def test_strand_list_no_read_id_multiread(self):
"""See if reads ids found iterating through strand list containing
filenames, not read ids"""
strand_list = os.path.join(self.STRAND_LIST_DIR,
"strand_list_no_read_id.txt")
self._check_found_read_ids(
iterate_fast5_reads(self.MULTIREAD_DIR, strand_list=strand_list))
def test_strand_list_no_filename_single_reads(self):
"""See if read ids found iterating through single-read files in
directory with strand list"""
strand_list = os.path.join(self.STRAND_LIST_DIR,
"strand_list_no_filename.txt")
self._check_found_read_ids(
iterate_fast5_reads(self.READ_DIR, strand_list=strand_list))
def main():
args = get_parser().parse_args()
worker_kwarg_names = ['back_prob', 'localpen', 'minscore', 'trim']
model = helpers.load_model(args.model)
fast5_reads = fast5utils.iterate_fast5_reads(
args.read_dir, limit=args.limit, strand_list=args.input_strand_list,
recursive=args.recursive)
with helpers.open_file_or_stdout(args.output) as fh:
for res in imap_mp(
squiggle_match.worker, fast5_reads, threads=args.jobs,
fix_kwargs=helpers.get_kwargs(args, worker_kwarg_names),
unordered=True, init=squiggle_match.init_worker,
initargs=[model, args.references]):
if res is None:
continue
read_id, sig, score, path, squiggle, bases = res
bases = bases.decode('ascii')
fh.write('#{} {}\n'.format(read_id, score))
for i, (s, p) in enumerate(zip(sig, path)):
fh.write('{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n'.format(
read_id, i, s, p, bases[p], squiggle[p, 0], squiggle[p, 1],
squiggle[p, 2]))
def main(argv):
"""Main function to process mapping for each read using functions in prepare_mapping_funcs"""
args = parser.parse_args()
print("Running prepare_mapping using flip-flop remapping")
if not args.overwrite:
if os.path.exists(args.output):
print("Cowardly refusing to overwrite {}".format(args.output))
sys.exit(1)
# Make an iterator that yields all the reads we're interested in.
fast5_reads = fast5utils.iterate_fast5_reads(
args.input_folder,
limit=args.limit,
strand_list=args.input_strand_list)
# Set up arguments (kwargs) for the worker function for each read
kwargs = helpers.get_kwargs(args,
['alphabet', 'collapse_alphabet', 'device'])
kwargs[
'per_read_params_dict'] = prepare_mapping_funcs.get_per_read_params_dict_from_tsv(
args.input_per_read_params)
kwargs['references'] = helpers.fasta_file_to_dict(args.references)
kwargs['model'] = helpers.load_model(args.model)
workerFunction = prepare_mapping_funcs.oneread_remap # remaps a single read using flip-flip network
results = imap_mp(workerFunction,
fast5_reads,
threads=args.jobs,
fix_kwargs=kwargs,
unordered=True)
# results is an iterable of dicts
# each dict is a set of return values from a single read
prepare_mapping_funcs.generate_output_from_results(results, args)
def test_strand_list_invalid(self):
"""Use strand list with no header line. Should throw an exception."""
strand_list = os.path.join(self.STRAND_LIST_DIR,
"invalid_strand_list_no_header.txt")
with self.assertRaises(Exception):
for fn, rid in iterate_fast5_reads(self.MULTIREAD_DIR,
strand_list=strand_list):
print("Filename=", fn, "read_id=", rid)
def main():
args = parser.parse_args()
trim_start, trim_end = args.trim
fast5_reads = fast5utils.iterate_fast5_reads(
args.input_folder,
limit=args.limit,
strand_list=args.input_strand_list,
recursive=args.recursive)
with open_file_or_stdout(args.output) as tsvfile:
writer = csv.writer(tsvfile, delimiter='\t', lineterminator='\n')
# UUID is 32hexdigits and four dashes eg. '43f6a05c-0856-4edc-8cd2-4866d9d60eaa'
writer.writerow(['UUID', 'trim_start', 'trim_end', 'shift', 'scale'])
results = imap_mp(one_read_shift_scale, fast5_reads, threads=args.jobs)
for result in results:
if all(result):
read_id, shift, scale = result
writer.writerow([read_id, trim_start, trim_end, shift, scale])
def test_strand_list_no_read_id_multiread(self):
strand_list = os.path.join(self.STRAND_LIST_DIR,
"strand_list_no_read_id.txt")
self._check_found_read_ids(
iterate_fast5_reads(self.MULTIREAD_DIR, strand_list=strand_list))
def test_sequencing_summary_multiread(self):
self._check_found_read_ids(
iterate_fast5_reads(self.MULTIREAD_DIR,
strand_list=self.SEQUENCING_SUMMARY))
def test_strand_list_no_filename_single_reads(self):
strand_list = os.path.join(self.STRAND_LIST_DIR,
"strand_list_no_filename.txt")
self._check_found_read_ids(
iterate_fast5_reads(self.READ_DIR, strand_list=strand_list))
def test_no_strand_list_multiread(self):
self._check_found_read_ids(iterate_fast5_reads(self.MULTIREAD_DIR))
def test_no_strand_list_single_reads(self):
self._check_found_read_ids(iterate_fast5_reads(self.READ_DIR))
def test_no_strand_list_multiread(self):
"""See if read ids found in multiread file with no strand list"""
self._check_found_read_ids(iterate_fast5_reads(self.MULTIREAD_DIR))
def main():
args = get_parser().parse_args()
# TODO convert to logging
sys.stderr.write("* Initializing reads file search.\n")
fast5_reads = fast5utils.iterate_fast5_reads(
args.input_folder, limit=args.limit,
strand_list=args.input_strand_list, recursive=args.recursive)
if args.scaling is not None:
sys.stderr.write(
"* Loading read scaling parameters from {}.\n".format(
args.scaling))
all_read_params = get_per_read_params_dict_from_tsv(args.scaling)
input_read_ids = frozenset(rec[1] for rec in fast5_reads)
scaling_read_ids = frozenset(all_read_params.keys())
sys.stderr.write("* {} / {} reads have scaling information.\n".format(
len(input_read_ids & scaling_read_ids), len(input_read_ids)))
fast5_reads = [rec for rec in fast5_reads if rec[
1] in scaling_read_ids]
else:
all_read_params = {}
sys.stderr.write("* Calling reads.\n")
nbase, ncalled, nread, nsample = 0, 0, 0, 0
t0 = time.time()
progress = Progress(quiet=args.quiet)
startcharacter = '@' if args.fastq else '>'
initargs = [args.device, args.model, args.chunk_size, args.overlap,
all_read_params, args.alphabet,
args.max_concurrent_chunks, args.fastq, args.qscore_scale,
args.qscore_offset, args.beam, args.posterior,
args.temperature]
pool = Pool(args.jobs, initializer=worker_init, initargs=initargs)
with open_file_or_stdout(args.output) as fh:
for read_id, basecall, qstring, read_nsample in \
pool.imap_unordered(worker, fast5_reads):
if basecall is not None and len(basecall) > 0:
fh.write("{}{}\n{}\n".format(
startcharacter, read_id,
basecall[::-1] if args.reverse else basecall))
nbase += len(basecall)
ncalled += 1
if args.fastq:
fh.write("+\n{}\n".format(
qstring[::-1] if args.reverse else qstring))
nread += 1
nsample += read_nsample
progress.step()
total_time = time.time() - t0
sys.stderr.write(
"* Called {} reads in {:.2f}s\n".format(nread, int(total_time)))
sys.stderr.write(
"* {:7.2f} kbase / s\n".format(nbase / total_time / 1000.0))
sys.stderr.write(
"* {:7.2f} ksample / s\n".format(nsample / total_time / 1000.0))
sys.stderr.write("* {} reads failed.\n".format(nread - ncalled))
return
def main():
args = parser.parse_args()
device = helpers.set_torch_device(args.device)
# TODO convert to logging
sys.stderr.write("* Loading model.\n")
model = load_model(args.model).to(device)
is_cat_mod = isinstance(model.sublayers[-1],
layers.GlobalNormFlipFlopCatMod)
do_output_mods = args.modified_base_output is not None
if do_output_mods and not is_cat_mod:
sys.stderr.write(
"Cannot output modified bases from canonical base only model.")
sys.exit()
n_can_states = nstate_flipflop(model.sublayers[-1].nbase)
stride = guess_model_stride(model)
chunk_size = args.chunk_size * stride
chunk_overlap = args.overlap * stride
sys.stderr.write("* Initializing reads file search.\n")
fast5_reads = list(
fast5utils.iterate_fast5_reads(args.input_folder,
limit=args.limit,
strand_list=args.input_strand_list,
recursive=args.recursive))
sys.stderr.write("* Found {} reads.\n".format(len(fast5_reads)))
if args.scaling is not None:
sys.stderr.write("* Loading read scaling parameters from {}.\n".format(
args.scaling))
all_read_params = get_per_read_params_dict_from_tsv(args.scaling)
input_read_ids = frozenset(rec[1] for rec in fast5_reads)
scaling_read_ids = frozenset(all_read_params.keys())
sys.stderr.write("* {} / {} reads have scaling information.\n".format(
len(input_read_ids & scaling_read_ids), len(input_read_ids)))
fast5_reads = [
rec for rec in fast5_reads if rec[1] in scaling_read_ids
]
else:
all_read_params = {}
mods_fp = None
if do_output_mods:
mods_fp = h5py.File(args.modified_base_output)
mods_fp.create_group('Reads')
mod_long_names = model.sublayers[-1].ordered_mod_long_names
sys.stderr.write("* Preparing modified base output: {}.\n".format(
', '.join(map(str, mod_long_names))))
mods_fp.create_dataset('mod_long_names',
data=np.array(mod_long_names, dtype='S'),
dtype=h5py.special_dtype(vlen=str))
sys.stderr.write("* Calling reads.\n")
nbase, ncalled, nread, nsample = 0, 0, 0, 0
t0 = time.time()
progress = Progress(quiet=args.quiet)
startcharacter = '@' if args.fastq else '>'
try:
with open_file_or_stdout(args.output) as fh:
for read_filename, read_id in fast5_reads:
read_params = all_read_params[
read_id] if read_id in all_read_params else None
basecall, qstring, read_nsample = process_read(
read_filename, read_id, model, chunk_size, chunk_overlap,
read_params, n_can_states, stride, args.alphabet,
is_cat_mod, mods_fp, args.max_concurrent_chunks,
args.fastq, args.qscore_scale, args.qscore_offset)
if basecall is not None:
fh.write("{}{}\n{}\n".format(
startcharacter, read_id,
basecall[::-1] if args.reverse else basecall))
nbase += len(basecall)
ncalled += 1
if args.fastq:
fh.write("+\n{}\n".format(
qstring[::-1] if args.reverse else qstring))
nread += 1
nsample += read_nsample
progress.step()
finally:
if mods_fp is not None:
mods_fp.close()
total_time = time.time() - t0
sys.stderr.write("* Called {} reads in {:.2f}s\n".format(
nread, int(total_time)))
sys.stderr.write("* {:7.2f} kbase / s\n".format(nbase / total_time /
1000.0))
sys.stderr.write("* {:7.2f} ksample / s\n".format(nsample / total_time /
1000.0))
sys.stderr.write("* {} reads failed.\n".format(nread - ncalled))
return
def test_no_strand_list_single_reads(self):
"""See if read ids found in single-read file with no strand list"""
self._check_found_read_ids(iterate_fast5_reads(self.READ_DIR))
metavar=('beginning', 'end'),
help='Number of samples to trim off start and end')
parser.add_argument('model', action=FileExists, help='Model file')
parser.add_argument('references', action=FileExists, help='Fasta file')
parser.add_argument('read_dir',
action=FileExists,
help='Directory for fast5 reads')
if __name__ == '__main__':
args = parser.parse_args()
worker_kwarg_names = ['back_prob', 'localpen', 'minscore', 'trim']
model = helpers.load_model(args.model)
fast5_reads = fast5utils.iterate_fast5_reads(
args.read_dir, limit=args.limit, strand_list=args.input_strand_list)
for res in imap_mp(squiggle_match.worker,
fast5_reads,
threads=args.jobs,
fix_kwargs=helpers.get_kwargs(args, worker_kwarg_names),
unordered=True,
init=squiggle_match.init_worker,
initargs=[model, args.references]):
if res is None:
continue
read_id, sig, score, path, squiggle, bases = res
bases = bases.decode('ascii')
print('#{} {}'.format(read_id, score))
for i, (s, p) in enumerate(zip(sig, path)):
print('{}\t{}\t{}\t{}\t{}\t{}\t{}'.format(read_id, i, s, p,
def test_sequencing_summary_multiread(self):
"""See if read ids found using sequencing-summary style strand list"""
self._check_found_read_ids(
iterate_fast5_reads(self.MULTIREAD_DIR,
strand_list=self.SEQUENCING_SUMMARY))
def test_strand_list_multiread(self):
"""See if read ids found using strand list with multi read fast5s"""
strand_list = os.path.join(self.STRAND_LIST_DIR, "strand_list.txt")
self._check_found_read_ids(
iterate_fast5_reads(self.MULTIREAD_DIR, strand_list=strand_list))
def main():
"""Main function to process mapping for each read using functions in prepare_mapping_funcs"""
args = parser.parse_args()
print("Running prepare_mapping using flip-flop remapping")
if not args.overwrite:
if os.path.exists(args.output):
print("Cowardly refusing to overwrite {}".format(args.output))
sys.exit(1)
# Create alphabet and check for consistency
modified_bases = [elt[0] for elt in args.mod]
canonical_bases = [elt[1] for elt in args.mod]
for b in modified_bases:
assert len(
b
) == 1, "Modified bases must be a single character, got {}".format(b)
assert b not in args.alphabet, "Modified base must not be a canonical base, got {}".format(
b)
for b in canonical_bases:
assert len(
b
) == 1, "Canonical coding for modified bases must be a single character, got {}".format(
b)
assert b in args.alphabet, "Canonical coding for modified base must be a canonical base, got {}".format(
b)
full_alphabet = args.alphabet + ''.join(modified_bases)
flat_alphabet = args.alphabet + ''.join(canonical_bases)
modification_names = [elt[2] for elt in args.mod]
alphabet_info = alphabet.AlphabetInfo(full_alphabet,
flat_alphabet,
modification_names,
do_reorder=True)
print("Converting references to labels using {}".format(
str(alphabet_info)))
# Make an iterator that yields all the reads we're interested in.
fast5_reads = fast5utils.iterate_fast5_reads(
args.input_folder,
limit=args.limit,
strand_list=args.input_strand_list,
recursive=args.recursive)
# Set up arguments (kwargs) for the worker function for each read
kwargs = {}
kwargs[
'per_read_params_dict'] = prepare_mapping_funcs.get_per_read_params_dict_from_tsv(
args.input_per_read_params)
kwargs['model'] = helpers.load_model(args.model)
kwargs['alphabet_info'] = alphabet_info
kwargs['max_read_length'] = args.max_read_length
kwargs['localpen'] = args.localpen
# remaps a single read using flip-flip network
workerFunction = prepare_mapping_funcs.oneread_remap
def iter_jobs():
references = bio.fasta_file_to_dict(args.references,
alphabet=full_alphabet)
for fn, read_id in fast5_reads:
yield fn, read_id, references.get(read_id, None)
if args.limit is not None:
chunksize = args.limit // (2 * args.jobs)
chunksize = int(np.clip(chunksize, 1, 50))
else:
chunksize = 50
results = imap_mp(workerFunction,
iter_jobs(),
threads=args.jobs,
fix_kwargs=kwargs,
unordered=True,
chunksize=chunksize)
# results is an iterable of dicts
# each dict is a set of return values from a single read
prepare_mapping_funcs.generate_output_from_results(results, args.output,
alphabet_info)
def main():
args = parser.parse_args()
assert args.device != 'cpu', "Flipflop basecalling in taiyaki requires a GPU and for cupy to be installed"
device = torch.device(args.device)
# TODO convert to logging
sys.stderr.write("* Loading model.\n")
model = load_model(args.model).to(device)
is_cat_mod = isinstance(model.sublayers[-1], layers.GlobalNormFlipFlopCatMod)
do_output_mods = args.modified_base_output is not None
if do_output_mods and not is_cat_mod:
sys.stderr.write(
"Cannot output modified bases from canonical base only model.")
sys.exit()
n_can_states = nstate_flipflop(model.sublayers[-1].nbase)
stride = guess_model_stride(model, device=device)
chunk_size, chunk_overlap = basecall_helpers.round_chunk_values(
args.chunk_size, args.overlap, stride)
sys.stderr.write("* Initializing reads file search.\n")
fast5_reads = fast5utils.iterate_fast5_reads(
args.input_folder, limit=args.limit, strand_list=args.input_strand_list,
recursive=args.recursive)
mods_fp = None
if do_output_mods:
mods_fp = h5py.File(args.modified_base_output)
mods_fp.create_group('Reads')
mod_long_names = model.sublayers[-1].ordered_mod_long_names
sys.stderr.write("* Preparing modified base output: {}.\n".format(
', '.join(map(str, mod_long_names))))
mods_fp.create_dataset(
'mod_long_names', data=np.array(mod_long_names, dtype='S'),
dtype=h5py.special_dtype(vlen=str))
sys.stderr.write("* Calling reads.\n")
nbase, ncalled, nread, nsample = 0, 0, 0, 0
t0 = time.time()
progress = Progress(quiet=args.quiet)
try:
with open_file_or_stdout(args.output) as fh:
for read_filename, read_id in fast5_reads:
basecall, read_nsample = process_read(
read_filename, read_id, model, chunk_size,
chunk_overlap, device, n_can_states, stride, args.alphabet,
is_cat_mod, mods_fp)
if basecall is not None:
fh.write(">{}\n{}\n".format(read_id, basecall))
nbase += len(basecall)
ncalled += 1
nread += 1
nsample += read_nsample
progress.step()
finally:
if mods_fp is not None:
mods_fp.close()
total_time = time.time() - t0
sys.stderr.write("* Called {} reads in {}s\n".format(nread, int(total_time)))
sys.stderr.write("* {:7.2f} kbase / s\n".format(nbase / total_time / 1000.0))
sys.stderr.write("* {:7.2f} ksample / s\n".format(nsample / total_time / 1000.0))
sys.stderr.write("* {} reads failed.\n".format(nread - ncalled))
return