def __init__(self, chromosome_name, bam_file_path, draft_file_path,
truth_bam, train_mode):
"""
Initialize a manager object
:param chromosome_name: Name of the chromosome
:param bam_file_path: Path to the BAM file
:param draft_file_path: Path to the reference FASTA file
:param truth_bam: Path to the truth sequence to reference mapping file
"""
# --- initialize handlers ---
# create objects to handle different files and query
self.bam_path = bam_file_path
self.fasta_path = draft_file_path
self.bam_handler = PEPPER.BAM_handler(bam_file_path)
self.fasta_handler = PEPPER.FASTA_handler(draft_file_path)
self.train_mode = train_mode
self.downsample_rate = 1.0
self.truth_bam_handler = None
if self.train_mode:
self.truth_bam_handler = PEPPER.BAM_handler(truth_bam)
# --- initialize names ---
# name of the chromosome
self.chromosome_name = chromosome_name
def make_train_images(bam_filepath, truth_bam_filepath, fasta_filepath, region,
region_bed, output_dir, threads):
"""
GENERATE IMAGES WITHOUT ANY LABELS. THIS IS USED BY pepper.py
:param bam_filepath: Path to the input bam file.
:param truth_bam_filepath: Path to the bam where truth is aligned to the assembly.
:param fasta_filepath: Path to the input fasta file.
:param region: Specific region of interest.
:param output_dir: Path to the output directory.
:param threads: Number of threads to use.
:return:
"""
# check the bam file
if not os.path.isfile(bam_filepath) or not PEPPER.BAM_handler(
bam_filepath):
sys.stderr.write("[" +
str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] ERROR: CAN NOT LOCATE BAM FILE.\n")
exit(1)
# check the truth bam file
if not os.path.isfile(truth_bam_filepath) or not PEPPER.BAM_handler(
truth_bam_filepath):
sys.stderr.write("[" +
str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] ERROR: CAN NOT LOCATE TRUTH BAM FILE.\n")
exit(1)
# check the fasta file
if not os.path.isfile(fasta_filepath):
sys.stderr.write("[" +
str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] ERROR: CAN NOT LOCATE FASTA FILE.\n")
exit(1)
# check the output directory
output_dir = UserInterfaceSupport.handle_output_directory(
os.path.abspath(output_dir))
# check number of threads
if threads <= 0:
sys.stderr.write("[" +
str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] ERROR: THREAD NEEDS TO BE >=0.\n")
exit(1)
# get the list of contigs
contig_list = UserInterfaceSupport.get_chromosome_list(
region, fasta_filepath, bam_filepath, region_bed)
# call the parallelization method to generate images in parallel
UserInterfaceSupport.chromosome_level_parallelization(
contig_list,
bam_filepath,
fasta_filepath,
truth_bam=truth_bam_filepath,
output_path=output_dir,
total_threads=threads,
train_mode=True)
def polish(bam_filepath, fasta_filepath, output_path, threads, region,
model_path, batch_size, gpu_mode, device_ids, num_workers):
"""
Run all the sub-modules to polish an input assembly.
"""
# check the bam file
if not os.path.isfile(bam_filepath) or not PEPPER.BAM_handler(
bam_filepath):
sys.stderr.write("[" +
str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] ERROR: CAN NOT LOCATE BAM FILE.\n")
exit(1)
# check the fasta file
if not os.path.isfile(fasta_filepath):
sys.stderr.write("[" +
str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] ERROR: CAN NOT LOCATE FASTA FILE.\n")
exit(1)
# check the model file
if not os.path.isfile(model_path):
sys.stderr.write("[" +
str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] ERROR: CAN NOT LOCATE MODEL FILE.\n")
exit(1)
# check number of threads
if threads <= 0:
sys.stderr.write("[" +
str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] ERROR: THREAD NEEDS TO BE >=0.\n")
exit(1)
# check batch_size
if batch_size <= 0:
sys.stderr.write("[" +
str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] ERROR: batch_size NEEDS TO BE >0.\n")
exit(1)
# check num_workers
if num_workers < 0:
sys.stderr.write("[" +
str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] ERROR: num_workers NEEDS TO BE >=0.\n")
exit(1)
callers = threads
threads_per_caller = int(threads / max(1, callers))
# check number of threads
if threads_per_caller <= 0:
sys.stderr.write("[" +
str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] ERROR: THREAD PER CALLER NEEDS TO BE >=0.\n")
exit(1)
# check if gpu inference can be done
if gpu_mode:
if not torch.cuda.is_available():
sys.stderr.write(
"[" + str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] ERROR: TORCH IS NOT BUILT WITH CUDA.\n")
sys.stderr.write(
"SEE TORCH CAPABILITY:\n$ python3\n"
">>> import torch \n"
">>> torch.cuda.is_available()\n If true then cuda is avilable"
)
exit(1)
# check if all devices are available
if device_ids is not None:
device_ids = [int(i) for i in device_ids.split(',')]
for device_id in device_ids:
major_capable, minor_capable = torch.cuda.get_device_capability(
device=device_id)
if major_capable < 0:
sys.stderr.write(
"[" + str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] ERROR: GPU DEVICE: " + str(device_id) +
" IS NOT CUDA CAPABLE.\n")
sys.stderr.write(
"Try running: $ python3\n"
">>> import torch \n"
">>> torch.cuda.get_device_capability(device=" +
str(device_id) + ")\n")
exit(1)
else:
sys.stderr.write(
"[" + str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] INFO: CAPABILITY OF GPU#" + str(device_id) + ":\t" +
str(major_capable) + "-" + str(minor_capable) + "\n")
timestr = time.strftime("%m%d%Y_%H%M%S")
# run directories
output_dir = UserInterfaceSupport.handle_output_directory(output_path)
image_output_directory = output_dir + "images_" + str(timestr) + "/"
prediction_output_directory = output_dir + "predictions_" + str(
timestr) + "/"
sys.stderr.write("[" + str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] INFO: RUN-ID: " + str(timestr) + "\n")
sys.stderr.write("[" + str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] INFO: IMAGE OUTPUT: " + str(image_output_directory) +
"\n")
sys.stderr.write("[" + str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] STEP 1: GENERATING IMAGES\n")
sys.stderr.flush()
# call the parallelization method to generate images in parallel
make_images(bam_filepath, fasta_filepath, region, image_output_directory,
threads)
sys.stderr.write("[" + str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] STEP 2: RUNNING INFERENCE\n")
sys.stderr.write("[" + str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] INFO: PREDICTION OUTPUT: " +
str(prediction_output_directory) + "\n")
sys.stderr.flush()
call_consensus(image_output_directory, model_path, batch_size, num_workers,
prediction_output_directory, device_ids, gpu_mode, threads)
sys.stderr.write("[" + str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] STEP 3: RUNNING STITCH\n")
sys.stderr.write("[" + str(datetime.now().strftime('%m-%d-%Y %H:%M:%S')) +
"] INFO: STITCH OUTPUT: " + str(output_dir) + "\n")
sys.stderr.flush()
perform_stitch(prediction_output_directory, output_dir, threads)
def get_chromosome_list(chromosome_names, ref_file, bam_file, region_bed):
"""
PARSES THROUGH THE CHROMOSOME PARAMETER TO FIND OUT WHICH REGIONS TO PROCESS
:param chromosome_names: NAME OF CHROMOSOME
:param ref_file: PATH TO THE REFERENCE FILE
:param bam_file: PATH TO BAM FILE
:return: LIST OF CHROMOSOME IN REGION SPECIFIC FORMAT
"""
if not chromosome_names and not region_bed:
fasta_handler = PEPPER.FASTA_handler(ref_file)
bam_handler = PEPPER.BAM_handler(bam_file)
bam_contigs = bam_handler.get_chromosome_sequence_names()
fasta_contigs = fasta_handler.get_chromosome_names()
common_contigs = list(set(fasta_contigs) & set(bam_contigs))
if len(common_contigs) == 0:
sys.stderr.write(
"[" + datetime.now().strftime('%m-%d-%Y %H:%M:%S') + "] " +
"ERROR: NO COMMON CONTIGS FOUND BETWEEN THE BAM FILE AND THE FASTA FILE."
)
sys.stderr.flush()
exit(1)
common_contigs = sorted(common_contigs,
key=UserInterfaceSupport.natural_key)
sys.stderr.write("[" +
datetime.now().strftime('%m-%d-%Y %H:%M:%S') +
"] INFO: COMMON CONTIGS FOUND: " +
str(common_contigs) + "\n")
sys.stderr.flush()
chromosome_name_list = []
for contig_name in common_contigs:
chromosome_name_list.append((contig_name, None))
return chromosome_name_list
if region_bed:
chromosome_name_list = []
with open(region_bed) as fp:
line = fp.readline()
cnt = 1
while line:
line_to_list = line.rstrip().split('\t')
chr_name, start_pos, end_pos = line_to_list[0], int(
line_to_list[1]), int(line_to_list[2])
region = sorted([start_pos, end_pos])
chromosome_name_list.append((chr_name, region))
line = fp.readline()
cnt += 1
return chromosome_name_list
split_names = chromosome_names.strip().split(',')
split_names = [name.strip() for name in split_names]
chromosome_name_list = []
for name in split_names:
# split on region
region = None
if ':' in name:
name_region = name.strip().split(':')
if len(name_region) != 2:
sys.stderr.write("ERROR: --region INVALID value.\n")
exit(0)
name, region = tuple(name_region)
region = region.strip().split('-')
region = [int(pos) for pos in region]
if len(region) != 2 or not region[0] <= region[1]:
sys.stderr.write("ERROR: --region INVALID value.\n")
exit(0)
range_split = name.split('-')
if len(range_split) > 1:
chr_prefix = ''
for p in name:
if p.isdigit():
break
else:
chr_prefix = chr_prefix + p
int_ranges = []
for item in range_split:
s = ''.join(i for i in item if i.isdigit())
int_ranges.append(int(s))
int_ranges = sorted(int_ranges)
for chr_seq in range(int_ranges[0], int_ranges[-1] + 1):
chromosome_name_list.append(
(chr_prefix + str(chr_seq), region))
else:
chromosome_name_list.append((name, region))
return chromosome_name_list