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run_clairs_to
executable file
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run_clairs_to
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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
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_to.py")
MAX_STEP = 20
OutputPath = namedtuple('OutputPath', [
'log_path',
'tmp_file_path',
'split_bed_path',
'split_indel_bed_path',
'candidates_path',
'pileup_tensor_can_affirmative_path',
'pileup_tensor_can_negational_path',
'vcf_output_path',
])
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)
split_indel_bed_path = folder_path_from(os.path.join(tmp_file_path, 'split_indel_beds'),
create_not_found=True) if not args.disable_indel_calling else None
candidates_path = folder_path_from(os.path.join(tmp_file_path, 'candidates'), create_not_found=True)
pileup_tensor_can_affirmative_path = folder_path_from(os.path.join(tmp_file_path, 'pileup_tensor_can_affirmative'),
create_not_found=True)
pileup_tensor_can_negational_path = folder_path_from(os.path.join(tmp_file_path, 'pileup_tensor_can_negational'),
create_not_found=True)
vcf_output_path = folder_path_from(os.path.join(tmp_file_path, 'vcf_output'), create_not_found=True)
if args.platform != 'ilmn':
phasing_log_path = folder_path_from(os.path.join(args.output_dir, 'logs', 'phasing_log'), create_not_found=True)
phasing_phased_vcf_output_path = folder_path_from(
os.path.join(tmp_file_path, 'phasing_output/phased_vcf_output'), create_not_found=True)
phasing_phased_bam_output_path = folder_path_from(
os.path.join(tmp_file_path, 'phasing_output/phased_bam_output'), create_not_found=True)
output_path = OutputPath(log_path=log_path,
tmp_file_path=tmp_file_path,
split_bed_path=split_bed_path,
split_indel_bed_path=split_indel_bed_path,
candidates_path=candidates_path,
pileup_tensor_can_affirmative_path=pileup_tensor_can_affirmative_path,
pileup_tensor_can_negational_path=pileup_tensor_can_negational_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 None
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=True):
expand_region_size = param.no_of_positions
if not expand_region:
expand_region_size = 0
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_indel_bed_file_provided = args.call_indels_only_in_these_regions 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')
split_indel_bed_path = os.path.join(args.output_dir, 'tmp',
'split_indel_beds') if not args.disable_indel_calling else None
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))
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)
found_contig = tumor_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)
if not args.disable_indel_calling and is_indel_bed_file_provided:
split_extend_bed(bed_fn=args.call_indels_only_in_these_regions, output_fn=split_indel_bed_path,
contig_set=contig_set, expand_region=False)
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
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.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)
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 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 not args.disable_indel_calling and args.platform not in {'ont_r10_dorado_sup_4khz', 'ont_r10_dorado_hac_4khz',
'ont_r10_dorado_sup_5khz',
'ont_r10_guppy_sup_4khz', 'ont_r10_guppy_hac_5khz',
'ont_r10_dorado_4khz',
'ont_r10_dorado_5khz', 'ont_r10_guppy',
'ont_r10_guppy_4khz', 'ont_r10_guppy_5khz', 'ilmn',
'hifi_revio'}:
sys.exit(log_error(
"[ERROR] Indel calling only support 'ont_r10_dorado_sup_4khz', 'ont_r10_dorado_hac_4khz', 'ont_r10_dorado_sup_5khz', 'ont_r10_guppy_sup_4khz', 'ont_r10_guppy_hac_5khz', 'ilmn', and 'hifi_revio' platform"))
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.snv_pileup_affirmative_model_path is None:
if args.platform == 'ont_r10_guppy_sup_4khz' or args.platform == 'ont_r10_guppy_4khz' or args.platform == 'ont_r10_guppy':
args.snv_pileup_affirmative_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_guppy_sup_4khz', 'pileup_affirmative.pkl')
elif args.platform == 'ont_r10_dorado_sup_5khz' or args.platform == 'ont_r10_dorado_5khz':
args.snv_pileup_affirmative_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_sup_5khz', 'pileup_affirmative.pkl')
elif args.platform == 'ont_r10_dorado_sup_4khz' or args.platform == 'ont_r10_dorado_4khz':
args.snv_pileup_affirmative_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_sup_4khz', 'pileup_affirmative.pkl')
elif args.platform == 'ont_r10_dorado_hac_4khz':
args.snv_pileup_affirmative_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_hac_4khz', 'pileup_affirmative.pkl')
elif args.platform == 'ont_r10_guppy_hac_5khz' or args.platform == 'ont_r10_guppy_5khz':
args.snv_pileup_affirmative_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_guppy_hac_5khz', 'pileup_affirmative.pkl')
else:
args.snv_pileup_affirmative_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
args.platform, 'pileup_affirmative.pkl')
if args.snv_pileup_negational_model_path is None:
if args.platform == 'ont_r10_guppy_sup_4khz' or args.platform == 'ont_r10_guppy_4khz' or args.platform == 'ont_r10_guppy':
args.snv_pileup_negational_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_guppy_sup_4khz', 'pileup_negational.pkl')
elif args.platform == 'ont_r10_dorado_sup_5khz' or args.platform == 'ont_r10_dorado_5khz':
args.snv_pileup_negational_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_sup_5khz', 'pileup_negational.pkl')
elif args.platform == 'ont_r10_dorado_sup_4khz' or args.platform == 'ont_r10_dorado_4khz':
args.snv_pileup_negational_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_sup_4khz', 'pileup_negational.pkl')
elif args.platform == 'ont_r10_dorado_hac_4khz':
args.snv_pileup_negational_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_hac_4khz', 'pileup_negational.pkl')
elif args.platform == 'ont_r10_guppy_hac_5khz' or args.platform == 'ont_r10_guppy_5khz':
args.snv_pileup_negational_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_guppy_hac_5khz', 'pileup_negational.pkl')
else:
args.snv_pileup_negational_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
args.platform, 'pileup_negational.pkl')
args.snv_pileup_affirmative_model_path = file_path_from(file_name=args.snv_pileup_affirmative_model_path,
exit_on_not_found=True, is_directory=False,
allow_none=False)
args.snv_pileup_negational_model_path = file_path_from(file_name=args.snv_pileup_negational_model_path,
exit_on_not_found=True, is_directory=False, allow_none=False)
if args.snv_likelihood_matrix_data is None:
if args.platform == 'ont_r10_guppy_sup_4khz' or args.platform == 'ont_r10_guppy_4khz' or args.platform == 'ont_r10_guppy':
args.snv_likelihood_matrix_data = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_guppy_sup_4khz', 'likelihood_matrix.txt')
elif args.platform == 'ont_r10_dorado_sup_5khz' or args.platform == 'ont_r10_dorado_5khz':
args.snv_likelihood_matrix_data = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_sup_5khz', 'likelihood_matrix.txt')
elif args.platform == 'ont_r10_dorado_sup_4khz' or args.platform == 'ont_r10_dorado_4khz':
args.snv_likelihood_matrix_data = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_sup_4khz', 'likelihood_matrix.txt')
elif args.platform == 'ont_r10_dorado_hac_4khz':
args.snv_likelihood_matrix_data = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_hac_4khz', 'likelihood_matrix.txt')
elif args.platform == 'ont_r10_guppy_hac_5khz' or args.platform == 'ont_r10_guppy_5khz':
args.snv_likelihood_matrix_data = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_guppy_hac_5khz', 'likelihood_matrix.txt')
else:
args.snv_likelihood_matrix_data = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models', args.platform,
'likelihood_matrix.txt')
if not args.disable_indel_calling:
if args.indel_pileup_affirmative_model_path is None:
if args.platform == 'ont_r10_guppy_sup_4khz' or args.platform == 'ont_r10_guppy_4khz' or args.platform == 'ont_r10_guppy':
args.indel_pileup_affirmative_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_guppy_sup_4khz', 'indel',
'pileup_affirmative.pkl')
elif args.platform == 'ont_r10_dorado_sup_5khz' or args.platform == 'ont_r10_dorado_5khz':
args.indel_pileup_affirmative_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_sup_5khz', 'indel',
'pileup_affirmative.pkl')
elif args.platform == 'ont_r10_dorado_sup_4khz' or args.platform == 'ont_r10_dorado_4khz':
args.indel_pileup_affirmative_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_sup_4khz', 'indel',
'pileup_affirmative.pkl')
elif args.platform == 'ont_r10_dorado_hac_4khz':
args.indel_pileup_affirmative_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_hac_4khz', 'indel',
'pileup_affirmative.pkl')
elif args.platform == 'ont_r10_guppy_hac_5khz' or args.platform == 'ont_r10_guppy_5khz':
args.indel_pileup_affirmative_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_guppy_hac_5khz', 'indel',
'pileup_affirmative.pkl')
else:
args.indel_pileup_affirmative_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
args.platform, 'indel',
'pileup_affirmative.pkl')
if args.indel_pileup_negational_model_path is None:
if args.platform == 'ont_r10_guppy_sup_4khz' or args.platform == 'ont_r10_guppy_4khz' or args.platform == 'ont_r10_guppy':
args.indel_pileup_negational_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_guppy_sup_4khz', 'indel',
'pileup_negational.pkl')
elif args.platform == 'ont_r10_dorado_sup_5khz' or args.platform == 'ont_r10_dorado_5khz':
args.indel_pileup_negational_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_sup_5khz', 'indel',
'pileup_negational.pkl')
elif args.platform == 'ont_r10_dorado_sup_4khz' or args.platform == 'ont_r10_dorado_4khz':
args.indel_pileup_negational_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_sup_4khz', 'indel',
'pileup_negational.pkl')
elif args.platform == 'ont_r10_dorado_hac_4khz':
args.indel_pileup_negational_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_hac_4khz', 'indel',
'pileup_negational.pkl')
elif args.platform == 'ont_r10_guppy_hac_5khz' or args.platform == 'ont_r10_guppy_5khz':
args.indel_pileup_negational_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_guppy_hac_5khz', 'indel',
'pileup_negational.pkl')
else:
args.indel_pileup_negational_model_path = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
args.platform, 'indel', 'pileup_negational.pkl')
args.indel_pileup_affirmative_model_path = file_path_from(file_name=args.indel_pileup_affirmative_model_path,
exit_on_not_found=True,
is_directory=False, allow_none=False)
args.indel_pileup_negational_model_path = file_path_from(file_name=args.indel_pileup_negational_model_path,
exit_on_not_found=True,
is_directory=False, allow_none=False)
if args.indel_likelihood_matrix_data is None:
if args.platform == 'ont_r10_guppy_sup_4khz' or args.platform == 'ont_r10_guppy_4khz' or args.platform == 'ont_r10_guppy':
args.indel_likelihood_matrix_data = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_guppy_sup_4khz', 'indel',
'likelihood_matrix.txt')
elif args.platform == 'ont_r10_dorado_sup_5khz' or args.platform == 'ont_r10_dorado_5khz':
args.indel_likelihood_matrix_data = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_sup_5khz', 'indel',
'likelihood_matrix.txt')
elif args.platform == 'ont_r10_dorado_sup_4khz' or args.platform == 'ont_r10_dorado_4khz':
args.indel_likelihood_matrix_data = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_sup_4khz', 'indel',
'likelihood_matrix.txt')
elif args.platform == 'ont_r10_dorado_hac_4khz':
args.indel_likelihood_matrix_data = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_dorado_hac_4khz', 'indel',
'likelihood_matrix.txt')
elif args.platform == 'ont_r10_guppy_hac_5khz' or args.platform == 'ont_r10_guppy_5khz':
args.indel_likelihood_matrix_data = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
'ont_r10_guppy_hac_5khz', 'indel',
'likelihood_matrix.txt')
else:
args.indel_likelihood_matrix_data = os.path.join(args.conda_prefix, 'bin', 'clairs-to_models',
args.platform,
'indel', 'likelihood_matrix.txt')
default_gnomad_resource = os.path.join(args.conda_prefix, 'bin', 'clairs-to_databases',
'gnomad.r2.1.af-ge-0.001.sites.vcf.gz')
default_dbsnp_resource = os.path.join(args.conda_prefix, 'bin', 'clairs-to_databases',
'dbsnp.b138.non-somatic.sites.vcf.gz')
default_1kgpon_resource = os.path.join(args.conda_prefix, 'bin', 'clairs-to_databases', '1000g-pon.sites.vcf.gz')
default_gnomad_resource = file_path_from(file_name=default_gnomad_resource, exit_on_not_found=True,
is_directory=False, allow_none=True)
default_dbsnp_resource = file_path_from(file_name=default_dbsnp_resource, exit_on_not_found=True,
is_directory=False, allow_none=True)
default_1kgpon_resource = file_path_from(file_name=default_1kgpon_resource, exit_on_not_found=True,
is_directory=False, allow_none=True)
default_indel_bed_resource = os.path.join(args.conda_prefix, 'bin', 'clairs-to_databases',
'GRCh38Chr1-22XY_excludedGIABStratifV3.3AllDifficultRegions_includedCMRGv1.0.bed')
if args.bed_fn is not None and args.call_indels_only_in_these_regions is not None:
logging(log_warning(
"[WARNING] `--bed_fn` will supersede `--call_indels_only_in_these_regions`."))
if args.call_indels_only_in_these_regions is None:
args.call_indels_only_in_these_regions = file_path_from(file_name=default_indel_bed_resource,
exit_on_not_found=True,
is_directory=False, allow_none=True)
if args.panel_of_normals is not None:
if args.panel_of_normals == 'None' or args.panel_of_normals == 'Null' or args.panel_of_normals == ' ':
args.disable_nonsomatic_tagging = True
else:
panel_of_normals_list = list(args.panel_of_normals.split(','))
for pon in panel_of_normals_list:
pon_path = file_path_from(file_name=str(pon), exit_on_not_found=True, is_directory=False,
allow_none=True)
if str(default_gnomad_resource) not in panel_of_normals_list:
logging(log_warning(
"[INFO] Default {} PoN is not included!".format(str(default_gnomad_resource))))
if str(default_dbsnp_resource) not in panel_of_normals_list:
logging(log_warning(
"[INFO] Default {} PoN is not included!".format(str(default_dbsnp_resource))))
if str(default_1kgpon_resource) not in panel_of_normals_list:
logging(log_warning(
"[INFO] Default {} PoN is not included!".format(str(default_1kgpon_resource))))
args.panel_of_normals = args.panel_of_normals
if args.panel_of_normals_require_allele_matching is not None:
ori_panel_of_normals_require_allele_matching_list = list(
args.panel_of_normals_require_allele_matching.split(','))
if len(panel_of_normals_list) != len(ori_panel_of_normals_require_allele_matching_list):
logging(log_warning(
"[WARNING] Please use `--panel_of_normals_require_allele_matching` together with `--panel_of_normals`."))
if args.panel_of_normals_require_allele_matching == 'None' or args.panel_of_normals_require_allele_matching == 'Null' or args.panel_of_normals_require_allele_matching == ' ':
panel_of_normals_require_allele_matching_list = ['True'] * len(panel_of_normals_list)
args.panel_of_normals_require_allele_matching = ','.join(
panel_of_normals_require_allele_matching_list)
else:
args.panel_of_normals_require_allele_matching = args.panel_of_normals_require_allele_matching
else:
panel_of_normals_require_allele_matching_list = ['True'] * len(panel_of_normals_list)
args.panel_of_normals_require_allele_matching = ','.join(panel_of_normals_require_allele_matching_list)
else:
args.panel_of_normals = str(default_gnomad_resource) + ',' + str(default_dbsnp_resource) + ',' + str(
default_1kgpon_resource)
args.panel_of_normals_require_allele_matching = 'True' + ',' + 'True' + ',' + 'False'
if args.whatshap is None:
args.whatshap = os.path.join(args.conda_prefix, 'bin', 'whatshap')
if args.longphase is None:
args.longphase = os.path.join(args.conda_prefix, 'bin', 'longphase')
args.use_longphase_for_intermediate_phasing = False if args.use_whatshap_for_intermediate_phasing is True else True
if args.use_longphase_for_intermediate_phasing and not os.path.exists(args.longphase):
sys.exit(log_error("[ERROR] Cannot find longphase at {}".format(args.longphase)))
if args.use_whatshap_for_intermediate_phasing and not os.path.exists(args.whatshap):
sys.exit(log_error("[ERROR] Cannot find whatshap at {}".format(args.whatshap)))
if args.use_longphase_for_intermediate_haplotagging is None:
args.use_longphase_for_intermediate_haplotagging = True
if args.use_longphase_for_intermediate_haplotagging and not os.path.exists(args.longphase):
sys.exit(log_error("[ERROR] Cannot find longphase at {}".format(args.longphase)))
if args.snv_min_af is None:
args.snv_min_af = param.snv_min_af
if args.indel_min_af is None:
if not args.disable_indel_calling:
args.indel_min_af = param.indel_min_af
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.min_bq is None:
args.min_bq = param.min_bq_dict[args.platform]
if args.platform not in {'ont_r10_dorado_sup_4khz', 'ont_r10_dorado_hac_4khz', 'ont_r10_dorado_sup_5khz',
'ont_r10_guppy_sup_4khz', 'ont_r10_guppy_hac_5khz', 'ont_r10_dorado_4khz',
'ont_r10_dorado_5khz', 'ont_r10_guppy', 'ont_r10_guppy_4khz', 'ont_r10_guppy_5khz', 'ilmn',
'hifi_revio'}:
logging(log_error(
'[ERROR] Invalid platform input, optional: {ont_r10_dorado_sup_4khz, ont_r10_dorado_hac_4khz, ont_r10_dorado_sup_5khz, ont_r10_guppy_sup_4khz, ont_r10_guppy_hac_5khz, ilmn, hifi_revio}'))
if args.qual is None:
args.qual = param.min_thred_qual[args.platform] if args.platform in param.min_thred_qual else \
param.min_thred_qual['ont']
if args.qual_cutoff_phaseable_region is None:
args.qual_cutoff_phaseable_region = param.min_phaseable_thred_qual[
args.platform] if args.platform in param.min_phaseable_thred_qual else \
param.min_phaseable_thred_qual['ont']
if args.qual_cutoff_unphaseable_region is None:
args.qual_cutoff_unphaseable_region = param.min_unphaseable_thred_qual[
args.platform] if args.platform in param.min_unphaseable_thred_qual else \
param.min_unphaseable_thred_qual['ont']
else:
if args.qual_cutoff_phaseable_region is not None or args.qual_cutoff_unphaseable_region is not None:
logging(log_warning(
"[WARNING] `--qual` will supersede `--qual_cutoff_phaseable_region` and `--qual_cutoff_unphaseable_region`."))
args.qual_cutoff_phaseable_region = args.qual
args.qual_cutoff_unphaseable_region = args.qual
if args.skip_steps is not None:
check_skip_steps_legal(args)
if args.disable_intermediate_phasing:
args.phase_tumor = False
if args.phase_tumor is None:
if args.genotyping_mode_vcf_fn is not None:
logging(log_warning(
"[WARNING] 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
if not args.disable_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.disable_verdict = True
logging(log_warning(
"[WARNING] Verdict currently only works for GRCh38 reference genome, apply the --disable_verdict option!"))
if args.cna_resource_dir is None:
args.cna_resource_dir = os.path.join(args.conda_prefix, 'bin', 'clairs-to_cna_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.disable_verdict = True
logging(log_warning(
"[WARNING] The allele counter {} is not found, apply the --disable_verdict option!".format(
args.allele_counter_dir)))
if not os.path.exists(args.cna_resource_dir):
args.disable_verdict = True
logging(log_warning(
"[WARNING] The CNA resource directory {} is not found, apply the --disable_verdict option!".format(
args.cna_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 option and disable --do_not_print_nonsomatic_calls in genotyping mode!"))
args.print_ref_calls = True
args.do_not_print_nonsomatic_calls = False
args.disable_indel_calling = True
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=0, exit_out_of_range=True)
legal_range_from(param_name="qual_cutoff_phaseable_region", x=args.qual_cutoff_phaseable_region, min_num=0,
exit_out_of_range=True)
legal_range_from(param_name="qual_cutoff_unphaseable_region", x=args.qual_cutoff_unphaseable_region, min_num=0,
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="min_bq", x=args.min_bq, 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)
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)
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] 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] SNV OUTPUT VCF PATH: {}".format(os.path.join(args.output_dir, args.snv_output_prefix + '.vcf.gz')))
logging(
"[INFO] INDEL OUTPUT VCF PATH: {}".format(os.path.join(args.output_dir, args.indel_output_prefix + '.vcf.gz')))
logging("[INFO] SNV MINIMUM AF: {}".format(args.snv_min_af))
logging("[INFO] INDEL MINIMUM AF: {}".format(args.indel_min_af))
logging("[INFO] SNV PILEUP AFFIRMATIVE MODEL PATH: {}".format(args.snv_pileup_affirmative_model_path))
logging("[INFO] SNV PILEUP NEGATIONAL MODEL PATH: {}".format(args.snv_pileup_negational_model_path))
logging("[INFO] INDEL PILEUP AFFIRMATIVE MODEL PATH: {}".format(args.indel_pileup_affirmative_model_path))
logging("[INFO] INDEL PILEUP NEGATIONAL MODEL PATH: {}".format(args.indel_pileup_negational_model_path))
logging("[INFO] BED FILE PATH: {}".format(args.bed_fn))
logging("[INFO] SPECIFIED REGIONS FOR CALLING: {}".format(args.region))
logging("[INFO] REGIONS FOR INDEL CALLING: {}".format(args.call_indels_only_in_these_regions))
logging("[INFO] CONTIGS FOR CALLING: {}".format(args.ctg_name))
logging("[INFO] ENABLE INCLUDING ALL CTGS FOR CALLING: {}".format(args.include_all_ctgs))
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] PANEL OF NORMALS: {}".format(args.panel_of_normals))
logging("[INFO] PANEL OF NORMALS REQUIRE ALLELE MATCHING: {}".format(args.panel_of_normals_require_allele_matching))
logging("[INFO] CHUNK SIZE: {}".format(args.chunk_size))
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] WHATSHAP BINARY PATH: {}".format(args.whatshap))
logging("[INFO] ENABLE DRY RUN: {}".format(args.dry_run))
logging("[INFO] ENABLE REMOVING INTERMEDIATE FILES: {}".format(args.remove_intermediate_dir))
logging("[INFO] DISABLE INTERMEDIATE PHASING: {}".format(args.disable_intermediate_phasing))
logging("[INFO] DISABLE INDEL CALLING: {}".format(args.disable_indel_calling))
logging("[INFO] ENABLE PRINTING REFERENCE CALLS: {}".format(args.print_ref_calls))
logging("[INFO] ENABLE APPLYING REALIGNMENT: {}".format(args.enable_realignment))
logging("[INFO] ENABLE APPLYING POSTFILTERING: {}".format(args.enable_postfilter))
logging("[INFO] ENABLE APPLYING HAPLOTYPE FILTERING: {}".format(args.apply_haplotype_filtering))
logging("[INFO] DISABLE APPLYING VERDICT: {}".format(args.disable_verdict))
logging("[INFO] DISABLE APPLYING NON-SOMATIC TAGGING: {}".format(args.disable_nonsomatic_tagging))
logging("[INFO] DISABLE PRINTING NON-SOMATIC CALLS: {}".format(args.do_not_print_nonsomatic_calls))
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 += '--ref_fn {} '.format(args.ref_fn)
cmdline += '--threads {} '.format(args.threads)
cmdline += '--platform {} '.format(args.platform)
cmdline += '--output_dir {} '.format(args.output_dir)
cmdline += '--snv_output_prefix {} '.format(args.snv_output_prefix) if args.snv_output_prefix != "snv" else ""
cmdline += '--indel_output_prefix {} '.format(
args.indel_output_prefix) if args.indel_output_prefix != "indel" else ""
cmdline += '--sample_name {} '.format(args.sample_name) if args.sample_name != "SAMPLE" else ""
cmdline += '--ctg_name {} '.format(args.ctg_name) if args.ctg_name is not None else ""
cmdline += '--include_all_ctgs ' if args.include_all_ctgs 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 += '--call_indels_only_in_these_regions {} '.format(
args.call_indels_only_in_these_regions) if args.call_indels_only_in_these_regions 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 += '--qual_cutoff_phaseable_region {} '.format(
args.qual_cutoff_phaseable_region) if args.qual_cutoff_phaseable_region is not None else ""
cmdline += '--qual_cutoff_unphaseable_region {} '.format(
args.qual_cutoff_unphaseable_region) if args.qual_cutoff_unphaseable_region 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 += '--min_coverage {} '.format(args.min_coverage) if args.min_coverage is not None else ""
cmdline += '--min_bq {} '.format(args.min_bq) if args.min_bq is not None else ""
cmdline += '--chunk_size {} '.format(args.chunk_size) if args.chunk_size is not None else ""
cmdline += '--dry_run ' if args.dry_run else ""
cmdline += '--remove_intermediate_dir ' if args.remove_intermediate_dir else ""
cmdline += '--panel_of_normals {} '.format(args.panel_of_normals) if args.panel_of_normals is not None else ""
cmdline += '--panel_of_normals_require_allele_matching {} '.format(
args.panel_of_normals_require_allele_matching) if args.panel_of_normals_require_allele_matching is not None else ""
cmdline += '--snv_pileup_affirmative_model_path {} '.format(
args.snv_pileup_affirmative_model_path) if args.snv_pileup_affirmative_model_path is not None else ""
cmdline += '--snv_pileup_negational_model_path {} '.format(
args.snv_pileup_negational_model_path) if args.snv_pileup_negational_model_path is not None else ""
cmdline += '--indel_pileup_affirmative_model_path {} '.format(
args.indel_pileup_affirmative_model_path) if args.indel_pileup_affirmative_model_path is not None else ""
cmdline += '--indel_pileup_negational_model_path {} '.format(
args.indel_pileup_negational_model_path) if args.indel_pileup_negational_model_path is not None 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 += '--longphase {} '.format(args.longphase) if args.longphase is not None else ""
cmdline += '--whatshap {} '.format(args.whatshap) if args.whatshap is not None else ""
cmdline += '--use_longphase_for_intermediate_phasing {} '.format(
args.use_longphase_for_intermediate_phasing) if args.use_longphase_for_intermediate_phasing is not None else ""
cmdline += '--use_whatshap_for_intermediate_phasing {} '.format(
args.use_whatshap_for_intermediate_phasing) if args.use_whatshap_for_intermediate_phasing is not None else ""