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run_clairs
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run_clairs
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#!/usr/bin/env python
# BSD 3-Clause License
#
# Copyright 2023 The University of Hong Kong, Department of Computer Science
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
import sys
import argparse
import shlex
import subprocess
from collections import defaultdict, namedtuple
from argparse import SUPPRESS
try:
from packaging.version import parse as version_parse
except ModuleNotFoundError:
from distutils.version import LooseVersion as version_parse
from time import time
import shared.param as param
from shared.interval_tree import bed_tree_from
from shared.utils import file_path_from, folder_path_from, subprocess_popen, str2bool, str_none, \
legal_range_from, log_error, log_warning, clair3_option_type
major_contigs = {"chr" + str(a) for a in list(range(1, 23)) + ["X", "Y"]}.union(
{str(a) for a in list(range(1, 23)) + ["X", "Y"]})
major_contigs_order = ["chr" + str(a) for a in list(range(1, 23)) + ["X", "Y"]] + [str(a) for a in
list(range(1, 23)) + ["X", "Y"]]
file_directory = os.path.dirname(os.path.realpath(__file__))
main_entry = os.path.join(file_directory, "clairs.py")
MAX_STEP = 20
OutputPath = namedtuple('OutputPath', [
'log_path',
'tmp_file_path',
'split_bed_path',
'candidates_path',
'pileup_tensor_can_path',
'fa_tensor_can_path',
'vcf_output_path',
'tmp_vcf_output_path',
])
Clair3Option = namedtuple('Clair3Option', [
'clair3_path',
'model_path',
'longphase_for_phasing',
'ctg_name_str',
'min_coverage',
'snp_min_af',
'indel_min_af',
'longphase'
])
supported_platforms = ['ont_r10_dorado_sup_5khz_ssrs', 'ont_r10_dorado_sup_5khz_ss', 'ont_r10_dorado_sup_5khz', 'ont_r10_dorado_sup_4khz', 'ont_r10_dorado_hac_5khz',
'ont_r10_dorado_hac_4khz', 'ont_r10_guppy', 'ont_r9_guppy', 'ilmn', 'hifi_sequel2', 'hifi_revio',
]
indel_supported_platforms = ['ont_r10_dorado_sup_5khz_ssrs', 'ont_r10_dorado_sup_5khz_ss', 'ont_r10_guppy', 'ont_r10', 'ont', 'hifi_revio', 'hifi_sequel2', 'ont_r10_dorado_sup_5khz',
'ont_r10_dorado_hac_5khz', 'ont_r10_dorado_sup_4khz', 'ont_r10_dorado_hac_5khz_liquid']
extend_supported_platforms = supported_platforms + ['ont_r10', 'ont_r9', 'ont', 'ont_r10_dorado_hac_5khz_liquid']
class Tee(object):
def __init__(self, name, mode):
self.file = open(name, mode)
self.stdout = sys.stdout
sys.stdout = self
def __del__(self):
sys.stdout = self.stdout
self.file.close()
def write(self, data):
self.file.write(data)
self.stdout.write(data)
def flush(self):
self.file.flush()
def logging(str):
if args.tee is None:
print(str)
else:
args.tee.stdin.write(bytes(str + '\n', encoding='utf8'))
def create_output_folder(args):
# create temp file folder
args.output_dir = folder_path_from(args.output_dir, create_not_found=True)
log_path = folder_path_from(os.path.join(args.output_dir, 'logs'), create_not_found=True)
tmp_file_path = folder_path_from(os.path.join(args.output_dir, 'tmp'), create_not_found=True)
split_bed_path = folder_path_from(os.path.join(tmp_file_path, 'split_beds'), create_not_found=True)
candidates_path = folder_path_from(os.path.join(tmp_file_path, 'candidates'), create_not_found=True)
pileup_tensor_can_path = folder_path_from(os.path.join(tmp_file_path, 'pileup_tensor_can'), create_not_found=True)
fa_tensor_can_path = folder_path_from(os.path.join(tmp_file_path, 'fa_tensor_can'), create_not_found=True)
vcf_output_path = folder_path_from(os.path.join(tmp_file_path, 'vcf_output'), create_not_found=True)
tmp_vcf_output_path = folder_path_from(os.path.join(tmp_file_path, 'tmp_vcf_output'), create_not_found=True)
if args.platform != 'ilmn':
clair3_log_path = folder_path_from(os.path.join(args.output_dir, 'logs', 'clair3_log'), create_not_found=True)
clair3_phased_output_path = folder_path_from(os.path.join(tmp_file_path, 'clair3_output/phased_output'), create_not_found=True)
clair3_vcf_output_path = folder_path_from(os.path.join(tmp_file_path, 'clair3_output/vcf'), create_not_found=True)
output_path = OutputPath(log_path=log_path,
tmp_file_path=tmp_file_path,
split_bed_path=split_bed_path,
candidates_path=candidates_path,
pileup_tensor_can_path=pileup_tensor_can_path,
fa_tensor_can_path=fa_tensor_can_path,
tmp_vcf_output_path=tmp_vcf_output_path,
vcf_output_path=vcf_output_path)
return output_path
def check_version(tool, pos=None, is_pypy=False):
try:
if is_pypy:
proc = subprocess.run("{} -c 'import sys; print (sys.version)'".format(tool), stdout=subprocess.PIPE,
shell=True)
else:
proc = subprocess.run([tool, "--version"], stdout=subprocess.PIPE)
if proc.returncode != 0:
return None
first_line = proc.stdout.decode().split("\n", 1)[0]
version = first_line.split()[pos]
version = version_parse(version)
except Exception:
return None
return version
def check_skip_steps_legal(args):
skip_steps = args.skip_steps
skip_steps_list = skip_steps.rstrip().split(",")
if len(skip_steps_list) == 0:
sys.exit(log_error("[ERROR] --skip_steps option provided but no skip steps index found"))
for step in skip_steps_list:
if int(step) < 1 or int(step) > MAX_STEP:
sys.exit(log_error("[ERROR] --skip_steps option provided but contains invalid skip steps index, should be 1-index"))
def check_python_path():
python_path = subprocess.run("which python", stdout=subprocess.PIPE, shell=True).stdout.decode().rstrip()
sys.exit(log_error("[ERROR] Current python execution path: {}".format(python_path)))
def check_python_version(python):
python_path = subprocess.run("{} --version".format(python), stdout=subprocess.PIPE, shell=True).stdout.decode().rstrip()
return python_path.split(' ')[1]
def check_tools_version(args):
required_tool_version = {
'python': version_parse('3.9.0'),
'pypy': version_parse('3.6'),
'samtools': version_parse('1.10'),
'whatshap': version_parse('1.0'),
'parallel': version_parse('20191122'),
}
tool_version = {
'python': version_parse(check_python_version(args.python)),
'pypy': check_version(tool=args.pypy, pos=0, is_pypy=True),
'samtools': check_version(tool=args.samtools, pos=1),
'parallel': check_version(tool=args.parallel, pos=2),
}
for tool, version in tool_version.items():
required_version = required_tool_version[tool]
if version is None:
logging(log_error("[ERROR] {} not found, please check if you are in the conda virtual environment".format(tool)))
check_python_path()
elif version < required_version:
logging(log_error("[ERROR] Tool version not match, please check if you are in the conda virtual environment"))
logging(' '.join([str(item).ljust(10) for item in ["Tool", "Version", "Required"]]))
error_info = ' '.join([str(item).ljust(10) for item in [tool, version, '>=' + str(required_version)]])
logging(error_info)
check_python_path()
return
def check_contig_in_bam(bam_fn, sorted_contig_list, samtools, allow_none=False, is_tumor=False):
flag = 'tumor' if is_tumor else 'normal'
if allow_none and bam_fn is None:
return sorted_contig_list, True
bai_process = subprocess_popen(shlex.split("{} idxstats {}".format(samtools, bam_fn)))
contig_with_read_support_set = set()
for row_id, row in enumerate(bai_process.stdout):
row = row.split('\t')
if len(row) != 4:
continue
contig_name, contig_length, mapped_reads, unmapped_reads = row
if contig_name not in sorted_contig_list:
continue
if int(mapped_reads) > 0:
contig_with_read_support_set.add(contig_name)
for contig_name in sorted_contig_list:
if contig_name not in contig_with_read_support_set:
logging(log_warning(
"[WARNING] Contig name {} provided but no mapped reads found in {} BAM, skip!".format(contig_name, flag)))
filtered_sorted_contig_list = [item for item in sorted_contig_list if item in contig_with_read_support_set]
found_contig = True
if len(filtered_sorted_contig_list) == 0:
found_contig = False
logging(log_warning(
"[WARNING] No mapped reads found in {} BAM for provided contigs set {}".format(
flag, ' '.join(sorted_contig_list))))
return filtered_sorted_contig_list, found_contig
def check_threads(args):
threads = args.threads
#sched_getaffinity is not exist in pypy
try:
sched_getaffinity_list = list(os.sched_getaffinity(0))
num_cpus = len(sched_getaffinity_list)
except:
num_cpus = int(subprocess.run(args.python + " -c \"import os; print(len(os.sched_getaffinity(0)))\"", \
stdout=subprocess.PIPE, shell=True).stdout.decode().rstrip())
if threads > num_cpus:
logging(log_warning(
'[WARNING] Threads setting {} is larger than the number of available threads {} in the system,'.format(
threads, num_cpus)))
logging(log_warning('Set --threads={} for better parallelism.'.format(num_cpus)))
args.threads = num_cpus
return args
def split_extend_vcf(genotyping_mode_vcf_fn, output_fn):
expand_region_size = param.no_of_positions
output_ctg_dict = defaultdict(list)
unzip_process = subprocess_popen(shlex.split("gzip -fdc %s" % (genotyping_mode_vcf_fn)))
for row_id, row in enumerate(unzip_process.stdout):
if row[0] == '#':
continue
columns = row.strip().split(maxsplit=3)
ctg_name = columns[0]
center_pos = int(columns[1])
ctg_start, ctg_end = center_pos - 1, center_pos
if ctg_start < 0:
sys.exit(
log_error("[ERROR] Invalid VCF input at the {}-th row {} {} {}".format(row_id + 1, ctg_name, center_pos)))
if ctg_start - expand_region_size < 0:
continue
expand_ctg_start = ctg_start - expand_region_size
expand_ctg_end = ctg_end + expand_region_size
output_ctg_dict[ctg_name].append(
' '.join([ctg_name, str(expand_ctg_start), str(expand_ctg_end)]))
for key, value in output_ctg_dict.items():
ctg_output_fn = os.path.join(output_fn, key)
with open(ctg_output_fn, 'w') as output_file:
output_file.write('\n'.join(value))
unzip_process.stdout.close()
unzip_process.wait()
know_vcf_contig_set = set(list(output_ctg_dict.keys()))
return know_vcf_contig_set
def split_extend_bed(bed_fn, output_fn, contig_set=None):
expand_region_size = param.no_of_positions
output_ctg_dict = defaultdict(list)
unzip_process = subprocess_popen(shlex.split("gzip -fdc %s" % (bed_fn)))
for row_id, row in enumerate(unzip_process.stdout):
if row[0] == '#':
continue
columns = row.strip().split()
ctg_name = columns[0]
if contig_set and ctg_name not in contig_set:
continue
ctg_start, ctg_end = int(columns[1]), int(columns[2])
if ctg_end < ctg_start or ctg_start < 0 or ctg_end < 0:
sys.exit(log_error(
"[ERROR] Invalid BED input at the {}-th row {} {} {}".format(row_id + 1, ctg_name, ctg_start, ctg_end)))
expand_ctg_start = max(0, ctg_start - expand_region_size)
expand_ctg_end = max(0, ctg_end + expand_region_size)
output_ctg_dict[ctg_name].append(
' '.join([ctg_name, str(expand_ctg_start), str(expand_ctg_end)]))
for key, value in output_ctg_dict.items():
ctg_output_fn = os.path.join(output_fn, key)
with open(ctg_output_fn, 'w') as output_file:
output_file.write('\n'.join(value))
unzip_process.stdout.close()
unzip_process.wait()
def write_region_bed(region):
try:
ctg_name, start_end = region.split(':')
ctg_start, ctg_end = int(start_end.split('-')[0]) - 1, int(start_end.split('-')[1]) - 1 # bed format
except:
sys.exit("[ERROR] Please use the correct format for --region: ctg_name:start-end, your input is {}".format(
region))
if ctg_end < ctg_start or ctg_start < 0 or ctg_end < 0:
sys.exit("[ERROR] Invalid region input: {}".format(region))
output_bed_path = os.path.join(args.output_dir, 'tmp', 'region.bed')
with open(output_bed_path, 'w') as f:
f.write('\t'.join([ctg_name, str(ctg_start), str(ctg_end)]) + '\n')
return output_bed_path
def check_contigs_intersection(args, fai_fn):
MIN_CHUNK_LENGTH = 200000
MAX_CHUNK_LENGTH = 20000000
is_include_all_contigs = args.include_all_ctgs
is_bed_file_provided = args.bed_fn is not None or args.region is not None
is_known_vcf_file_provided = args.genotyping_mode_vcf_fn is not None
is_ctg_name_list_provided = args.ctg_name is not None
if args.region is not None:
args.bed_fn = write_region_bed(args.region)
split_bed_path = os.path.join(args.output_dir, 'tmp', 'split_beds')
tree = bed_tree_from(bed_file_path=args.bed_fn, region=args.region)
know_vcf_contig_set = split_extend_vcf(genotyping_mode_vcf_fn=args.genotyping_mode_vcf_fn, output_fn=split_bed_path) if is_known_vcf_file_provided else set()
contig_set = set(args.ctg_name.split(',')) if is_ctg_name_list_provided else set()
if not args.include_all_ctgs:
logging("[INFO] --include_all_ctgs not enabled, use chr{1..22,X,Y} and {1..22,X,Y} by default")
else:
logging("[INFO] --include_all_ctgs enabled")
if is_ctg_name_list_provided and is_bed_file_provided:
logging(log_warning("[WARNING] both --ctg_name and --bed_fn provided, will only proceed with the contigs appeared in both"))
if is_ctg_name_list_provided and is_known_vcf_file_provided:
logging(log_warning("[WARNING] both --ctg_name and --genotyping_mode_vcf_fn provided, will only proceed with the contigs appeared in both"))
if is_ctg_name_list_provided:
contig_set = contig_set.intersection(
set(tree.keys())) if is_bed_file_provided else contig_set
contig_set = contig_set.intersection(
know_vcf_contig_set) if is_known_vcf_file_provided else contig_set
else:
contig_set = contig_set.union(
set(tree.keys())) if is_bed_file_provided else contig_set
contig_set = contig_set.union(
know_vcf_contig_set) if is_known_vcf_file_provided else contig_set
# if each split region is too small(long) for given default chunk num, will increase(decrease) the total chunk num
default_chunk_num = 0
DEFAULT_CHUNK_SIZE = args.chunk_size
contig_length_list = []
contig_chunk_num = {}
with open(fai_fn, 'r') as fai_fp:
for row in fai_fp:
columns = row.strip().split("\t")
contig_name, contig_length = columns[0], int(columns[1])
if not is_include_all_contigs and (
not (is_bed_file_provided or is_ctg_name_list_provided or is_known_vcf_file_provided)) and str(
contig_name) not in major_contigs:
continue
if is_bed_file_provided and contig_name not in tree:
continue
if is_ctg_name_list_provided and contig_name not in contig_set:
continue
if is_known_vcf_file_provided and contig_name not in contig_set:
continue
contig_set.add(contig_name)
contig_length_list.append(contig_length)
chunk_num = int(
contig_length / float(DEFAULT_CHUNK_SIZE)) + 1 if contig_length % DEFAULT_CHUNK_SIZE else int(
contig_length / float(DEFAULT_CHUNK_SIZE))
contig_chunk_num[contig_name] = max(chunk_num, 1)
if default_chunk_num > 0:
min_chunk_length = min(contig_length_list) / float(default_chunk_num)
max_chunk_length = max(contig_length_list) / float(default_chunk_num)
contigs_order = major_contigs_order + list(contig_set)
sorted_contig_list = sorted(list(contig_set), key=lambda x: contigs_order.index(x))
found_contig = True
if not len(contig_set):
if is_bed_file_provided:
all_contig_in_bed = ' '.join(list(tree.keys()))
logging(log_warning("[WARNING] No contig in --bed_fn was found in the reference, contigs in BED {}: {}".format(args.bed_fn, all_contig_in_bed)))
if is_known_vcf_file_provided:
all_contig_in_vcf = ' '.join(list(know_vcf_contig_set))
logging(log_warning("[WARNING] No contig in --genotyping_mode_vcf_fn was found in the reference, contigs in VCF {}: {}".format(args.genotyping_mode_vcf_fn, all_contig_in_vcf)))
if is_ctg_name_list_provided:
all_contig_in_ctg_name = ' '.join(args.ctg_name.split(','))
logging(log_warning("[WARNING] No contig in --ctg_name was found in the reference, contigs in contigs list: {}".format(all_contig_in_ctg_name)))
found_contig = False
else:
for c in sorted_contig_list:
if c not in contig_chunk_num:
logging(log_warning(("[WARNING] Contig {} given but not found in the reference".format(c))))
# check contig in bam have support reads
sorted_contig_list, tumor_found_contig = check_contig_in_bam(bam_fn=args.tumor_bam_fn, sorted_contig_list=sorted_contig_list,
samtools=args.samtools, is_tumor=True)
sorted_contig_list, normal_found_contig = check_contig_in_bam(bam_fn=args.normal_bam_fn, sorted_contig_list=sorted_contig_list,
samtools=args.samtools, allow_none=False, is_tumor=False)
found_contig = tumor_found_contig and normal_found_contig
if not found_contig:
log_warning("[WARNING] Exit calling because no contig was found in BAM!")
sys.exit(0)
logging('[INFO] Call variants in contigs: {}'.format(' '.join(sorted_contig_list)))
logging('[INFO] Number of chunks for each contig: {}'.format(
' '.join([str(contig_chunk_num[c]) for c in sorted_contig_list])))
if default_chunk_num > 0 and max_chunk_length > MAX_CHUNK_LENGTH:
logging(log_warning(
'[WARNING] The maximum chunk size set {} is larger than the suggested maximum chunk size {}, consider setting a larger --chunk_num= instead for better parallelism.'.format(
min_chunk_length, MAX_CHUNK_LENGTH)))
elif default_chunk_num > 0 and min_chunk_length < MIN_CHUNK_LENGTH:
logging(log_warning(
'[WARNING] The minimum chunk size set {} is smaller than the suggested minimum chunk size {}, consider setting a smaller --chunk_num= instead.'.format(
min_chunk_length, MIN_CHUNK_LENGTH)))
if default_chunk_num == 0 and max(contig_length_list) < DEFAULT_CHUNK_SIZE / 5:
logging(log_warning(
'[WARNING] The length of the longest contig {} is more than five times smaller smaller than the default chunk size {}, consider setting a smaller --chunk_size= instead for better parallelism.'.format(
max(contig_length_list), DEFAULT_CHUNK_SIZE)))
if is_bed_file_provided:
split_extend_bed(bed_fn=args.bed_fn, output_fn=split_bed_path, contig_set=contig_set)
contig_path = os.path.join(args.output_dir, 'tmp', 'CONTIGS')
with open(contig_path, 'w') as output_file:
output_file.write('\n'.join(sorted_contig_list))
chunk_list = []
chunk_list_path = os.path.join(args.output_dir, 'tmp', 'CHUNK_LIST')
with open(chunk_list_path, 'w') as output_file:
for contig_name in sorted_contig_list:
chunk_num = contig_chunk_num[contig_name] if args.chunk_num is None else args.chunk_num
for chunk_id in range(1, chunk_num + 1):
output_file.write(contig_name + ' ' + str(chunk_id) + ' ' + str(chunk_num) + '\n')
chunk_list.append((contig_name, chunk_id, chunk_num))
args.chunk_list = chunk_list
if args.clair3_path is not None and args.platform != 'ilmn':
args.clair3_option = args.clair3_option._replace(ctg_name_str=','.join(sorted_contig_list))
return args
def check_clair3_options(args):
conda_prefix = args.conda_prefix
if args.clair3_path is None:
args.clair3_path = os.path.join(conda_prefix, 'bin')
if not os.path.exists(args.clair3_path + '/run_clair3.sh'):
sys.exit(log_error("[ERROR] Cannot find clair3 main entry in {}".format(args.clair3_path)))
if args.clair3_model_path is None:
if args.platform == 'ont_r10' or args.platform == 'ont' or args.platform == 'ont_r10_guppy':
args.clair3_model_path = os.path.join(conda_prefix, 'bin', 'clairs_models', 'clair3_models', 'ont_r104_e81_sup_g5015')
elif args.platform == 'ont_r9' or args.platform == 'ont_r9_guppy':
# clair3 r9 model in bioconda bin
args.clair3_model_path = os.path.join(conda_prefix, 'bin', 'models', 'r941_prom_sup_g5014')
elif args.platform == 'hifi' or args.platform == 'hifi_sequel2':
# clair3 hifi model in bioconda bin
args.clair3_model_path = os.path.join(conda_prefix, 'bin', 'models', 'hifi')
elif args.platform == "hifi_revio":
args.clair3_model_path = os.path.join(conda_prefix, 'bin', 'clairs_models', 'clair3_models', 'hifi_revio')
elif args.platform == "ont_r10_dorado_4khz" or args.platform == "ont_r10_dorado_sup_4khz":
args.clair3_model_path = os.path.join(conda_prefix, 'bin', 'clairs_models', 'clair3_models', 'r1041_e82_400bps_sup_v410')
elif args.platform == "ont_r10_dorado_5khz" or args.platform.startswith("ont_r10_dorado_sup_5khz"):
args.clair3_model_path = os.path.join(conda_prefix, 'bin', 'clairs_models', 'clair3_models', 'r1041_e82_400bps_sup_v420')
elif args.platform == "ont_r10_dorado_hac_5khz" or args.platform == "ont_r10_dorado_hac_5khz_liquid":
args.clair3_model_path = os.path.join(conda_prefix, 'bin', 'clairs_models', 'clair3_models', 'r1041_e82_400bps_hac_v420')
elif args.platform == "ont_r10_dorado_hac_4khz":
args.clair3_model_path = os.path.join(conda_prefix, 'bin', 'clairs_models', 'clair3_models', 'r1041_e82_400bps_hac_v410')
if not os.path.exists(args.clair3_model_path):
sys.exit(log_error("[ERROR] Cannot find clair3 model path in {}".format(args.clair3_model_path)))
if args.longphase is None:
args.longphase = os.path.join(conda_prefix, 'bin', 'longphase')
if not os.path.exists(args.longphase):
args.longphase = os.path.join(args.clair3_path, 'longphase')
if args.whatshap is None:
args.whatshap = os.path.join(conda_prefix, 'bin', 'whatshap')
longphase_for_phasing = False if args.whatshap_for_phasing is True else param.clair3_fast_option['longphase_for_phasing']
if longphase_for_phasing and not os.path.exists(args.longphase):
sys.exit(log_error("[ERROR] Cannot find longphase at {}".format(args.longphase)))
if args.whatshap_for_phasing and not os.path.exists(args.whatshap):
sys.exit(log_error("[ERROR] Cannot find whatshap at {}".format(args.whatshap)))
clair3_option = param.clair3_fast_option
if args.enable_clair3_germline_output:
# use clair3 default settings than the fast setting for germline calling
clair3_option = param.clair3_default_option
args.clair3_min_coverage = args.clair3_min_coverage if args.clair3_min_coverage is not None else clair3_option['min_coverage']
args.clair3_snp_min_af = args.clair3_snp_min_af if args.clair3_snp_min_af is not None else clair3_option['snp_min_af']
args.clair3_indel_min_af = args.clair3_indel_min_af if args.clair3_indel_min_af is not None else clair3_option['indel_min_af']
args.clair3_option = Clair3Option(
clair3_path=args.clair3_path,
model_path=args.clair3_model_path,
longphase_for_phasing=longphase_for_phasing,
ctg_name_str=None, # update in check_contigs_intersection
min_coverage=str(args.clair3_min_coverage),
snp_min_af=str(args.clair3_snp_min_af),
indel_min_af=str(args.clair3_indel_min_af),
longphase=args.longphase)
return args
def check_args(args):
if args.conda_prefix is None:
if 'CONDA_PREFIX' in os.environ:
args.conda_prefix = os.environ['CONDA_PREFIX']
else:
try:
python_path = subprocess.run('which python', stdout=subprocess.PIPE, shell=True).stdout.decode().rstrip()
args.conda_prefix = os.path.dirname(os.path.dirname(python_path))
except:
sys.exit(log_error("[ERROR] Conda prefix not found, please activate a correct conda environment."))
args.normal_bam_fn = file_path_from(file_name=args.normal_bam_fn, exit_on_not_found=True, allow_none=False)
normal_bai_fn = file_path_from(file_name=args.normal_bam_fn, suffix=".bai", exit_on_not_found=False, sep='.')
normal_crai_fn = file_path_from(file_name=args.normal_bam_fn, suffix=".crai", exit_on_not_found=False, sep='.')
normal_csi_fn = file_path_from(file_name=args.normal_bam_fn, suffix=".csi", exit_on_not_found=False, sep='.')
args.tumor_bam_fn = file_path_from(file_name=args.tumor_bam_fn, exit_on_not_found=True)
tumor_bai_fn = file_path_from(file_name=args.tumor_bam_fn, suffix=".bai", exit_on_not_found=False, sep='.')
tumor_crai_fn = file_path_from(file_name=args.tumor_bam_fn, suffix=".crai", exit_on_not_found=False, sep='.')
tumor_csi_fn = file_path_from(file_name=args.tumor_bam_fn, suffix=".csi", exit_on_not_found=False, sep='.')
args.ref_fn = file_path_from(file_name=args.ref_fn, exit_on_not_found=True)
fai_fn = file_path_from(file_name=args.ref_fn, suffix=".fai", exit_on_not_found=True, sep='.')
args.bed_fn = file_path_from(file_name=args.bed_fn, exit_on_not_found=True, allow_none=True)
args.genotyping_mode_vcf_fn = file_path_from(file_name=args.genotyping_mode_vcf_fn, exit_on_not_found=True, allow_none=True)
args.hybrid_mode_vcf_fn = file_path_from(file_name=args.hybrid_mode_vcf_fn, exit_on_not_found=True, allow_none=True)
args.normal_vcf_fn = file_path_from(file_name=args.normal_vcf_fn, exit_on_not_found=True, allow_none=True)
if args.platform in param.model_name_platform_dict:
updated_platform = param.model_name_platform_dict[args.platform]
logging("[INFO] Platform parameter is using ONT model name format, change --platform {} to --platform {}".format(args.platform, updated_platform))
args.platform = updated_platform
if normal_bai_fn is None and normal_crai_fn is None and normal_csi_fn is None:
sys.exit(log_error("[ERROR] Normal BAM index file {} or {} not found. Please run `samtools index $BAM` first.".format(args.normal_bam_fn + '.bai',
args.normal_bam_fn + '.crai')))
if tumor_bai_fn is None and tumor_crai_fn is None and tumor_csi_fn is None:
sys.exit(log_error("[ERROR] Tumor BAM index file {} or {} not found. Please run `samtools index $BAM` first.".format(args.tumor_bam_fn + '.bai',
args.tumor_bam_fn + '.crai')))
if args.enable_indel_calling and args.platform not in set(indel_supported_platforms):
sys.exit(log_error("[ERROR] Indel calling only support {} platforms".format(','.join(indel_supported_platforms))))
if args.genotyping_mode_vcf_fn is not None and args.hybrid_mode_vcf_fn is not None:
sys.exit(log_error("[ERROR] Please provide either --genotyping_mode_vcf_fn or --hybrid_mode_vcf_fn only"))
if args.platform == 'ont_r10_dorado_hac_5khz_liquid':
args.enable_params_for_liquid_tumor_sample = True
if args.pileup_model_path is None:
args.pileup_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs_models', args.platform, 'pileup.pkl')
if args.full_alignment_model_path is None:
args.full_alignment_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs_models', args.platform, 'full_alignment.pkl')
args.pileup_model_path = file_path_from(file_name=args.pileup_model_path, exit_on_not_found=True, is_directory=False, allow_none=False)
args.full_alignment_model_path = file_path_from(file_name=args.full_alignment_model_path, exit_on_not_found=True, is_directory=False, allow_none=False)
if args.enable_indel_calling:
if args.indel_pileup_model_path is None:
args.indel_pileup_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs_models', args.platform, 'indel',
'pileup.pkl')
if args.indel_full_alignment_model_path is None:
args.indel_full_alignment_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs_models', args.platform, 'indel',
'full_alignment.pkl')
args.indel_pileup_model_path = file_path_from(file_name=args.indel_pileup_model_path, exit_on_not_found=True,
is_directory=False, allow_none=False)
args.indel_full_alignment_model_path = file_path_from(file_name=args.indel_full_alignment_model_path,
exit_on_not_found=True, is_directory=False, allow_none=False)
if args.snv_min_af is None:
args.snv_min_af = param.snv_min_af
if args.indel_min_af is None:
if args.enable_indel_calling:
if args.platform.startswith('ont'):
args.indel_min_af = 0.1
else:
args.indel_min_af = 0.05
else:
args.indel_min_af = 1.0
if args.min_coverage is None:
args.min_coverage = param.min_coverage
if args.chunk_size is None:
args.chunk_size = 5000000
if args.platform not in set(extend_supported_platforms):
logging(log_error('[ERROR] Invalid platform input, optional: {}'.format(', '.join(supported_platforms))))
if args.qual is None:
if args.prefer_balance:
args.qual = param.best_thred_qual[args.platform] if args.platform in param.best_thred_qual else param.best_thred_qual['ont']
else:
# default we enable min_thred_qual
args.qual = param.min_thred_qual[args.platform] if args.platform in param.min_thred_qual else param.min_thred_qual['ont']
if args.skip_steps is not None:
check_skip_steps_legal(args)
if args.enable_realignment and args.platform != 'ilmn':
args.enable_realignment = False
if args.min_bq is None and (args.platform == "ont_r10_dorado_hac_5khz" or args.platform == "ont_r10_dorado_hac_4khz" or args.platform == 'ont_r10_dorado_hac_5khz_liquid'):
args.min_bq = 15
if args.disable_phasing or args.haplotagged_tumor_bam_provided_so_skip_intermediate_phasing_and_haplotagging:
args.phase_tumor = False
if args.phase_tumor is None:
if args.genotyping_mode_vcf_fn is not None:
logging(log_warning(
"[WARNING] Clair3 HET SNPs based phasing is disabled if `--genotyping_mode_vcf_fn` is provided, add `--phase_tumor True` if phasing the tumor is still needed. Please ensure you have sufficient heterozygous variant candidates given in the --genotyping_mode_vcf_fn file, otherwise the phasing step might lead to worse performance."))
else:
args.phase_tumor = True if args.platform != 'ilmn' else False
#N: normal V: variant T: tumor B: BAM
NVNB = args.use_heterozygous_snp_in_normal_sample_and_normal_bam_for_intermediate_phasing
TVTB = args.use_heterozygous_snp_in_tumor_sample_and_tumor_bam_for_intermediate_phasing
NVTB = args.use_heterozygous_snp_in_normal_sample_and_tumor_bam_for_intermediate_phasing
TVNB = args.use_heterozygous_snp_in_tumor_sample_and_normal_bam_for_intermediate_phasing
# By default, we use HET SNP in normal VCF for phasing after v0.1.7
if NVNB is None and TVTB is None and NVTB is None and TVNB is None:
args.use_heterozygous_snp_in_normal_sample_and_tumor_bam_for_intermediate_phasing = True
NVTB = True
if NVTB:
args.use_heterozygous_snp_in_normal_sample_for_intermediate_phasing = True
args.use_normal_bam_for_intermediate_phasing = False
elif TVNB:
args.use_heterozygous_snp_in_tumor_sample_for_intermediate_phasing = True
args.use_normal_bam_for_intermediate_phasing = True
elif NVNB:
args.use_heterozygous_snp_in_normal_sample_for_intermediate_phasing = True
args.use_normal_bam_for_intermediate_phasing = True
elif TVTB:
args.use_heterozygous_snp_in_tumor_sample_for_intermediate_phasing = True
args.use_normal_bam_for_intermediate_phasing = False
if args.use_heterozygous_indel_for_intermediate_phasing is None:
args.use_heterozygous_indel_for_intermediate_phasing = False
if args.use_longphase_for_intermediate_haplotagging is None:
args.use_longphase_for_intermediate_haplotagging = False
if args.enable_verdict:
ref_contigs_set = set()
with open(fai_fn, 'r') as fai_fp:
for row in fai_fp:
columns = row.strip().split("\t")
contig_name = columns[0]
ref_contigs_set.add(contig_name)
contigs_order = ["chr" + str(a) for a in list(range(1, 23)) + ["X"]]
verdict_flag = set(contigs_order).intersection(ref_contigs_set) is not None
if not verdict_flag:
args.enable_verdict = False
logging(log_warning(
"[WARNING] Verdict currently only works for GRCh38 reference genome, disable the --enable_verdict option!"))
if args.cnv_resource_dir is None:
args.cnv_resource_dir = os.path.join(args.conda_prefix, 'bin', 'cnv_data', 'reference_files')
if args.allele_counter_dir is None:
args.allele_counter_dir = os.path.join(file_directory, 'src', 'verdict', 'allele_counter')
if not os.path.exists(args.allele_counter_dir):
args.enable_verdict = False
logging(log_warning(
"[WARNING] The allele counter {}is not found, disable the --enable_verdict option!".format(args.allele_counter_dir)))
if not os.path.exists(args.cnv_resource_dir):
args.enable_verdict = False
logging(log_warning(
"[WARNING] The CNV resource directory {} is not found, disable the --enable_verdict option!".format(args.cnv_resource_dir)))
if args.genotyping_mode_vcf_fn is not None or args.hybrid_mode_vcf_fn is not None:
logging(log_warning("[INFO] Enable --print_ref_calls and --print_germline_calls options in genotyping mode!"))
args.print_ref_calls = True
args.print_germline_calls = True
if args.hybrid_mode_vcf_fn is not None and args.platform != 'ilmn':
logging(log_warning(
"[INFO] Enable --enable_clair3_germline_output option in hybrid calling mode!"))
args.enable_clair3_germline_output = True
if args.phase_tumor is False:
# cannot only phase normal
args.phase_normal = False
legal_range_from(param_name="threads", x=args.threads, min_num=1, exit_out_of_range=True)
legal_range_from(param_name="qual", x=args.qual, min_num=-1, exit_out_of_range=True)
legal_range_from(param_name="min_coverage", x=args.min_coverage, min_num=0, exit_out_of_range=True)
legal_range_from(param_name="snv_min_af", x=args.snv_min_af, min_num=0, max_num=1, exit_out_of_range=True)
legal_range_from(param_name="indel_min_af", x=args.indel_min_af, min_num=0, max_num=1, exit_out_of_range=True)
if args.indel_max_af_in_normal is not None:
legal_range_from(param_name="indel_max_af_in_normal", x=args.indel_max_af_in_normal, min_num=0, max_num=1, exit_out_of_range=True)
legal_range_from(param_name="chunk_size", x=args.chunk_size, min_num=0, exit_out_of_range=True)
args.output_path = create_output_folder(args)
check_tools_version(args=args)
args = check_threads(args=args)
if args.platform != 'ilmn':
args = check_clair3_options(args)
args = check_contigs_intersection(args=args, fai_fn=fai_fn)
return args
def print_args(args):
logging("")
logging("[INFO] CALLER VERSION: {}".format(param.version))
logging("[INFO] NORMAL BAM FILE PATH: {}".format(args.normal_bam_fn))
logging("[INFO] TUMOR BAM FILE PATH: {}".format(args.tumor_bam_fn))
logging("[INFO] REFERENCE FILE PATH: {}".format(args.ref_fn))
logging("[INFO] PLATFORM: {}".format(args.platform))
logging("[INFO] THREADS: {}".format(args.threads))
logging("[INFO] OUTPUT FOLDER: {}".format(args.output_dir))
logging("[INFO] OUTPUT VCF PATH: {}".format(os.path.join(args.output_dir, args.output_prefix + '.vcf.gz')))
logging("[INFO] PILEUP MODEL PATH: {}".format(args.pileup_model_path))
logging("[INFO] FULL-ALIGNMENT MODEL PATH: {}".format(args.full_alignment_model_path))
logging("[INFO] BED FILE PATH: {}".format(args.bed_fn))
logging("[INFO] GENOTYPING MODE VCF FILE PATH: {}".format(args.genotyping_mode_vcf_fn))
logging("[INFO] HYBRID MODE VCF FILE PATH: {}".format(args.hybrid_mode_vcf_fn))
logging("[INFO] REGION FOR CALLING: {}".format(args.region))
logging("[INFO] CONTIGS FOR CALLING: {}".format(args.ctg_name))
logging("[INFO] CONDA BINARY PREFIX: {}".format(args.conda_prefix))
logging("[INFO] SAMTOOLS BINARY PATH: {}".format(args.samtools))
logging("[INFO] PYTHON BINARY PATH: {}".format(args.python))
logging("[INFO] PYPY BINARY PATH: {}".format(args.pypy))
logging("[INFO] PARALLEL BINARY PATH: {}".format(args.parallel))
logging("[INFO] LONGPHASE BINARY PATH: {}".format(args.longphase))
logging("[INFO] CHUNK SIZE: {}".format(args.chunk_size))
logging("[INFO] SNV MINIMUM AF: {}".format(args.snv_min_af))
if args.enable_indel_calling:
logging("[INFO] INDEL MINIMUM AF: {}".format(args.indel_min_af))
logging("[INFO] INDEL PILEUP MODEL PATH: {}".format(args.indel_pileup_model_path))
logging("[INFO] INDEL FULL-ALIGNMENT MODEL PATH: {}".format(args.indel_full_alignment_model_path))
logging("[INFO] NORMAL VCF FILE PATH: {}".format(args.normal_vcf_fn))
logging("[INFO] DISABLE PHASING: {}".format(args.disable_phasing))
logging("[INFO] ENABLE DRY RUN: {}".format(args.dry_run))
logging("[INFO] ENABLE INDEL CALLING: {}".format(args.enable_indel_calling))
logging("[INFO] ENABLE PRINTING REFERENCE CALLS: {}".format(args.print_ref_calls))
logging("[INFO] ENABLE PRINTING GERMLINE CALLS: {}".format(args.print_germline_calls))
logging("[INFO] ENABLE INCLUDING ALL CTGS FOR CALLING: {}".format(args.include_all_ctgs))
logging("[INFO] ENABLE REMOVING INTERMEDIATE FILES: {}".format(args.remove_intermediate_dir))
logging("")
if args.platform.startswith('ont'):
args.platform = 'ont'
if args.platform.startswith('hifi'):
args.platform = 'hifi'
if args.cmdline is not None and args.cmdline != "":
with open(args.output_dir + '/tmp/CMD', 'w') as f:
f.write(args.cmdline + '\n')
return args
def print_command_line(args):
try:
cmdline = os.path.realpath(__file__)
cmdline += ' --tumor_bam_fn {} '.format(args.tumor_bam_fn)
cmdline += '--normal_bam_fn {} '.format(args.normal_bam_fn)
cmdline += '--ref_fn {} '.format(args.ref_fn)
cmdline += '--threads {} '.format(args.threads)
cmdline += '--platform {} '.format(args.platform)
cmdline += '--output_dir {} '.format(args.output_dir)
cmdline += '--pileup_model_path {} '.format(args.pileup_model_path) if args.pileup_model_path is not None else ""
cmdline += '--full_alignment_model_path {} '.format(args.full_alignment_model_path) if args.full_alignment_model_path is not None else ""
cmdline += '--ctg_name {} '.format(args.ctg_name) if args.ctg_name is not None else ""
cmdline += '--region {} '.format(args.region) if args.region is not None else ""
cmdline += '--bed_fn {} '.format(args.bed_fn) if args.bed_fn is not None else ""
cmdline += '--genotyping_mode_vcf_fn {} '.format(args.genotyping_mode_vcf_fn) if args.genotyping_mode_vcf_fn is not None else ""
cmdline += '--hybrid_mode_vcf_fn {} '.format(args.hybrid_mode_vcf_fn) if args.hybrid_mode_vcf_fn is not None else ""
cmdline += '--qual {} '.format(args.qual) if args.qual is not None else ""
cmdline += '--snv_min_af {} '.format(args.snv_min_af) if args.snv_min_af is not None else ""
cmdline += '--indel_min_af {} '.format(args.indel_min_af) if args.indel_min_af is not None else ""
cmdline += '--indel_max_af_in_normal {} '.format(args.indel_max_af_in_normal) if args.indel_max_af_in_normal is not None else ""
cmdline += '--min_coverage {} '.format(args.min_coverage) if args.min_coverage is not None else ""
cmdline += '--chunk_size {} '.format(args.chunk_size) if args.chunk_size is not None else ""
cmdline += '--sample_name {} '.format(args.sample_name) if args.sample_name != "SAMPLE" else ""
cmdline += '--output_prefix {} '.format(args.output_prefix) if args.output_prefix != "output" else ""
cmdline += '--remove_intermediate_dir ' if args.remove_intermediate_dir else ""
cmdline += '--include_all_ctgs ' if args.include_all_ctgs else ""
cmdline += '--print_ref_calls ' if args.print_ref_calls else ""
cmdline += '--print_germline_calls ' if args.print_germline_calls else ""
cmdline += '--python {} '.format(args.python) if args.python != "python3" else ""
cmdline += '--pypy {} '.format(args.pypy) if args.pypy != "pypy3" else ""
cmdline += '--samtools {} '.format(args.samtools) if args.samtools != "samtools" else ""
cmdline += '--parallel {} '.format(args.parallel) if args.parallel != "parallel" else ""
cmdline += '--normal_vcf_fn {} '.format(args.normal_vcf_fn) if args.normal_vcf_fn is not None else ""
cmdline += '--disable_phasing ' if args.disable_phasing else ""
cmdline += '--haplotagged_tumor_bam_provided_so_skip_intermediate_phasing_and_haplotagging ' if args.haplotagged_tumor_bam_provided_so_skip_intermediate_phasing_and_haplotagging else ""
cmdline += '--enable_indel_calling ' if args.enable_indel_calling else ""
cmdline += '--indel_output_prefix {} '.format(args.indel_output_prefix) if args.indel_output_prefix != "indel" else ""
cmdline += '--indel_pileup_model_path {} '.format(args.indel_pileup_model_path) if args.indel_pileup_model_path is not None else ""
cmdline += '--indel_full_alignment_model_path {} '.format(args.indel_full_alignment_model_path) if args.indel_full_alignment_model_path is not None else ""
cmdline += '--clair3_path {} '.format(args.clair3_path) if args.clair3_path is not None else ""
cmdline += '--clair3_model_path {} '.format(args.clair3_model_path) if args.clair3_model_path is not None else ""
cmdline += '--longphase {} '.format(args.longphase) if args.longphase is not None else ""
cmdline += '--whatshap {} '.format(args.whatshap) if args.whatshap is not None else ""
cmdline += '--whatshap_for_phasing {} '.format(args.whatshap_for_phasing) if args.whatshap_for_phasing is not None else ""
cmdline += '--prefer_recall ' if args.prefer_recall else ""
cmdline += '--prefer_balance ' if args.prefer_balance else ""
cmdline += '--phase_normal {} '.format(args.phase_normal) if args.phase_normal is True else ""
cmdline += '--phase_tumor {} '.format(args.phase_tumor) if args.phase_tumor is not None else ""
cmdline += '--use_gpu ' if args.use_gpu else ""
cmdline += '--indel_min_af {} '.format(args.indel_min_af) if args.indel_min_af is not None else ""
cmdline += '--enable_realignment False ' if args.enable_realignment is False else ""
cmdline += '--apply_post_processing False ' if args.apply_post_processing is False else ""
cmdline += '--skip_steps {} '.format(args.skip_steps) if args.skip_steps is not None else ""
cmdline += '--clair3_min_coverage {} '.format(args.clair3_min_coverage) if args.clair3_min_coverage is not None else ""
cmdline += '--clair3_snp_min_af {} '.format(args.clair3_snp_min_af) if args.clair3_snp_min_af is not None else ""
cmdline += '--clair3_indel_min_af {} '.format(args.clair3_indel_min_af) if args.clair3_indel_min_af is not None else ""
cmdline += '--enable_clair3_germline_output ' if args.enable_clair3_germline_output else ""
cmdline += '--use_heterozygous_snp_in_normal_sample_and_normal_bam_for_intermediate_phasing {} '.format(args.use_heterozygous_snp_in_normal_sample_and_normal_bam_for_intermediate_phasing) if args.use_heterozygous_snp_in_normal_sample_and_normal_bam_for_intermediate_phasing is not None else ""
cmdline += '--use_heterozygous_snp_in_tumor_sample_and_tumor_bam_for_intermediate_phasing {} '.format(args.use_heterozygous_snp_in_tumor_sample_and_tumor_bam_for_intermediate_phasing) if args.use_heterozygous_snp_in_tumor_sample_and_tumor_bam_for_intermediate_phasing is not None else ""
cmdline += '--use_heterozygous_snp_in_normal_sample_and_tumor_bam_for_intermediate_phasing {} '.format(args.use_heterozygous_snp_in_normal_sample_and_tumor_bam_for_intermediate_phasing) if args.use_heterozygous_snp_in_normal_sample_and_tumor_bam_for_intermediate_phasing is not None else ""
cmdline += '--use_heterozygous_snp_in_tumor_sample_and_normal_bam_for_intermediate_phasing {} '.format(args.use_heterozygous_snp_in_tumor_sample_and_normal_bam_for_intermediate_phasing) if args.use_heterozygous_snp_in_tumor_sample_and_normal_bam_for_intermediate_phasing is not None else ""
cmdline += '--use_heterozygous_snp_in_normal_sample_for_intermediate_phasing {} '.format(args.use_heterozygous_snp_in_normal_sample_for_intermediate_phasing) if args.use_heterozygous_snp_in_normal_sample_for_intermediate_phasing is not None else ""
cmdline += '--use_heterozygous_snp_in_tumor_sample_for_intermediate_phasing {} '.format(args.use_heterozygous_snp_in_tumor_sample_for_intermediate_phasing) if args.use_heterozygous_snp_in_tumor_sample_for_intermediate_phasing is not None else ""
cmdline += '--use_heterozygous_indel_for_intermediate_phasing {} '.format(args.use_heterozygous_indel_for_intermediate_phasing) if args.use_heterozygous_indel_for_intermediate_phasing is not None else ""
cmdline += '--use_normal_bam_for_intermediate_phasing {} '.format(args.use_normal_bam_for_intermediate_phasing) if args.use_normal_bam_for_intermediate_phasing is not None else ""
cmdline += '--use_longphase_for_intermediate_haplotagging {} '.format(args.use_longphase_for_intermediate_haplotagging) if args.use_longphase_for_intermediate_haplotagging is not None else ""
cmdline += '--enable_verdict ' if args.enable_verdict else ""
cmdline += '--conda_prefix {} '.format(args.conda_prefix) if args.conda_prefix is not None else ""
args.cmdline = cmdline
except:
return args
logging("[COMMAND] " + cmdline + '\n')
return args
def somatic_calling(args):
step = 1
echo_list = []
commands_list = []
tmp_vcf_output_path = args.output_path.tmp_vcf_output_path
vcf_output_path = args.output_path.vcf_output_path
clair3_output_path = args.output_dir + '/tmp/clair3_output'
normal_bam_fn = clair3_output_path + '/phased_output/normal_{1/.}.bam' if args.phase_normal else args.normal_bam_fn
tumor_bam_fn = clair3_output_path + '/phased_output/tumor_{1/.}.bam' if args.phase_tumor else args.tumor_bam_fn
tumor_bam_prefix = clair3_output_path + '/phased_output/tumor_' if args.phase_tumor else args.tumor_bam_fn
try:
rc = subprocess.check_call('time', shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
time = 'time '
except subprocess.CalledProcessError as e:
time = ''
if args.clair3_path is not None and args.platform != 'ilmn' and args.phase_tumor:
if args.normal_vcf_fn:
normal_vcf_fn = args.normal_vcf_fn
else:
normal_vcf_fn = clair3_output_path + '/clair3_normal_output/merge_output.vcf.gz'
echo_list.append("[INFO] Call Germline Variants in Normal BAM using Clair3")
clair3_normal_command = '( ' + time + args.clair3_path + '/run_clair3.sh'
clair3_normal_command += ' --bam_fn ' + args.normal_bam_fn
clair3_normal_command += ' --ref_fn ' + args.ref_fn
clair3_normal_command += ' --model_path ' + args.clair3_model_path
clair3_normal_command += ' --platform ' + args.platform
clair3_normal_command += ' --threads ' + str(args.threads)
clair3_normal_command += ' --output ' + clair3_output_path + '/clair3_normal_output'
clair3_normal_command += ' --ctg_name=' + args.clair3_option.ctg_name_str
clair3_normal_command += ' --samtools=' + args.samtools
clair3_normal_command += ' --pypy=' + args.pypy
clair3_normal_command += ' --python=' + args.python
clair3_normal_command += ' --min_coverage=' + args.clair3_option.min_coverage
clair3_normal_command += ' --snp_min_af=' + args.clair3_option.snp_min_af
clair3_normal_command += ' --indel_min_af=' + args.clair3_option.indel_min_af
if args.clair3_option.longphase_for_phasing is True:
clair3_normal_command += ' --longphase_for_phasing '
clair3_normal_command += ' ) 2>&1 | tee ' + args.output_dir + '/logs/clair3_log/1_CLAIR3_NORMAL.log'
commands_list.append(clair3_normal_command)
echo_list.append("[INFO] Call Germline Variant in Tumor BAM using Clair3")
clair3_tumor_command = '( ' + time + args.clair3_path + '/run_clair3.sh'
clair3_tumor_command += ' --bam_fn ' + args.tumor_bam_fn
clair3_tumor_command += ' --ref_fn ' + args.ref_fn
clair3_tumor_command += ' --model_path ' + args.clair3_model_path
clair3_tumor_command += ' --platform ' + args.platform
clair3_tumor_command += ' --threads ' + str(args.threads)
clair3_tumor_command += ' --output ' + clair3_output_path + '/clair3_tumor_output'
clair3_tumor_command += ' --ctg_name=' + args.clair3_option.ctg_name_str
clair3_tumor_command += ' --samtools=' + args.samtools
clair3_tumor_command += ' --pypy=' + args.pypy
clair3_tumor_command += ' --python=' + args.python
clair3_tumor_command += ' --min_coverage=' + args.clair3_option.min_coverage
clair3_tumor_command += ' --snp_min_af=' + args.clair3_option.snp_min_af
clair3_tumor_command += ' --indel_min_af=' + args.clair3_option.indel_min_af
if args.clair3_option.longphase_for_phasing is True:
clair3_tumor_command += ' --longphase_for_phasing '
clair3_tumor_command += ' ) 2>&1 | tee ' + args.output_dir + '/logs/clair3_log/2_CLAIR3_TUMOR.log'
commands_list.append(clair3_tumor_command)
echo_list.append("[INFO] Select Heterozygous SNP for Phasing")
ssp_command = '( ' + time + args.parallel
ssp_command += ' --joblog ' + args.output_dir + '/logs/clair3_log/parallel_1_select_hetero_snp_for_phasing.log'
ssp_command += ' -j ' + str(args.threads)
ssp_command += ' ' + args.pypy + ' ' + main_entry + ' select_hetero_snp_for_phasing'
ssp_command += ' --tumor_vcf_fn ' + clair3_output_path + '/clair3_tumor_output/merge_output.vcf.gz'
ssp_command += ' --normal_vcf_fn ' + normal_vcf_fn
ssp_command += ' --output_folder ' + clair3_output_path + '/vcf'
ssp_command += ' --ctg_name {1}'
ssp_command += ' --use_heterozygous_snp_in_normal_sample_for_intermediate_phasing True' if args.use_heterozygous_snp_in_normal_sample_for_intermediate_phasing else ""
ssp_command += ' --use_heterozygous_snp_in_tumor_sample_for_intermediate_phasing True' if args.use_heterozygous_snp_in_tumor_sample_for_intermediate_phasing else ""
ssp_command += ' --use_heterozygous_indel_for_intermediate_phasing True' if args.use_heterozygous_indel_for_intermediate_phasing else ""
ssp_command += ' :::: ' + args.output_dir + '/tmp/CONTIGS'
ssp_command += ' ) 2>&1 | tee ' + args.output_dir + '/logs/clair3_log/1_select_hetero_snp_for_phasing.log'
commands_list.append(ssp_command)
if args.phase_normal:
echo_list.append("[INFO] Phase the Normal BAM")
if args.clair3_option.longphase_for_phasing is not None:
pn_command = '( ' + time + args.parallel
pn_command += ' --joblog ' + args.output_dir + '/logs/clair3_log/parallel_2_phase_normal.log'
pn_command += ' -j ' + str(args.threads)
pn_command += ' ' + args.longphase + ' phase '
pn_command += ' -s ' + clair3_output_path + '/vcf/{1}.vcf'
pn_command += ' -b ' + args.normal_bam_fn
pn_command += ' -r ' + args.ref_fn
pn_command += ' -t ' + str(args.threads)
pn_command += ' --indels ' if args.use_heterozygous_indel_for_intermediate_phasing else ""
pn_command += ' -o ' + clair3_output_path + '/phased_output/normal_phased_{1}'
pn_command += ' --ont' if args.platform == 'ont' else ' --pb'
pn_command += ' :::: ' + args.output_dir + '/tmp/CONTIGS'
pn_command += ' && ' + args.parallel
pn_command += ' -j ' + str(args.threads)
pn_command += ' bgzip -f ' + clair3_output_path + '/phased_output/normal_phased_{1}.vcf'
pn_command += ' :::: ' + args.output_dir + '/tmp/CONTIGS'
pn_command += ' ) 2>&1 | tee ' + args.output_dir + '/logs/clair3_log/2_phase_normal.log'
else:
pn_command = '( ' + time + args.parallel
pn_command += ' --joblog ' + args.output_dir + '/logs/clair3_log/parallel_2_phase_normal.log'
pn_command += ' -j ' + str(args.threads)
pn_command += ' ' + args.whatshap + ' phase '
pn_command += ' --output ' + clair3_output_path + '/phased_output/normal_phased_{1}.vcf.gz'
pn_command += ' --reference ' + args.ref_fn
pn_command += ' --chromosome {1}'
pn_command += ' --distrust-genotypes'
pn_command += ' --ignore-read-groups'
pn_command += ' ' + clair3_output_path + '/vcf/{1}.vcf'
pn_command += ' :::: ' + args.output_dir + '/tmp/CONTIGS'
pn_command += ' ) 2>&1 | tee ' + args.output_dir + '/logs/clair3_log/2_phase_normal.log'
tabix_command = args.parallel + ' -j ' + str(args.threads)
tabix_command += ' tabix' + ' -f -p vcf'
tabix_command += ' ' + clair3_output_path + '/phased_output/normal_phased_{1}.vcf.gz'