True
def update_batches(samples, silent=False):
batch_by_name = {bn: Batch(bn) for bn in list(set([b for s in samples for b in s.batch_names]))}
for sample in samples:
for bn in sample.batch_names:
batch_by_name[bn].name = bn
sample.batch = batch_by_name[bn]
if sample.phenotype == 'normal':
if batch_by_name[bn].normal:
critical('Multiple normal samples for batch ' + bn)
batch_by_name[bn].normal = sample
else:
batch_by_name[bn].tumor = sample
for batch in batch_by_name.values():
if batch.normal and not batch.tumor:
if not silent: info('Batch ' + batch.name + ' contains only normal, treating sample ' + batch.normal.name + ' as tumor')
batch.normal.phenotype = 'tumor'
batch.normal.batch = batch
batch.tumor = batch.normal
batch.normal = None
# setting up batch properties
for b in batch_by_name.values():
b.tumor.normal_match = b.normal
return batch_by_name
def find_bams(args):
bam_by_sample = OrderedDict()
bad_bam_fpaths = []
good_args = []
for arg in args:
# /ngs/oncology/Analysis/bioscience/Bio_0038_KudosCellLinesExomes/Bio_0038_150521_D00443_0159_AHK2KTADXX/bcbio,Kudos159 /ngs/oncology/Analysis/bioscience/Bio_0038_KudosCellLinesExomes/Bio_0038_150521_D00443_0160_BHKWMNADXX/bcbio,Kudos160
fpath = arg.split(',')[0]
fname, ext = splitext(fpath)
if ext == '.bam':
bam_fpath = verify_bam(fpath)
if not bam_fpath:
bad_bam_fpaths.append(fpath)
else:
if len(arg.split(',')) > 1:
sname = arg.split(',')[1]
else:
sname = basename(splitext(bam_fpath)[0])
bam_by_sample[sname] = bam_fpath
good_args.append(arg)
if bad_bam_fpaths:
critical('BAM files cannot be found, empty or not BAMs: ' + ', '.join(bad_bam_fpaths))
for arg in good_args:
args.remove(arg)
return bam_by_sample
def tx_tmpdir(base_dir, rollback_dirpath):
"""Context manager to create and remove a transactional temporary directory.
"""
# tmp_dir_base = join(base_dir, 'tx', str(uuid.uuid4()))
# unique_attempts = 0
# while os.path.exists(tmp_dir_base):
# if unique_attempts > 5:
# break
# tmp_dir_base = join(base_dir, 'tx', str(uuid.uuid4()))
# time.sleep(1)
# unique_attempts += 1
# if base_dir is not None:
# tmp_dir_base = os.path.join(base_dir, "tx")
# else:
# tmp_dir_base = os.path.join(os.getcwd(), "tx")
if exists(rollback_dirpath):
critical(rollback_dirpath + ' already exists')
tmp_dir = tempfile.mkdtemp(dir=base_dir)
safe_mkdir(tmp_dir)
try:
yield tmp_dir
finally:
if tmp_dir and exists(tmp_dir):
os.rename(tmp_dir, rollback_dirpath)
def make_cluster_cmdl(log_dir, refdata, app_name, cluster_submit_cmd=None):
""" Generates cluster command line parameters for snakemake
"""
if not cluster_submit_cmd and not refdata.cluster_cmd:
logger.critical(f'Automatic cluster submission '
f'is not supported for the machine "{refdata.name}". '
f'Use exclicit --cluster-cmd')
if not cluster_submit_cmd:
cluster_submit_cmd = refdata.cluster_cmd
# Replacing the curly braces to avoid confusing snakemake formatter which for some reason triggers
cluster_submit_cmd = cluster_submit_cmd.replace('{', '[').replace('}', ']')
cluster_submitter = get_submit_script()
timestamp = datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
from reference_data import api as refdata
cluster_cmdl = \
f' --cluster "{cluster_submitter} {timestamp} {log_dir} {app_name} ' \
f'\'{cluster_submit_cmd}\'"'
# Also overriding jobscript?
jobscript = refdata.cluster_jobscript
if jobscript:
safe_mkdir(log_dir)
jobscript_file = join(log_dir, 'jobscript.sh')
with open(jobscript_file, 'w') as f_out:
f_out.write(jobscript.replace('{path}', os.environ["PATH"]))
cluster_cmdl += f' --jobscript "{jobscript_file}"'
return cluster_cmdl
def _get(relative_path, genome=None):
"""
:param relative_path: relative path of the file inside the repository
:param genome: genome name. Can contain chromosome name after comma, like hg19-chr20,
in case of BED, the returning BedTool will be with added filter.
:return: BedTools object if it's a BED file, or filepath
"""
chrom = None
if genome:
if '-chr' in genome:
genome, chrom = genome.split('-')
check_genome(genome)
relative_path = relative_path.format(genome=genome)
path = abspath(join(dirname(__file__), relative_path))
if not isfile(path) and isfile(path + '.gz'):
path += '.gz'
if path.endswith('.bed') or path.endswith('.bed.gz'):
if path.endswith('.bed.gz'):
bedtools = which('bedtools')
if not bedtools:
critical('bedtools not found in PATH: ' + str(os.environ['PATH']))
debug('BED is compressed, creating BedTool')
bed = BedTool(path)
else:
debug('BED is uncompressed, creating BedTool')
bed = BedTool(path)
if chrom:
debug('Filtering BEDTool for chrom ' + chrom)
bed = bed.filter(lambda r: r.chrom == chrom)
return bed
else:
return path
def sort_bed_gsort(input_bed_fpath,
output_bed_fpath=None,
work_dir=None,
fai_fpath=None,
genome=None):
input_bed_fpath = verify_bed(input_bed_fpath, is_critical=True)
output_bed_fpath = adjust_path(output_bed_fpath) if output_bed_fpath \
else intermediate_fname(work_dir, input_bed_fpath, 'sorted')
debug('Sorting regions in ' + str(input_bed_fpath))
if can_reuse(output_bed_fpath, input_bed_fpath):
debug(output_bed_fpath + ' exists, reusing')
return output_bed_fpath
if fai_fpath:
fai_fpath = verify_file(fai_fpath)
elif genome:
fai_fpath = verify_file(ref.get_fai(genome))
else:
critical('Either of fai_fpath or genome build name must be specified')
with file_transaction(work_dir, output_bed_fpath) as tx:
run('gsort {input_bed_fpath} {fai_fpath}'.format(**locals()),
output_fpath=tx)
return output_bed_fpath
def _set_name_and_paths(self, name, variantcallers_data, ensemble=False, silent=False):
self.raw_name = name
self.name = self.raw_name.replace('.', '_')
self.rgid = self.name
self.dirpath = verify_dir(join(self.parent_project.final_dir, self.name))
if not verify_dir(self.dirpath, silent=silent):
critical(f'Sample "{self.name}" specified in bcbio YAML is not found in the final directory '
f'{self.parent_project.final_dir}. Please check consistency between the YAML '
f'{self.parent_project.bcbio_yaml_fpath} and the directories in `final`: '
f'to every "description" value in YAML, there should be a corresponding folder with the '
f'same name in `final`. You can use `-e` option to exclude samples (comma-separated) '
f'from consideration, if you are sure that missing folders are expected.')
self.bam = self.find_bam(silent=silent)
if self.is_rnaseq:
gene_counts = adjust_path(join(self.dirpath, self.get_name_for_files() + '-ready.counts'))
if isfile(gene_counts) and verify_file(gene_counts):
self.counts_file = gene_counts
else:
if not silent: warn('Counts for ' + self.name + ' not found')
else:
if variantcallers_data:
self._set_variant_callers(variantcallers_data, ensemble=ensemble)
else:
if not silent: warn('No variant callers set in config, skipping finding VCF files')
def cnv_to_bed(cnv_path, out_bed_path):
with open(cnv_path) as fh:
parse_fn = None
header = next(fh).strip().split('\t')
if header[0].startswith('##fileformat=VCF'):
# Manta
info(f'Detected {cnv_path} as caller "manta"')
parse_fn = iter_manta
else:
for caller, hdr in header_by_caller.items():
if header == hdr:
print(
f'Parsing {cnv_path} as caller "{caller}" with header {hdr}'
)
parse_fn = get_iter_cnv(header,
parse_row_by_caller[caller])
if not parse_fn:
critical(f'Cannot detect CNV file format in {cnv_path}')
with open(out_bed_path, 'w') as out:
writer = csv.writer(out, delimiter='\t')
for i, call in enumerate(parse_fn(cnv_path)):
if call:
bed_row = call.get_bed_raw()
writer.writerow(bed_row)
if i == 0:
print(bed_row)
print('')
def get_chrom_lengths(genome=None, fai_fpath=None):
assert genome or fai_fpath, f'One of genome or fai_fpath should be not None: genome={genome}, fai_fpath={fai_fpath}'
if not fai_fpath:
check_genome(genome)
fai_fpath = get_fai(genome)
else:
fai_fpath = verify_file(fai_fpath, is_critical=True)
if not fai_fpath.endswith('.fai') and not fai_fpath.endswith('.fa'):
critical('Error: .fai or .fa is accepted.')
chr_lengths = []
if fai_fpath.endswith('.fa'):
debug('Reading genome sequence (.fa) to get chromosome lengths')
with open(fai_fpath, 'r') as handle:
from Bio import SeqIO
reference_records = SeqIO.parse(handle, 'fasta')
for record in reference_records:
chrom = record.id
chr_lengths.append((chrom, len(record.seq)))
else:
debug('Reading genome index file (.fai) to get chromosome lengths')
with open(fai_fpath, 'r') as handle:
for line in handle:
line = line.strip()
if line:
chrom, length = line.split()[0], int(line.split()[1])
chr_lengths.append((chrom, length))
return chr_lengths
def lift_over(fpath, from_genome, to_genome):
chain_file = join(dirname(__file__), 'over.chain', f'{from_genome}To{to_genome.title()}.over.chain.gz')
if not verify_file(chain_file):
log.critical(f'Error: conversion from {from_genome} to {to_genome} is not supported!')
out_fpath = add_suffix(fpath, to_genome)
call_process.run(f'liftOver {fpath} {chain_file} {out_fpath} {out_fpath}.unMapped')
return out_fpath
def pair_dragen_directories(paths):
# DRAGEN tumor/normal and normal directories are paired on the basis of the normal sample
# name.
#
# Tumor and normal sample names are extracted from the BAM header. Specifically, the BAM
# sample name is retrieved from the 'SM' (sample) field of the '@RG' (read group) header line.
#
# Tumor or normal identity of a sample is inferred from the BAM filename: if a BAM filename
# contains the '_tumor.bam' suffix then it and the sample name is set as the tumor, otherwise
# set as the normal sample.
#
# The subject identifier is from the DRAGEN output directory name.
#
# Assumes a one-to-one pairing for DRAGEN tumor/normal and normal output directories i.e. no
# multiple tumor/normal runs to a single normal run.
# Sort paths by normal sample name so that normal and tumor/normal are placed together
paths_sorted = dict()
for path in paths:
dir_type = 'tumor_normal_run' if is_dragen_tumor_normal_directory(
path) else 'normal_run'
samples = get_samples_from_dragen_dir_bams(path)
# Ensure we have found normal names
if 'normal' not in samples:
critical(
f'Could not find normal sample name for DRAGEN directory {path}'
)
# Sort by normal sample name, add path, subject ID to stored data
sample_normal = samples['normal']
if sample_normal not in paths_sorted:
paths_sorted[sample_normal] = dict()
assert dir_type not in paths_sorted[sample_normal]
paths_sorted[sample_normal][dir_type] = samples
paths_sorted[sample_normal][dir_type]['path'] = path
paths_sorted[sample_normal][dir_type][
'prefix'] = get_dragen_output_prefix(path)
paths_sorted[sample_normal][dir_type][
'subject_id'] = get_subject_id_from_dragen_dir(path)
# Differentiated paired and unpaired paths
paths_unpaired = list()
paths_paired = list()
for paths in paths_sorted.values():
if 'normal_run' in paths and 'tumor_normal_run' in paths:
# Ensure we have collected only one subject id for this set of inputs
assert len({d['subject_id'] for d in paths.values()}) == 1
paths['subject_id'] = paths['normal_run']['subject_id']
paths_paired.append(paths)
else:
for dir_type, data in paths.items():
paths_unpaired.append((dir_type, data['path']))
# Emit warning for unpaired paths
if paths_unpaired:
paths_unpaired_strs = list()
for dir_type, path in paths_unpaired:
paths_unpaired_strs.append(f'{dir_type}: {path}')
paths_unpaired_str = '\n\t'.join(paths_unpaired_strs)
warn(f'could not pair DRAGEN directories:\n\t{paths_unpaired_str}')
return paths_paired
def is_small_target(bed_file=None):
try: # to allow optional pybedtools
from ngs_utils.bed_utils import get_total_bed_size
except ImportError:
print_exc()
critical('Please, install pybedtools (conda install -c bioconda -y pybedtools)')
else:
return bed_file and isfile(bed_file) and get_total_bed_size(bed_file) < 10 * 1000 * 1000
def determine_sex(work_dir, bam_fpath, avg_depth, genome, target_bed=None):
debug()
debug('Determining sex')
pybedtools.set_tempdir(safe_mkdir(join(work_dir, 'pybedtools_tmp')))
male_bed = None
for k in chry_key_regions_by_genome:
if k in genome:
male_bed = BedTool(chry_key_regions_by_genome.get(k))
break
if not male_bed:
warn('Warning: no male key regions for ' + genome + ', cannot identify sex')
return None
male_area_size = get_total_bed_size(male_bed)
debug('Male region total size: ' + str(male_area_size))
if target_bed:
target_male_bed = join(work_dir, 'male.bed')
with file_transaction(work_dir, target_male_bed) as tx:
BedTool(target_bed).intersect(male_bed).merge().saveas(tx)
target_male_area_size = get_total_bed_size(target_male_bed)
if target_male_area_size == 0:
debug('The male non-PAR region does not overlap with the capture target - cannot determine sex.')
return None
male_bed = target_male_bed
else:
debug('WGS, determining sex based on chrY key regions coverage.')
info('Detecting sex by comparing the Y chromosome key regions coverage and average coverage depth.')
if not bam_fpath:
critical('BAM file is required.')
index_bam(bam_fpath)
chry_mean_coverage = _calc_mean_coverage(work_dir, male_bed, bam_fpath, 1)
debug('Y key regions average depth: ' + str(chry_mean_coverage))
avg_depth = float(avg_depth)
debug('Sample average depth: ' + str(avg_depth))
if avg_depth < AVG_DEPTH_THRESHOLD_TO_DETERMINE_SEX:
debug('Sample average depth is too low (less than ' + str(AVG_DEPTH_THRESHOLD_TO_DETERMINE_SEX) +
') - cannot determine sex')
return None
if chry_mean_coverage == 0:
debug('Y depth is 0 - it\s female')
sex = 'F'
else:
factor = avg_depth / chry_mean_coverage
debug('Sample depth / Y depth = ' + str(factor))
if factor > FEMALE_Y_COVERAGE_FACTOR: # if mean target coverage much higher than chrY coverage
debug('Sample depth is more than ' + str(FEMALE_Y_COVERAGE_FACTOR) + ' times higher than Y depth - it\s female')
sex = 'F'
else:
debug('Sample depth is not more than ' + str(FEMALE_Y_COVERAGE_FACTOR) + ' times higher than Y depth - it\s male')
sex = 'M'
debug('Sex is ' + sex)
debug()
return sex
def calc_genomic_bp_offset(self):
genomic_coord, is_in_intron = FusionSide.offset_to_genome_coord(
self.trx, self.bp_offset)
if genomic_coord is None:
logger.critical(
f' Error: could not convert transcript {id} offest {genomic_coord} to genomic coordinate'
)
return None
return genomic_coord, is_in_intron
def get_dragen_output_prefix(dirpath):
for fp in dirpath.iterdir():
if not fp.match('*replay.json'):
continue
return fp.name.replace('-replay.json', '')
else:
critical(
'could not determine output prefix for DRAGEN directory \'{dirpath}\''
)
def load_yaml_config(fpath):
verify_file(fpath, is_critical=True)
try:
dic = load_yaml(open(fpath))
except Exception:
err(format_exc())
critical('Could not parse bcbio YAML ' + fpath)
else:
return dic
def load_yaml_config(fpath):
verify_file(fpath, is_critical=True)
try:
dic = _load_yaml(fpath)
except Exception:
err(format_exc())
critical('Could not parse bcbio YAML ' + fpath)
else:
return dic
def find_in_log(self, fname, is_critical=False, silent=True):
options = [join(self.log_dir, fname),
join(self.date_dir, fname)]
for fpath in options:
if isfile(fpath):
return fpath
if is_critical:
critical('Log file not found as ' + ', '.join(options))
elif not silent:
err('Log file not found as ' + ', '.join(options))
def secondary_conda_env(env_name='pcgr', is_critical=False):
py_path = sys.executable # e.g. /miniconda/envs/umccrise/bin/python
env_path = dirname(dirname(py_path)) # e.g. /miniconda/envs/umccrise
env_path = env_path + '_' + env_name # e.g. /miniconda/envs/umccrise_pcgr
if not isdir(env_path):
if is_critical:
critical(f'Can\'t find environment {env_path}')
else:
return None
return env_path
def find_in_log(self, fname, is_critical=False, silent=True):
options = [join(self.log_dir, fname),
join(self.date_dir, fname)]
for fpath in options:
if isfile(fpath):
return fpath
if is_critical:
critical('Log file not found as ' + ', '.join(options))
elif not silent:
err('Log file not found as ' + ', '.join(options))
def _translate_from_start_codon(seq, to_stop, name):
""" Seq must start with START. Translates until STOP.
"""
codon_table = CodonTable.unambiguous_dna_by_name['Standard']
if str(seq[:3]).upper() not in codon_table.start_codons:
logger.critical(name + ' expected to start with a START codon: ' +
seq[:3])
pep_5p = _trim3(seq).translate(to_stop=to_stop)
# for the case if the peptide starts with an alternative start codon, replace it with M
return 'M' + pep_5p[1:]
def sort_bed(input_bed_fpath,
output_bed_fpath=None,
work_dir=None,
fai_fpath=None,
chr_order=None,
genome=None):
input_bed_fpath = verify_bed(input_bed_fpath, is_critical=True)
output_bed_fpath = adjust_path(output_bed_fpath) if output_bed_fpath \
else intermediate_fname(work_dir, input_bed_fpath, 'sorted')
debug('Sorting regions in ' + str(input_bed_fpath))
if can_reuse(output_bed_fpath, input_bed_fpath):
debug(output_bed_fpath + ' exists, reusing')
return output_bed_fpath
regions = []
if not chr_order:
if fai_fpath:
fai_fpath = verify_file(fai_fpath)
elif genome:
fai_fpath = verify_file(ref.get_fai(genome))
else:
critical(
'Either of chr_order, fai_fpath, or genome build name must be specified'
)
chr_order = get_chrom_order(fai_fpath=fai_fpath)
with open(input_bed_fpath) as f:
with file_transaction(work_dir, output_bed_fpath) as tx:
with open(tx, 'w') as out:
for l in f:
if not l.strip():
continue
if l.strip().startswith('#'):
out.write(l)
continue
fs = l.strip().split('\t')
chrom = fs[0]
start = int(fs[1])
end = int(fs[2])
other_fields = fs[3:]
order = chr_order.get(chrom, -1)
regions.append(
Region(chrom, start, end, other_fields, order))
for region in sorted(regions, key=lambda r: r.get_key()):
fs = [region.chrom, str(region.start), str(region.end)]
fs.extend(region.other_fields)
out.write('\t'.join(fs) + '\n')
debug('Sorted ' + str(len(regions)) + ' regions, saved to ' +
output_bed_fpath)
return output_bed_fpath
def get_bed_genes(bed_fpath):
""" Returns a list from the 4th column of a bed file
"""
try: # to allow optional pybedtools
from ngs_utils.bed_utils import get_genes_from_bed
except ImportError:
print_exc()
critical('Please, install pybedtools (conda install -c bioconda -y pybedtools)')
else:
gene_set, gene_list = get_genes_from_bed(bed_fpath)
return [gn for gn in gene_list if (gn and gn != '.')]
def is_small_target(bed_file=None):
try: # to allow optional pybedtools
from ngs_utils.bed_utils import get_total_bed_size
except ImportError:
print_exc()
critical(
'Please, install pybedtools (conda install -c bioconda -y pybedtools)'
)
else:
return bed_file and isfile(
bed_file) and get_total_bed_size(bed_file) < 10 * 1000 * 1000
def get_read_group_sample_name(bam_fp):
bam = pysam.AlignmentFile(bam_fp)
header = bam.header.to_dict()
samples = {rg['SM'] for rg in header.get('RG', list())}
if len(samples) == 0:
critical(
f'could not retrieve sample name from the @RG SM field in {bam_fp}'
)
elif len(samples) > 1:
critical('found more than one sample name in the @RG SM fields for '
f'{bam_fp}: {", ".join(samples)}')
return samples.pop()
def get_bed_genes(bed_fpath):
""" Returns a list from the 4th column of a bed file
"""
try: # to allow optional pybedtools
from ngs_utils.bed_utils import get_genes_from_bed
except ImportError:
print_exc()
critical(
'Please, install pybedtools (conda install -c bioconda -y pybedtools)'
)
else:
gene_set, gene_list = get_genes_from_bed(bed_fpath)
return [gn for gn in gene_list if (gn and gn != '.')]
def set_final_dir(bcbio_cnf, config_dir, final_dir=None, create_dir=False):
if final_dir:
return final_dir
elif 'upload' in bcbio_cnf and 'dir' in bcbio_cnf['upload']:
final_dirname = bcbio_cnf['upload']['dir']
final_dir = adjust_path(join(config_dir, final_dirname))
if create_dir: safe_mkdir(final_dir)
verify_dir(final_dir, 'upload directory specified in the bcbio config', is_critical=True)
else:
final_dir = abspath(join(config_dir, pardir, 'final'))
if create_dir: safe_mkdir(final_dir)
if not verify_dir(final_dir):
critical('If final directory it is not named "final", please, specify it in the bcbio config.')
return final_dir
def set_final_dir(bcbio_cnf, config_dir, final_dir=None, create_dir=False):
if final_dir:
return final_dir
elif 'upload' in bcbio_cnf and 'dir' in bcbio_cnf['upload']:
final_dirname = bcbio_cnf['upload']['dir']
final_dir = adjust_path(join(config_dir, final_dirname))
if create_dir: safe_mkdir(final_dir)
verify_dir(final_dir, 'upload directory specified in the bcbio config', is_critical=True)
else:
final_dir = abspath(join(config_dir, pardir, 'final'))
if create_dir: safe_mkdir(final_dir)
if not verify_dir(final_dir):
critical('If final directory it is not named "final", please, specify it in the bcbio config.')
return final_dir
def annotate_target(work_dir, target_bed, genome_build):
output_fpath = intermediate_fname(work_dir, target_bed, 'ann')
if can_reuse(output_fpath, target_bed):
info(output_fpath + ' exists, reusing')
return output_fpath
bed_annotation = which('annotate_bed.py')
if not bed_annotation:
bed_annotation = which('bed_annotation')
critical('Error: bed_annotation not found in PATH, please install `conda install -c vladsaveliev bed_annotation`.')
cmdline = '{bed_annotation} {target_bed} -g {genome_build} -o {output_fpath}'.format(**locals())
run(cmdline, output_fpath, stdout_to_outputfile=False)
output_fpath = clean_bed(output_fpath, work_dir)
return output_fpath
def find_fastq_pairs(fpaths):
info('Finding FastQ pairs...')
fastqs_by_sample_name = dict()
for fpath in fpaths:
fn, ext = splitext_plus(basename(fpath))
if ext in ['.fq', '.fq.gz', '.fastq', '.fastq.gz']:
sname, l_fpath, r_fpath = None, None, None
if fn.endswith('_1'):
sname = fn[:-2]
l_fpath = fpath
if fn.endswith('_R1'):
sname = fn[:-3]
l_fpath = fpath
if fn.endswith('_2'):
sname = fn[:-2]
r_fpath = fpath
if fn.endswith('_R2'):
sname = fn[:-3]
r_fpath = fpath
if sname:
m = re.match(r'(.*)_S\d+', sname)
if m:
sname = m.group(1)
sname = sname.replace('-', '_')
else:
sname = fn
info('Cannot detect file for ' + sname)
l, r = fastqs_by_sample_name.get(sname, (None, None))
if l and l_fpath:
critical('Duplicated left FastQ files for ' + sname + ': ' +
l + ' and ' + l_fpath)
if r and r_fpath:
critical('Duplicated right FastQ files for ' + sname + ': ' +
r + ' and ' + r_fpath)
fastqs_by_sample_name[sname] = l or l_fpath, r or r_fpath
fixed_fastqs_by_sample_name = dict()
for sname, (l, r) in fastqs_by_sample_name.items():
if not l:
err('ERROR: for sample ' + sname + ', left reads not found')
if not r:
err('ERROR: for sample ' + sname + ', right reads not found')
if l and r:
fixed_fastqs_by_sample_name[sname] = l, r
return fixed_fastqs_by_sample_name
def calc_bases_within_threshs(bases_by_depth, total_size, depth_thresholds):
bases_within_threshs = OrderedDict((depth, 0) for depth in depth_thresholds)
rates_within_threshs = OrderedDict((depth, None) for depth in depth_thresholds)
for depth, bases in bases_by_depth.items():
for t in depth_thresholds:
if depth >= t:
bases_within_threshs[t] += bases
for t in depth_thresholds:
bs = bases_within_threshs[t]
if total_size > 0:
rate = 1.0 * bases_within_threshs[t] / total_size
if rate > 1:
critical('Error: rate is > 1: rate = ' + str(rate) + ', bases = ' + str(bs) + ', size = ' + str(total_size))
rates_within_threshs[t] = rate
return bases_within_threshs, rates_within_threshs
def sort_bed(input_bed_fpath, output_bed_fpath=None, work_dir=None, fai_fpath=None, chr_order=None, genome=None):
input_bed_fpath = verify_bed(input_bed_fpath, is_critical=True)
output_bed_fpath = adjust_path(output_bed_fpath) if output_bed_fpath \
else intermediate_fname(work_dir, input_bed_fpath, 'sorted')
debug('Sorting regions in ' + str(input_bed_fpath))
if can_reuse(output_bed_fpath, input_bed_fpath):
debug(output_bed_fpath + ' exists, reusing')
return output_bed_fpath
regions = []
if not chr_order:
if fai_fpath:
fai_fpath = verify_file(fai_fpath)
elif genome:
fai_fpath = verify_file(ref.get_fai(genome))
else:
critical('Either of chr_order, fai_fpath, or genome build name must be specified')
chr_order = get_chrom_order(fai_fpath=fai_fpath)
with open(input_bed_fpath) as f:
with file_transaction(work_dir, output_bed_fpath) as tx:
with open(tx, 'w') as out:
for l in f:
if not l.strip():
continue
if l.strip().startswith('#'):
out.write(l)
continue
fs = l.strip().split('\t')
chrom = fs[0]
start = int(fs[1])
end = int(fs[2])
other_fields = fs[3:]
order = chr_order.get(chrom, -1)
regions.append(Region(chrom, start, end, other_fields, order))
for region in sorted(regions, key=lambda r: r.get_key()):
fs = [region.chrom, str(region.start), str(region.end)]
fs.extend(region.other_fields)
out.write('\t'.join(fs) + '\n')
debug('Sorted ' + str(len(regions)) + ' regions, saved to ' + output_bed_fpath)
return output_bed_fpath
def find_fastq_pairs(fpaths):
info('Finding FastQ pairs...')
fastqs_by_sample_name = dict()
for fpath in fpaths:
fn, ext = splitext_plus(basename(fpath))
if ext in ['.fq', '.fq.gz', '.fastq', '.fastq.gz']:
sname, l_fpath, r_fpath = None, None, None
if fn.endswith('_1'):
sname = fn[:-2]
l_fpath = fpath
if fn.endswith('_R1'):
sname = fn[:-3]
l_fpath = fpath
if fn.endswith('_2'):
sname = fn[:-2]
r_fpath = fpath
if fn.endswith('_R2'):
sname = fn[:-3]
r_fpath = fpath
if sname:
m = re.match(r'(.*)_S\d+', sname)
if m:
sname = m.group(1)
sname = sname.replace('-', '_')
else:
sname = fn
info('Cannot detect file for ' + sname)
l, r = fastqs_by_sample_name.get(sname, (None, None))
if l and l_fpath:
critical('Duplicated left FastQ files for ' + sname + ': ' + l + ' and ' + l_fpath)
if r and r_fpath:
critical('Duplicated right FastQ files for ' + sname + ': ' + r + ' and ' + r_fpath)
fastqs_by_sample_name[sname] = l or l_fpath, r or r_fpath
fixed_fastqs_by_sample_name = dict()
for sname, (l, r) in fastqs_by_sample_name.items():
if not l:
err('ERROR: for sample ' + sname + ', left reads not found')
if not r:
err('ERROR: for sample ' + sname + ', right reads not found')
if l and r:
fixed_fastqs_by_sample_name[sname] = l, r
return fixed_fastqs_by_sample_name
def main():
options = [
(['-g', '--genome'],
dict(
dest='genome',
help='Genome build. Accepted values: ' +
', '.join(ebl.SUPPORTED_GENOMES),
)),
(['-c', '--canonical'],
dict(
dest='canonical',
action='store_true',
help='Use canonical only',
)),
]
parser = OptionParser()
for args, kwargs in options:
parser.add_option(*args, **kwargs)
opts, args = parser.parse_args()
if not opts.genome:
logger.critical(
'Error: please, specify genome build name with -g (e.g. `-g hg19`)'
)
genome = opts.genome
logger.debug('Getting features from storage')
features_bed = ebl.get_all_features(genome)
if features_bed is None:
logger.critical('Genome ' + genome + ' is not supported. Supported: ' +
', '.join(ebl.SUPPORTED_GENOMES))
logger.warn('Extracting features from Ensembl GTF')
features_bed = features_bed.filter(
lambda x: x[ebl.BedCols.FEATURE] == 'CDS')
if opts.canonical:
features_bed = features_bed.filter(
ebl.get_only_canonical_filter(genome))
logger.warn('Saving CDS regions...')
output_fpath = adjust_path(
join(dirname(__file__), pardir, genome, 'bed', 'CDS-canonical.bed'))
with file_transaction(None, output_fpath) as tx:
features_bed.cut(range(6)).saveas(tx)
logger.warn('Done, saved to ' + output_fpath)
def main():
parser = OptionParser(usage='Usage: ' + basename(__file__) + ' -o Output_BED_file -g hg19 Input_BED_file')
parser.add_option('-o', '--output-bed', dest='output_fpath')
parser.add_option('-g', '--genome', dest='genome')
parser.add_option('--fai', dest='fai_fpath')
(opts, args) = parser.parse_args(sys.argv[1:])
if len(args) < 1:
parser.print_help(file=sys.stderr)
sys.exit(1)
if not opts.output_fpath:
critical(parser.usage)
sort_bed(input_bed_fpath=verify_bed(args[0], is_critical=True),
output_bed_fpath=adjust_path(opts.output_fpath),
fai_fpath=adjust_path(opts.fai_fpath),
genome=opts.genome)
def calc_genomic_bp_pos(self):
genomic_coord, is_in_intron = FusionSide.offset_to_genome_coord(
self.trx, self.bp_offset)
if genomic_coord is None:
logger.critical(f' Error: could not convert transcript {id} '
f'offset {genomic_coord} to genomic coordinate')
return False
if genomic_coord == -1:
logger.debug(
f' Fusion in takes the entire transcript {self.trx.id} '
f'(genomic_coord={genomic_coord}, bp_offset={self.bp_offset}). '
f'That\'s suspicious, so we are skipping it.')
return False
self.bp_genomic_pos = genomic_coord
self.bp_is_in_intron = is_in_intron
return True
def read_samples(args):
bam_by_sample = find_bams(args)
if bam_by_sample:
info('Found ' + str(len(bam_by_sample)) + ' BAM file' + ('s' if len(bam_by_sample) > 1 else ''))
input_not_bam = [verify_file(fpath) for fpath in args if adjust_path(fpath) not in bam_by_sample]
input_not_bam = [fpath for fpath in input_not_bam if fpath]
fastqs_by_sample = dict()
if not input_not_bam and not bam_by_sample:
critical('No correct input files')
if input_not_bam:
info('Input ' + str(len(input_not_bam)) + ' correct input non-BAM files')
fastqs_by_sample = find_fastq_pairs(input_not_bam)
if fastqs_by_sample:
info('Found ' + str(len(fastqs_by_sample)) + ' FastQ pairs')
intersection = set(fastqs_by_sample.keys()) & set(bam_by_sample.keys())
if intersection:
critical('The following samples both had input BAMs and FastQ: ' + ', '.join(list(intersection)))
return fastqs_by_sample, bam_by_sample
def make_cluster_cmdl(log_dir, app_name=''):
""" Generates cluster command line parameters for snakemake
"""
from hpc_utils import hpc
if not hpc.cluster_cmd:
logger.critical(f'Automatic cluster submission is not supported for the machine "{hpc.name or hpc.hostname}"')
cluster_submitter = get_submit_script()
timestamp = datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
cluster_cmdl = f' --cluster "{cluster_submitter} {timestamp} {log_dir} {app_name}"'
# Also overriding jobscript?
jobscript = hpc.cluster_jobscript
if jobscript:
jobscript_file = join(log_dir, 'jobscript.sh')
with open(jobscript_file, 'w') as f_out:
f_out.write(jobscript.replace('{path}', os.environ["PATH"]))
cluster_cmdl += f' --jobscript "{jobscript_file}"'
return cluster_cmdl
def sort_bed_gsort(input_bed_fpath, output_bed_fpath=None, work_dir=None, fai_fpath=None, genome=None):
input_bed_fpath = verify_bed(input_bed_fpath, is_critical=True)
output_bed_fpath = adjust_path(output_bed_fpath) if output_bed_fpath \
else intermediate_fname(work_dir, input_bed_fpath, 'sorted')
debug('Sorting regions in ' + str(input_bed_fpath))
if can_reuse(output_bed_fpath, input_bed_fpath):
debug(output_bed_fpath + ' exists, reusing')
return output_bed_fpath
if fai_fpath:
fai_fpath = verify_file(fai_fpath)
elif genome:
fai_fpath = verify_file(ref.get_fai(genome))
else:
critical('Either of fai_fpath or genome build name must be specified')
with file_transaction(work_dir, output_bed_fpath) as tx:
run('gsort {input_bed_fpath} {fai_fpath}'.format(**locals()), output_fpath=tx)
return output_bed_fpath
def load_bcbio_cnf(config_dir, silent=False):
all_yamls = [
abspath(join(config_dir, fname))
for fname in listdir(config_dir)
if fname.endswith('.yaml')]
if len(all_yamls) == 0:
critical('No YAML file in the config directory.')
bcbio_yamls = []
for fpath in all_yamls:
if not fpath.endswith('-template.yaml'):
if 'details' in load_yaml_config(fpath):
bcbio_yamls.append(fpath)
if len(bcbio_yamls) == 0:
critical('No bcbio YAMLs found in the config directory: ' + config_dir +
' (only ' + ', '.join(map(basename, all_yamls)) +
' which do not have the "details" section)')
if len(bcbio_yamls) > 1:
critical('More than one bcbio YAML file found in the config directory ' +
config_dir + ': ' + ' '.join(bcbio_yamls))
yaml_fpath = bcbio_yamls[0]
if not silent: info('Using bcbio YAML config: ' + yaml_fpath)
return load_yaml_config(yaml_fpath), yaml_fpath
def _annotate(bed, ref_bed, chr_order, fai_fpath, work_dir, ori_col_num,
high_confidence=False, reannotate=False, is_debug=False, **kwargs):
# if genome:
# genome_fpath = cut(fai_fpath, 2, output_fpath=intermediate_fname(work_dir, fai_fpath, 'cut2'))
# intersection = bed.intersect(ref_bed, sorted=True, wao=True, g='<(cut -f1,2 ' + fai_fpath + ')')
# intersection = bed.intersect(ref_bed, sorted=True, wao=True, genome=genome.split('-')[0])
# else:
intersection_bed = None
intersection_fpath = None
pybedtools.set_tempdir(safe_mkdir(join(work_dir, 'bedtools')))
if is_debug:
intersection_fpath = join(work_dir, 'intersection.bed')
if isfile(intersection_fpath):
info('Loading from ' + intersection_fpath)
intersection_bed = BedTool(intersection_fpath)
if not intersection_bed:
if count_bed_cols(fai_fpath) == 2:
debug('Fai fields size is 2 ' + fai_fpath)
intersection_bed = bed.intersect(ref_bed, wao=True, g=fai_fpath)
else:
debug('Fai fields is ' + str(count_bed_cols(fai_fpath)) + ', not 2')
intersection_bed = bed.intersect(ref_bed, wao=True)
if is_debug and not isfile(intersection_fpath):
intersection_bed.saveas(intersection_fpath)
debug('Saved intersection to ' + intersection_fpath)
total_annotated = 0
total_uniq_annotated = 0
total_off_target = 0
met = set()
overlaps_by_tx_by_gene_by_loc = OrderedDefaultDict(lambda: OrderedDefaultDict(lambda: defaultdict(list)))
# off_targets = list()
expected_fields_num = ori_col_num + len(ba.BedCols.cols[:-4]) + 1
for i, intersection_fields in enumerate(intersection_bed):
inters_fields_list = list(intersection_fields)
if len(inters_fields_list) < expected_fields_num:
critical(
f'Cannot parse the reference BED file - unexpected number of lines '
f'({len(inters_fields_list)} in {inters_fields_list} (less than {expected_fields_num})')
a_chr, a_start, a_end = intersection_fields[:3]
a_extra_columns = intersection_fields[3:ori_col_num]
overlap_fields = [None for _ in ba.BedCols.cols]
overlap_fields[:len(intersection_fields[ori_col_num:])] = intersection_fields[ori_col_num:]
keep_gene_column = not reannotate
a_gene = None
if keep_gene_column:
a_gene = a_extra_columns[0]
e_chr = overlap_fields[0]
overlap_size = int(intersection_fields[-1])
assert e_chr == '.' or a_chr == e_chr, f'Error on line {i}: chromosomes don\'t match ({a_chr} vs {e_chr}). Line: {intersection_fields}'
# fs = [None for _ in ebl.BedCols.cols]
# fs[:3] = [a_chr, a_start, a_end]
reg = (a_chr, int(a_start), int(a_end), tuple(a_extra_columns))
if e_chr == '.':
total_off_target += 1
# off_targets.append(fs)
overlaps_by_tx_by_gene_by_loc[reg][a_gene] = OrderedDefaultDict(list)
else:
# fs[3:-1] = db_feature_fields[3:-1]
total_annotated += 1
if (a_chr, a_start, a_end) not in met:
total_uniq_annotated += 1
met.add((a_chr, a_start, a_end))
e_gene = overlap_fields[ba.BedCols.GENE] if not high_confidence else overlap_fields[ba.BedCols.HUGO]
if keep_gene_column and e_gene != a_gene:
overlaps_by_tx_by_gene_by_loc[reg][a_gene] = OrderedDefaultDict(list)
else:
transcript_id = overlap_fields[ba.BedCols.ENSEMBL_ID]
overlaps_by_tx_by_gene_by_loc[reg][e_gene][transcript_id].append((overlap_fields, overlap_size))
info(' Total annotated regions: ' + str(total_annotated))
info(' Total unique annotated regions: ' + str(total_uniq_annotated))
info(' Total off target regions: ' + str(total_off_target))
info('Resolving ambiguities...')
annotated = _resolve_ambiguities(overlaps_by_tx_by_gene_by_loc, chr_order, **kwargs)
return annotated
def _proc_ucsc(inp, output_fpath, chr_order): #, approved_gene_by_name, approved_gnames_by_prev_gname, approved_gnames_by_synonym):
gene_by_name_and_chrom = dict()
for l in inp:
if l and not l.startswith('#'):
fs = l.replace('\n', '').split('\t')
txStart, txEnd = None, None
if len(fs) > 9:
_, transcript_id, ucsc_chrom, strand, txStart, txEnd, cdsStart, cdsEnd, exonCount, exonStarts, exonEnds, \
_, gene_symbol = fs[:13]
txStart, txEnd = int(txStart), int(txEnd)
else:
transcript_id, ucsc_chrom, strand, cdsStart, cdsEnd, exonCount, exonStarts, exonEnds, gene_symbol =\
l.replace('\n', '').split('\t')
cdsStart = int(cdsStart)
cdsEnd = int(cdsEnd)
exonCount = int(exonCount)
exonStarts = [int(v) + 1 for v in exonStarts.split(',') if v]
exonEnds = map(int, filter(None, exonEnds.split(',')))
# if ucsc_chrom != prev_chrom: # RefGene is not sorted
# info(ucsc_chrom)
# prev_chrom = ucsc_chrom
# approved_gene_symbol, status = get_approved_gene_symbol(
# approved_gene_by_name, approved_gnames_by_prev_gname, approved_gnames_by_synonym,
# gene_symbol, ucsc_id, ucsc_chrom)
#
# if not approved_gene_symbol:
# not_approved_gene_names.append(gene_symbol + '\t' + status)
# if DO_APPROVE:
# continue
# else:
# approved_gene_symbol = gene_symbol
txStart = txStart or exonStarts[0] - 1
txEnd = txEnd or exonEnds[exonCount - 1]
# out.write('\t'.join([ucsc_chrom, str(min(txStart, cdsStart)), str(max(txEnd, cdsEnd)),
# gene_symbol, '.', strand, 'Gene', '.']) + '\n')
assert txStart <= cdsStart, l
assert txEnd >= cdsEnd, l
if (gene_symbol, ucsc_chrom) not in gene_by_name_and_chrom:
gene = Gene(ucsc_chrom, chr_order.get(ucsc_chrom), gene_symbol, strand)
gene_by_name_and_chrom[(gene_symbol, ucsc_chrom)] = gene
gene = gene_by_name_and_chrom[(gene_symbol, ucsc_chrom)]
transcript = Transcript(gene, transcript_id, txStart, txEnd, strand) # one line - one transcript
gene.transcripts.append(transcript)
if transcript_id.startswith('NR_'):
transcript.coding = False
transcript.biotype = 'RNA'
elif transcript_id.startswith('NM_'):
transcript.coding = True
transcript.biotype = 'protein_coding'
else:
critical('Unknown transcript ID prefix ' + transcript_id.split('_')[0] + ' in ' + transcript_id)
r''' cdsStart cdsEnd exonsCount exonStarts exonsEnds
NM_001303242 chr1 + 150981108 151006710 7 150980866,150990287,150990942,150997086,150997990,150999708,151006281,150981147,150990380,150991145,150997271,150998149,150999803,151008189, PRUNE
NM_021222 chr1 + 150981108 151006710 8 150980866,150990287,150990942,150997086,150997990,150999708,151001261,151006281, 150981147,150990380,150991145,150997271,150998149,150999803,151001420,151008189, PRUNE
NM_001303243 chr1 + 150991069 151006710 6 150980866,150990287,150990942,150999708,151001261,151006281, 150981147,150990380,150991145,150999803,151001420,151008189, PRUNE
NM_001303229 chr1 + 150998016 151006710 7 150980866,150990287,150997086,150997990,150999708,151001261,151006281, 150981147,150990380,150997271,150998149,150999803,151001420,151008189, PRUNE
NR_130132 chr1 + 151008189 151008189 4 150980866,150990287,150999708,151006281, 150981147,150990380,150999803,151008189, PRUNE
NR_130131 chr1 + 151008189 151008189 5 150980866,150990287,150999708,151001261,151006281, 150981147,150990380,150999803,151001420,151008189, PRUNE
NR_130130 chr1 + 151008189 151008189 4 150980866,150997990,150999708,151006281, 150981147,150998149,150999803,151008189, PRUNE
NR_130135 chr1 + 151008189 151008189 5 150980866,150990287,150997990,150999708,151006281, 150981147,150990380,150998149,150999803,151008189, PRUNE
exonSt cdsSt exonEnd exonSt exonEnd exonSt cdsEnd exonEnd ncRNA: CDS is empty
NM_001303242 0980866/ *0981108* \0981147 0990287/ \0990380 ... 1006281/ *1006710* \1008189
NM_001303243 0980866/ *0991069* \0981147 0990287/ \0990380 ... 1006281/ *1006710* \1008189
NM_001303229 0980866/ *0998016* \0981147 0990287/ \0990380 ... 1006281/ *1006710* \1008189
NR_130132 0980866/ \0981147 0990287/ \0990380 ... 1006281/ \1008189 *1008189* *1008189*
'''
for exon_number, eStart, eEnd in zip(
range(exonCount),
[s for s in exonStarts if s],
[e for e in exonEnds if e]):
eStart -= 1
if eEnd <= cdsStart or eStart > cdsEnd:
biotype = 'UTR' if transcript.coding else transcript.biotype
transcript.exons.append(Exon(transcript, eStart, eEnd, 'Exon', exon_number, biotype))
else:
assert transcript.coding, 'Non-coding NM_ transcript ' + transcript_id
if eStart < cdsStart:
transcript.exons.append(Exon(transcript, eStart, cdsStart, 'Exon', exon_number, biotype='UTR'))
if eEnd > cdsEnd:
transcript.exons.append(Exon(transcript, cdsEnd, eEnd, 'Exon', exon_number, biotype='UTR'))
transcript.exons.append(Exon(transcript, max(cdsStart, eStart), min(cdsEnd, eEnd), 'CDS', exon_number))
return gene_by_name_and_chrom
def get_executable():
sys_path = which('sambamba')
if not sys_path:
critical('Error: sambamba executable is not found')
return sys_path
def check_genome(genome):
if genome not in SUPPORTED_GENOMES:
critical('Genome ' + str(genome) + ' is not supported. Supported genomes: ' + ', '.join(SUPPORTED_GENOMES))
def annotate(input_bed_fpath, output_fpath, work_dir, genome=None,
reannotate=True, high_confidence=False, only_canonical=False,
coding_only=False, short=False, extended=False, is_debug=False, **kwargs):
debug('Getting features from storage')
features_bed = ba.get_all_features(genome)
if features_bed is None:
critical('Genome ' + genome + ' is not supported. Supported: ' + ', '.join(ba.SUPPORTED_GENOMES))
if genome:
fai_fpath = reference_data.get_fai(genome)
chr_order = reference_data.get_chrom_order(genome)
else:
fai_fpath = None
chr_order = bed_chrom_order(input_bed_fpath)
input_bed_fpath = sort_bed(input_bed_fpath, work_dir=work_dir, chr_order=chr_order, genome=genome)
ori_bed = BedTool(input_bed_fpath)
ori_col_num = ori_bed.field_count()
reannotate = reannotate or ori_col_num == 3
pybedtools.set_tempdir(safe_mkdir(join(work_dir, 'bedtools')))
ori_bed = BedTool(input_bed_fpath)
if high_confidence:
features_bed = features_bed.filter(ba.high_confidence_filter)
if only_canonical:
features_bed = features_bed.filter(ba.get_only_canonical_filter(genome))
if coding_only:
features_bed = features_bed.filter(ba.protein_coding_filter)
# unique_tx_by_gene = find_best_tx_by_gene(features_bed)
info('Extracting features from Ensembl GTF')
features_bed = features_bed.filter(lambda x:
x[ba.BedCols.FEATURE] in ['exon', 'CDS', 'stop_codon', 'transcript'])
# x[ebl.BedCols.ENSEMBL_ID] == unique_tx_by_gene[x[ebl.BedCols.GENE]])
info('Overlapping regions with Ensembl data')
if is_debug:
ori_bed = ori_bed.saveas(join(work_dir, 'bed.bed'))
debug(f'Saved regions to {ori_bed.fn}')
features_bed = features_bed.saveas(join(work_dir, 'features.bed'))
debug(f'Saved features to {features_bed.fn}')
annotated = _annotate(ori_bed, features_bed, chr_order, fai_fpath, work_dir, ori_col_num,
high_confidence=False, reannotate=reannotate, is_debug=is_debug, **kwargs)
full_header = [ba.BedCols.names[i] for i in ba.BedCols.cols]
add_ori_extra_fields = ori_col_num > 3
if not reannotate and ori_col_num == 4:
add_ori_extra_fields = False # no need to report the original gene field if we are not re-annotating
info('Saving annotated regions...')
total = 0
with file_transaction(work_dir, output_fpath) as tx:
with open(tx, 'w') as out:
header = full_header[:6]
if short:
header = full_header[:4]
if extended:
header = full_header[:-1]
if add_ori_extra_fields:
header.append(full_header[-1])
if extended:
out.write('## ' + ba.BedCols.names[ba.BedCols.TX_OVERLAP_PERCENTAGE] +
': part of region overlapping with transcripts\n')
out.write('## ' + ba.BedCols.names[ba.BedCols.EXON_OVERLAPS_PERCENTAGE] +
': part of region overlapping with exons\n')
out.write('## ' + ba.BedCols.names[ba.BedCols.CDS_OVERLAPS_PERCENTAGE] +
': part of region overlapping with protein coding regions\n')
out.write('\t'.join(header) + '\n')
for full_fields in annotated:
fields = full_fields[:6]
if short:
fields = full_fields[:4]
if extended:
fields = full_fields[:-1]
if add_ori_extra_fields:
fields.append(full_fields[-1])
out.write('\t'.join(map(_format_field, fields)) + '\n')
total += 1
debug('Saved ' + str(total) + ' total annotated regions')
return output_fpath
