def main(args):
print_abfinder_start()
db = mongodb.get_db(args.db, args.ip, args.port,
args.user, args.password)
make_directories(args)
standards = get_standards(args)
print_standards_info(standards)
collections = mongodb.get_collections(db, args.collection, prefix=args.collection_prefix)
print_collections_info(collections)
for collection in collections:
indexed = False
print_single_collection(collection)
if args.remove_padding:
print_remove_padding()
mongodb.remove_padding(db, collection)
seq_files = get_sequences(db, collection, args.temp_dir, args)
for standard in standards:
print_single_standard(standard)
scores = run_jobs(seq_files, standard, args)
if args.output_dir:
make_figure(standard.id, scores, collection, args)
if args.update:
if not indexed:
mongodb.index(db, collection, 'seq_id')
indexed = True
update_db(db, standard.id, scores, collection, args)
clean_up(seq_files)
def main(args):
db = mongodb.get_db(args.db, args.ip, args.port,
args.user, args.password)
print_method(args.method)
pairs = get_collection_pairs(db, args)
index_collections(db, pairs)
prev1 = None
scores = {}
cscores = {}
for pair in pairs:
s1, s2 = pair
curr1 = s1
if prev1 != curr1:
print_collection_info(s1)
s1_all_vgenes = get_vgenes(db, s1, args.chain)
print_pair_info(s1, s2)
s1_vgenes, s2_vgenes = get_vgenes(db, s2, args.chain, prev_data=s1_all_vgenes)
logger.info('')
logger.info('Calculating similarities...')
median, counts, bins, similarities = calculate_similarities(s1_vgenes,
s2_vgenes,
args)
write_output(s1, s2, median, counts, bins, similarities, args)
scores = update_scores(s1, s2, median, scores)
if args.control_similarity:
logger.info('')
logger.info('Calculating control similarities...')
cmedian, ccounts, cbins, csimilarities = calculate_control_similarities(s1_vgenes,
s2_vgenes,
args)
write_output(s1, s2, cmedian, ccounts, cbins, csimilarities, args)
cscores = update_scores(s1, s2, cmedian, cscores)
prev1 = s1
print_final_results(scores)
print_final_results(cscores, control=True)
def main(args):
_print_start_info(args)
if args.sleep:
countdown(args)
for d in [args.output, args.temp_dir]:
make_dir(d)
if args.consensus and args.germs:
germs = parse_germs(args.germs)
else:
germs = args.germs
# check whether JSON files have been passed
if args.json is not None and all([args.db is None, args.collection is None]):
if os.path.isfile(args.json) and args.json.endswith('.json'):
collections = [args.json, ]
else:
collections = list_files(args.json, extension='json')
db = None
sample_names = [os.path.basename(c).replace('.json', '') for c in collections]
# check whether MINIMAL files have been passed:
if args.minimal_input is not None and all([args.db is None, args.collection is None]):
if os.path.isfile(args.minimal_input) and args.minimal_input.endswith('.txt'):
collections = [args.minimal_input, ]
else:
collections = list_files(args.minimal_input, extension='txt')
db = None
sample_names = [os.path.basename(c).replace('.txt', '') for c in collections]
# otherwise, get sequences from MongoDB
else:
db = mongodb.get_db(args.db, args.ip, args.port, args.user, args.password)
collections = mongodb.get_collections(db, collection=args.collection)
sample_names = collections
for collection, sample_name in zip(collections, sample_names):
collection_start = time.time()
print_collection_info(collection, sample_name)
if args.non_redundant:
seqs = get_seqs(db, collection, args, make_seq_db=False)
unique_file = unix_sort_unique(seqs, args)
write_nr_output(collection, unique_file, collection_start, args)
else:
seq_db_path = get_seqs(db, collection, args)
initial_clusters = initial_clustering(seq_db_path, args)
if args.min_seqs == 1:
singletons = [ic for ic in initial_clusters if ic.size == 1]
initial_clusters = [ic for ic in initial_clusters if ic.size > 1]
logger.info('{} clusters contained only a single sequence. Processing singletons...'.format(len(singletons)))
singleton_consentroids = process_singleton_clusters(singletons, seq_db_path, args)
logger.info('')
else:
singleton_consentroids = []
consentroids = process_initial_clusters(initial_clusters, seq_db_path, args)
consentroids += singleton_consentroids
sequences, sizes = zip(*consentroids)
write_output(sample_name, sequences, sizes, collection_start, args)
for ic in initial_clusters:
ic.cleanup()
remove_sqlite_db(args)
def main(args):
db = mongodb.get_db(args.db, ip=args.ip, port=args.port, user=args.user, password=args.password)
for collection in mongodb.get_collections(db):
print_collection_info(collection)
seqs = query(db, collection, args.chain)
if len(seqs) == 0:
continue
germline_plot(seqs, 'V', collection, args.output, args.var_plot, args.species, args.chain)
if args.chain == 'heavy':
germline_plot(seqs, 'D', collection, args.output, args.div_plot, args.species, args.chain)
germline_plot(seqs, 'J', collection, args.output, args.join_plot, args.species, args.chain)
cdr3_plot(seqs, collection, args.cdr3_plot, args.chain, args.output)
vj_heatmap(seqs, collection, args.heatmap, args.species, args.chain, args.output)
def update(db, collection, data, standard, version, args):
db = mongodb.get_db(args.db, args.ip, args.port, args.user, args.password)
coll = db[collection]
score = data[0]
ids = data[1]
mab_id_field = 'mab_identity_aa' if args.is_aa else 'mab_identity_nt'
if int(version.split('.')[0]) < 3:
result = coll.update({'seq_id': {'$in': ids}},
{'$set': {'{}.{}'.format(mab_id_field, standard.lower()): float(score)}},
multi=True)
else:
result = coll.update_many({'seq_id': {'$in': ids}},
{'$set': {'{}.{}'.format(mab_id_field, standard.lower()): float(score)}})
if args.debug:
print('matched: {}'.format(result.matched_count))
print('modified: {}'.format(result.modified_count))
def update_db(db, standard, scores, collection, args):
db = mongodb.get_db(args.db, args.ip, args.port, args.user, args.password)
print_index_info()
mongodb.index(db, collection, ['seq_id'])
print_update_info()
start = time.time()
conn = mongodb.get_connection(args.ip, args.port,
args.user, args.password)
mongo_version = conn.server_info()['version']
standard = standard.replace('.', '_')
g = scores.groupby('identity')
groups = regroup(g.groups)
for g in range(0, len(groups), args.update_threads):
tlist = []
for group in groups[g:g + args.update_threads]:
t = Thread(target=update, args=(db, collection, group, standard, mongo_version, args))
t.start()
tlist.append(t)
for t in tlist:
t.join()
progbar.progress_bar(g + args.update_threads, len(groups))
# if platform.system().lower() == 'darwin' or args.debug or args.single_process_update:
# for i, group in enumerate(groups):
# update(db, collection, group, standard, mongo_version, args)
# progbar.progress_bar(i, len(groups))
# else:
# p = mp.Pool(processes=25)
# async_results = []
# for group in groups:
# async_results.append(p.apply_async(update, args=(db, collection, group, standard, mongo_version, args)))
# monitor_update(async_results)
# p.close()
# p.join()
print('')
run_time = time.time() - start
logger.info('Updating took {} seconds. ({} sequences per second)'.format(round(run_time, 2),
round(len(scores) / run_time, 1)))