def main():
args = parse_args()
if args.debug:
init_console_logger(3)
logging.info("MOB-recon v. {} ".format(__version__))
if not args.outdir:
logging.error(
'Error, no output directory specified, please specify one')
sys.exit(-1)
if not args.infile:
logging.error('Error, no fasta specified, please specify one')
sys.exit(-1)
if not os.path.isfile(args.infile):
logging.error('Error, input fasta file does not exist: "{}"'.format(
args.infile))
sys.exit(-1)
logging.info('Processing fasta file {}'.format(args.infile))
logging.info('Analysis directory {}'.format(args.outdir))
if not os.path.isdir(args.outdir):
os.mkdir(args.outdir, 0o755)
# Check that the needed databases have been initialized
database_dir = os.path.abspath(args.database_directory)
verify_init(logging, database_dir)
status_file = os.path.join(database_dir, 'status.txt')
if not os.path.isfile(status_file):
logging.info(
'Warning! Needed databases have not been initialize please run mob_init and try again'
)
mob_suite.mob_init.main()
plasmid_files = dict()
input_fasta = args.infile
out_dir = args.outdir
num_threads = args.num_threads
tmp_dir = os.path.join(out_dir, '__tmp')
file_id = os.path.basename(input_fasta)
fixed_fasta = os.path.join(tmp_dir, 'fixed.input.fasta')
chromosome_file = os.path.join(out_dir, 'chromosome.fasta')
replicon_blast_results = os.path.join(tmp_dir,
'replicon_blast_results.txt')
mob_blast_results = os.path.join(tmp_dir, 'mobrecon_blast_results.txt')
repetitive_blast_results = os.path.join(tmp_dir,
'repetitive_blast_results.txt')
contig_blast_results = os.path.join(tmp_dir, 'contig_blast_results.txt')
# Input numeric params
min_rep_ident = float(args.min_rep_ident)
min_mob_ident = float(args.min_mob_ident)
min_con_ident = float(args.min_con_ident)
min_rpp_ident = float(args.min_rpp_ident)
idents = {
'min_rep_ident': min_rep_ident,
'min_mob_ident': min_mob_ident,
'min_con_ident': min_con_ident,
'min_rpp_ident': min_rpp_ident
}
for param in idents:
value = float(idents[param])
if value < 60:
logging.error(
"Error: {} is too low, please specify an integer between 70 - 100"
.format(param))
sys.exit(-1)
if value > 100:
logging.error(
"Error: {} is too high, please specify an integer between 70 - 100"
.format(param))
sys.exit(-1)
min_rep_cov = float(args.min_rep_cov)
min_mob_cov = float(args.min_mob_cov)
min_con_cov = float(args.min_con_cov)
min_rpp_cov = float(args.min_rpp_cov)
covs = {
'min_rep_cov': min_rep_cov,
'min_mob_cov': min_mob_cov,
'min_con_cov': min_con_cov,
'min_rpp_cov': min_rpp_cov
}
for param in covs:
value = float(covs[param])
if value < 60:
logging.error(
"Error: {} is too low, please specify an integer between 50 - 100"
.format(param))
sys.exit(-1)
if value > 100:
logging.error(
"Error: {} is too high, please specify an integer between 50 - 100"
.format(param))
sys.exit(-1)
min_rep_evalue = float(args.min_rep_evalue)
min_mob_evalue = float(args.min_mob_evalue)
min_con_evalue = float(args.min_con_evalue)
min_rpp_evalue = float(args.min_rpp_evalue)
evalues = {
'min_rep_evalue': min_rep_evalue,
'min_mob_evalue': min_mob_evalue,
'min_con_evalue': min_con_evalue,
'min_rpp_evalue': min_rpp_evalue
}
for param in evalues:
value = float(evalues[param])
if value > 1:
logging.error(
"Error: {} is too high, please specify an float evalue between 0 to 1"
.format(param))
sys.exit(-1)
min_overlapp = int(args.min_overlap)
min_length = int(args.min_length)
# Input numeric params
min_rep_ident = float(args.min_rep_ident)
min_mob_ident = float(args.min_mob_ident)
min_con_ident = float(args.min_con_ident)
min_rpp_ident = float(args.min_rpp_ident)
idents = {
'min_rep_ident': min_rep_ident,
'min_mob_ident': min_mob_ident,
'min_con_ident': min_con_ident,
'min_rpp_ident': min_rpp_ident
}
for param in idents:
value = idents[param]
if value < 60:
logging.error(
"Error: {} is too low, please specify an integer between 70 - 100"
.format(param))
sys.exit(-1)
if value > 100:
logging.error(
"Error: {} is too high, please specify an integer between 70 - 100"
.format(param))
sys.exit(-1)
min_rep_cov = float(args.min_rep_cov)
min_mob_cov = float(args.min_mob_cov)
min_con_cov = float(args.min_con_cov)
min_rpp_cov = float(args.min_rpp_cov)
covs = {
'min_rep_cov': min_rep_cov,
'min_mob_cov': min_mob_cov,
'min_con_cov': min_con_cov,
'min_rpp_cov': min_rpp_cov
}
for param in covs:
value = covs[param]
if value < 60:
logging.error(
"Error: {} is too low, please specify an integer between 50 - 100"
.format(param))
sys.exit(-1)
if value > 100:
logging.error(
"Error: {} is too high, please specify an integer between 50 - 100"
.format(param))
sys.exit(-1)
min_rep_evalue = float(args.min_rep_evalue)
min_mob_evalue = float(args.min_mob_evalue)
min_con_evalue = float(args.min_con_evalue)
min_rpp_evalue = float(args.min_rpp_evalue)
evalues = {
'min_rep_evalue': min_rep_evalue,
'min_mob_evalue': min_mob_evalue,
'min_con_evalue': min_con_evalue,
'min_rpp_evalue': min_rpp_evalue
}
for param in evalues:
value = evalues[param]
if value > 1:
logging.error(
"Error: {} is too high, please specify an float evalue between 0 to 1"
.format(param))
sys.exit(-1)
min_overlapp = args.min_overlap
min_length = args.min_length
# Input Databases
default_database_dir = os.path.join(
os.path.dirname(os.path.realpath(__file__)), 'databases')
if database_dir == default_database_dir:
plasmid_ref_db = args.plasmid_db
replicon_ref = args.plasmid_replicons
mob_ref = args.plasmid_mob
mash_db = args.plasmid_mash_db
repetitive_mask_file = args.repetitive_mask
else:
plasmid_ref_db = os.path.join(database_dir,
'ncbi_plasmid_full_seqs.fas')
replicon_ref = os.path.join(database_dir, 'rep.dna.fas')
mob_ref = os.path.join(database_dir, 'mob.proteins.faa')
mash_db = os.path.join(database_dir, 'ncbi_plasmid_full_seqs.fas.msh')
repetitive_mask_file = os.path.join(database_dir, 'repetitive.dna.fas')
check_dependencies(logging)
needed_dbs = [
plasmid_ref_db, replicon_ref, mob_ref, mash_db, repetitive_mask_file,
"{}.nin".format(repetitive_mask_file)
]
for db in needed_dbs:
if (not os.path.isfile(db)):
logging.error('Error needed database missing "{}"'.format(db))
sys.exit(-1)
contig_report_file = os.path.join(out_dir, 'contig_report.txt')
minimus_prefix = os.path.join(tmp_dir, 'minimus')
filtered_blast = os.path.join(tmp_dir, 'filtered_blast.txt')
repetitive_blast_report = os.path.join(out_dir,
'repetitive_blast_report.txt')
mobtyper_results_file = os.path.join(out_dir,
'mobtyper_aggregate_report.txt')
keep_tmp = args.keep_tmp
run_circlator = args.run_circlator
unicycler_contigs = args.unicycler_contigs
if not isinstance(args.num_threads, int):
logging.info(
'Error number of threads must be an integer, you specified "{}"'.
format(args.num_threads))
logging.info('Creating tmp working directory {}'.format(tmp_dir))
if not os.path.isdir(tmp_dir):
os.mkdir(tmp_dir, 0o755)
logging.info(
'Writing cleaned header input fasta file from {} to {}'.format(
input_fasta, fixed_fasta))
fix_fasta_header(input_fasta, fixed_fasta)
contig_seqs = read_fasta_dict(fixed_fasta)
logging.info('Running replicon blast on {}'.format(replicon_ref))
replicon_contigs = getRepliconContigs(
replicon_blast(replicon_ref,
fixed_fasta,
min_rep_ident,
min_rep_cov,
min_rep_evalue,
tmp_dir,
replicon_blast_results,
num_threads=num_threads))
logging.info('Running relaxase blast on {}'.format(mob_ref))
mob_contigs = getRepliconContigs(
mob_blast(mob_ref,
fixed_fasta,
min_mob_ident,
min_mob_cov,
min_mob_evalue,
tmp_dir,
mob_blast_results,
num_threads=num_threads))
logging.info('Running contig blast on {}'.format(plasmid_ref_db))
contig_blast(fixed_fasta, plasmid_ref_db, min_con_ident, min_con_cov,
min_con_evalue, min_length, tmp_dir, contig_blast_results)
pcl_clusters = contig_blast_group(filtered_blast, min_overlapp)
logging.info(
'Running repetitive contig masking blast on {}'.format(mob_ref))
repetitive_contigs = repetitive_blast(fixed_fasta,
repetitive_mask_file,
min_rpp_ident,
min_rpp_cov,
min_rpp_evalue,
min_length,
tmp_dir,
repetitive_blast_results,
num_threads=num_threads)
circular_contigs = dict()
logging.info('Running circlator minimus2 on {}'.format(fixed_fasta))
if run_circlator:
circular_contigs = circularize(fixed_fasta, minimus_prefix)
if unicycler_contigs:
for seqid in contig_seqs:
if 'circular=true' in seqid:
circular_contigs[seqid] = ''
repetitive_dna = dict()
results_fh = open(repetitive_blast_report, 'w')
results_fh.write(
"contig_id\tmatch_id\tmatch_type\tscore\tcontig_match_start\tcontig_match_end\n"
)
for contig_id in repetitive_contigs:
match_info = repetitive_contigs[contig_id]['id'].split('|')
repetitive_dna[contig_id] = "{}\t{}\t{}\t{}\t{}".format(
match_info[1], match_info[len(match_info) - 1],
repetitive_contigs[contig_id]['score'],
repetitive_contigs[contig_id]['contig_start'],
repetitive_contigs[contig_id]['contig_end'])
results_fh.write("{}\t{}\t{}\t{}\t{}\t{}\n".format(
contig_id, match_info[1], match_info[len(match_info) - 1],
repetitive_contigs[contig_id]['score'],
repetitive_contigs[contig_id]['contig_start'],
repetitive_contigs[contig_id]['contig_end']))
results_fh.close()
seq_clusters = dict()
cluster_bitscores = dict()
for seqid in pcl_clusters:
cluster_id = list(pcl_clusters[seqid].keys())[0]
bitscore = pcl_clusters[seqid][cluster_id]
cluster_bitscores[cluster_id] = bitscore
sorted_cluster_bitscores = sorted(list(cluster_bitscores.items()),
key=operator.itemgetter(1))
sorted_cluster_bitscores.reverse()
contigs_assigned = dict()
for cluster_id, bitscore in sorted_cluster_bitscores:
if not cluster_id in seq_clusters:
seq_clusters[cluster_id] = dict()
for seqid in pcl_clusters:
if not cluster_id in pcl_clusters[seqid]:
continue
if seqid in contig_seqs and seqid not in contigs_assigned:
seq_clusters[cluster_id][seqid] = contig_seqs[seqid]
contigs_assigned[seqid] = cluster_id
# Add sequences with known replicons regardless of whether they belong to a mcl cluster
clust_id = 0
refined_clusters = dict()
for contig_id in mob_contigs:
if not contig_id in pcl_clusters:
if contig_id in contig_seqs:
if not clust_id in seq_clusters:
seq_clusters["Novel_" + str(clust_id)] = dict()
if not contig_id in pcl_clusters:
pcl_clusters[contig_id] = dict()
pcl_clusters[contig_id]["Novel_" + str(clust_id)] = 0
seq_clusters["Novel_" +
str(clust_id)][contig_id] = contig_seqs[contig_id]
clust_id += 1
# Add sequences with known relaxases regardless of whether they belong to a mcl cluster
count_replicons = dict()
for contig_id in replicon_contigs:
if not contig_id in pcl_clusters:
if contig_id in contig_seqs:
if not clust_id in seq_clusters:
seq_clusters["Novel_" + str(clust_id)] = dict()
if not contig_id in pcl_clusters:
pcl_clusters[contig_id] = dict()
pcl_clusters[contig_id]["Novel_" + str(clust_id)] = dict()
seq_clusters["Novel_" +
str(clust_id)][contig_id] = contig_seqs[contig_id]
clust_id += 1
refined_clusters = dict()
# split out circular sequences from each other
replicon_clusters = dict()
for contig_id in replicon_contigs:
for hit_id in replicon_contigs[contig_id]:
id, rep_type = hit_id.split('|')
cluster = list(pcl_clusters[contig_id].keys())[0]
if not cluster in replicon_clusters:
replicon_clusters[cluster] = 0
replicon_clusters[cluster] += 1
for id in seq_clusters:
cluster = seq_clusters[id]
if not id in refined_clusters:
refined_clusters[id] = dict()
for contig_id in cluster:
if contig_id in circular_contigs and len(cluster) > 1 and (
id in replicon_clusters and replicon_clusters[id] > 1):
if not clust_id in refined_clusters:
refined_clusters["Novel_" + str(clust_id)] = dict()
refined_clusters["Novel_" +
str(clust_id)][contig_id] = cluster[contig_id]
clust_id += 1
continue
refined_clusters[id][contig_id] = cluster[contig_id]
seq_clusters = refined_clusters
m = mash()
mash_distances = dict()
mash_top_dists = dict()
contig_report = list()
results_fh = open(contig_report_file, 'w')
results_fh.write("file_id\tcluster_id\tcontig_id\tcontig_length\tcircularity_status\trep_type\t" \
"rep_type_accession\trelaxase_type\trelaxase_type_accession\tmash_nearest_neighbor\t"
" mash_neighbor_distance\trepetitive_dna_id\tmatch_type\tscore\tcontig_match_start\tcontig_match_end\n")
filter_list = dict()
counter = 0
for cluster in seq_clusters:
clusters = seq_clusters[cluster]
total_cluster_length = 0
count_seqs = len(clusters)
count_rep = 0
count_small = 0
temp = dict()
for contig_id in clusters:
temp[contig_id] = ''
if contig_id in repetitive_contigs:
count_rep += 1
length = len(clusters[contig_id])
total_cluster_length += length
if length < 3000:
count_small += 1
if count_rep == count_seqs or (
float(count_rep) / count_seqs * 100 > 50
and count_small == count_seqs) or total_cluster_length < 1500:
continue
for contig_id in temp:
filter_list[contig_id] = ''
cluster_file = os.path.join(tmp_dir,
'clust_' + str(cluster) + '.fasta')
mash_file = os.path.join(tmp_dir, 'clust_' + str(cluster) + '.txt')
write_fasta_dict(clusters, cluster_file)
mashfile_handle = open(mash_file, 'w')
m.run_mash(mash_db, cluster_file, mashfile_handle)
mash_results = m.read_mash(mash_file)
mash_top_hit = getMashBestHit(mash_results)
# delete low scoring clusters
if float(mash_top_hit['mash_hit_score']) > 0.05:
skip = True
for contig_id in clusters:
if contig_id in replicon_contigs:
skip = False
break
if contig_id in circular_contigs:
skip = False
break
if contig_id in mob_contigs:
skip = False
break
if skip:
for contig_id in clusters:
del (filter_list[contig_id])
continue
new_clust_file = None
if os.path.isfile(cluster_file):
if float(mash_top_hit['mash_hit_score']) < 0.05:
cluster = mash_top_hit['clustid']
new_clust_file = os.path.join(out_dir,
'plasmid_' + cluster + ".fasta")
else:
cluster = 'novel_' + str(counter)
new_clust_file = os.path.join(out_dir,
'plasmid_' + cluster + ".fasta")
counter += 1
if os.path.isfile(new_clust_file):
temp_fh = open(cluster_file, 'r')
data = temp_fh.read()
temp_fh.close()
temp_fh = open(new_clust_file, 'a')
temp_fh.write(data)
temp_fh.close()
mash_file = os.path.join(tmp_dir,
'clust_' + str(cluster) + '.txt')
mashfile_handle = open(mash_file, 'w')
m.run_mash(mash_db, cluster_file, mashfile_handle)
mash_results = m.read_mash(mash_file)
mash_top_hit = getMashBestHit(mash_results)
else:
os.rename(cluster_file, new_clust_file)
if new_clust_file is not None:
plasmid_files[new_clust_file] = ''
for contig_id in clusters:
found_replicon_string = ''
found_replicon_id_string = ''
found_mob_string = ''
found_mob_id_string = ''
contig_status = 'Incomplete'
if contig_id in circular_contigs:
contig_status = 'Circular'
if contig_id in replicon_contigs:
rep_ids = dict()
rep_hit_ids = dict()
for hit_id in replicon_contigs[contig_id]:
id, rep_type = hit_id.split('|')
rep_ids[rep_type] = ''
rep_hit_ids[id] = ''
found_replicon_string = ','.join(list(rep_ids.keys()))
found_replicon_id_string = ','.join(list(rep_hit_ids.keys()))
if contig_id in mob_contigs:
mob_ids = dict()
mob_hit_ids = dict()
for hit_id in mob_contigs[contig_id]:
id, mob_type = hit_id.split('|')
mob_ids[mob_type] = ''
mob_hit_ids[id] = ''
found_mob_string = ','.join(list(mob_ids.keys()))
found_mob_id_string = ','.join(list(mob_hit_ids.keys()))
rep_dna_info = "\t\t\t\t"
if contig_id in repetitive_dna:
rep_dna_info = repetitive_dna[contig_id]
results_fh.write(
"{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format(
file_id, cluster, contig_id, len(clusters[contig_id]),
contig_status, found_replicon_string,
found_replicon_id_string, found_mob_string,
found_mob_id_string, mash_top_hit['top_hit'],
mash_top_hit['mash_hit_score'], rep_dna_info))
chr_contigs = dict()
for contig_id in contig_seqs:
if contig_id not in filter_list:
chr_contigs[contig_id] = contig_seqs[contig_id]
rep_dna_info = "\t\t\t\t"
if contig_id in repetitive_dna:
rep_dna_info = repetitive_dna[contig_id]
contig_status = 'Incomplete'
if contig_id in circular_contigs:
contig_status = 'Circular'
results_fh.write(
"{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format(
file_id, 'chromosome', contig_id,
len(contig_seqs[contig_id]), contig_status, '', '', '', '',
'', '', rep_dna_info))
results_fh.close()
write_fasta_dict(chr_contigs, chromosome_file)
if args.run_typer:
mobtyper_results = "file_id\tnum_contigs\ttotal_length\tgc\t" \
"rep_type(s)\trep_type_accession(s)\t" \
"relaxase_type(s)\trelaxase_type_accession(s)\t" \
"mpf_type\tmpf_type_accession(s)\t" \
"orit_type(s)\torit_accession(s)\tPredictedMobility\t" \
"mash_nearest_neighbor\tmash_neighbor_distance\tmash_neighbor_cluster\n"
for file in plasmid_files:
mobtyper_results = mobtyper_results + "{}".format(
run_mob_typer(
file, out_dir, str(num_threads),
database_dir=database_dir))
fh = open(mobtyper_results_file, 'w')
fh.write(mobtyper_results)
fh.close()
if not keep_tmp:
shutil.rmtree(tmp_dir)
def main():
args = parse_args()
if args.debug:
init_console_logger(3)
logging.info('Running plasmid detector v. {}'.format(__version__))
if not args.outdir:
logging.info(
'Error, no output directory specified, please specify one')
sys.exit()
if not args.infile:
logging.info('Error, no fasta specified, please specify one')
sys.exit()
logging.info('Processing fasta file {}'.format(args.infile))
logging.info('Analysis directory {}'.format(args.outdir))
if not os.path.isdir(args.outdir):
os.mkdir(args.outdir, 0o755)
verify_init(logging)
plasmid_files = dict()
input_fasta = args.infile
out_dir = args.outdir
num_threads = args.num_threads
tmp_dir = os.path.join(out_dir, '__tmp')
file_id = os.path.basename(input_fasta)
fixed_fasta = os.path.join(tmp_dir, 'fixed.input.fasta')
chromosome_file = os.path.join(out_dir, 'chromosome.fasta')
replicon_blast_results = os.path.join(tmp_dir,
'replicon_blast_results.txt')
mob_blast_results = os.path.join(tmp_dir, 'mobrecon_blast_results.txt')
repetitive_blast_results = os.path.join(tmp_dir,
'repetitive_blast_results.txt')
contig_blast_results = os.path.join(tmp_dir, 'contig_blast_results.txt')
min_ident = args.min_ident
min_cov = args.min_cov
evalue = args.evalue
min_length = args.min_length
plasmid_ref_db = args.plasmid_db
replicon_ref = args.plasmid_replicons
mob_ref = args.plasmid_mob
mash_db = args.plasmid_mash_db
repetitive_mask_file = args.repetitive_mask
contig_report_file = os.path.join(out_dir, 'contig_report.txt')
minimus_prefix = os.path.join(tmp_dir, 'minimus')
filtered_blast = os.path.join(tmp_dir, 'filtered_blast.txt')
repetitive_blast_report = os.path.join(out_dir,
'repetitive_blast_report.txt')
mobtyper_results_file = os.path.join(out_dir,
'mobtyper_aggregate_report.txt')
keep_tmp = args.keep_tmp
if args.run_circlator == 'False' or args.run_circlator == 'false':
run_circlator = False
else:
run_circlator = True
if args.unicycler_contigs == 'False' or args.unicycler_contigs == 'false':
unicycler_contigs = False
else:
unicycler_contigs = True
if not isinstance(args.num_threads, int):
logging.info(
'Error number of threads must be an integer, you specified "{}"'.
format(args.num_threads))
logging.info('Creating tmp working directory {}'.format(tmp_dir))
if not os.path.isdir(tmp_dir):
os.mkdir(tmp_dir, 0o755)
logging.info(
'Writing cleaned header input fasta file from {} to {}'.format(
input_fasta, fixed_fasta))
fix_fasta_header(input_fasta, fixed_fasta)
contig_seqs = read_fasta_dict(fixed_fasta)
logging.info('Running replicon blast on {}'.format(replicon_ref))
replicon_contigs = getRepliconContigs(
replicon_blast(replicon_ref,
fixed_fasta,
80,
80,
evalue,
tmp_dir,
replicon_blast_results,
num_threads=num_threads))
logging.info('Running relaxase blast on {}'.format(mob_ref))
mob_contigs = getRepliconContigs(
mob_blast(mob_ref,
fixed_fasta,
80,
80,
evalue,
tmp_dir,
mob_blast_results,
num_threads=num_threads))
logging.info('Running contig blast on {}'.format(plasmid_ref_db))
contig_blast(fixed_fasta, plasmid_ref_db, args.min_ident, args.min_cov,
evalue, min_length, tmp_dir, contig_blast_results)
pcl_clusters = contig_blast_group(filtered_blast, 10)
logging.info(
'Running repetitive contig masking blast on {}'.format(mob_ref))
repetitive_contigs = repetitive_blast(fixed_fasta,
repetitive_mask_file,
min_ident,
min_cov,
evalue,
300,
tmp_dir,
repetitive_blast_results,
num_threads=num_threads)
circular_contigs = dict()
logging.info('Running circlator minimus2 on {}'.format(fixed_fasta))
if run_circlator:
circular_contigs = circularize(fixed_fasta, minimus_prefix)
if unicycler_contigs:
for seqid in contig_seqs:
if 'circular=true' in seqid:
circular_contigs[seqid] = ''
repetitive_dna = dict()
results_fh = open(repetitive_blast_report, 'w')
results_fh.write(
"contig_id\tmatch_id\tmatch_type\tscore\tcontig_match_start\tcontig_match_end\n"
)
for contig_id in repetitive_contigs:
match_info = repetitive_contigs[contig_id]['id'].split('|')
repetitive_dna[contig_id] = "{}\t{}\t{}\t{}\t{}".format(
match_info[1], match_info[len(match_info) - 1],
repetitive_contigs[contig_id]['score'],
repetitive_contigs[contig_id]['contig_start'],
repetitive_contigs[contig_id]['contig_end'])
results_fh.write("{}\t{}\t{}\t{}\t{}\t{}\n".format(
contig_id, match_info[1], match_info[len(match_info) - 1],
repetitive_contigs[contig_id]['score'],
repetitive_contigs[contig_id]['contig_start'],
repetitive_contigs[contig_id]['contig_end']))
results_fh.close()
seq_clusters = dict()
cluster_bitscores = dict()
for seqid in pcl_clusters:
cluster_id = list(pcl_clusters[seqid].keys())[0]
bitscore = pcl_clusters[seqid][cluster_id]
cluster_bitscores[cluster_id] = bitscore
sorted_cluster_bitscores = sorted(list(cluster_bitscores.items()),
key=operator.itemgetter(1))
sorted_cluster_bitscores.reverse()
contigs_assigned = dict()
for cluster_id, bitscore in sorted_cluster_bitscores:
if not cluster_id in seq_clusters:
seq_clusters[cluster_id] = dict()
for seqid in pcl_clusters:
if not cluster_id in pcl_clusters[seqid]:
continue
if seqid in contig_seqs and seqid not in contigs_assigned:
seq_clusters[cluster_id][seqid] = contig_seqs[seqid]
contigs_assigned[seqid] = cluster_id
# Add sequences with known replicons regardless of whether they belong to a mcl cluster
clust_id = 0
refined_clusters = dict()
for contig_id in mob_contigs:
if not contig_id in pcl_clusters:
if contig_id in contig_seqs:
if not clust_id in seq_clusters:
seq_clusters["Novel_" + str(clust_id)] = dict()
if not contig_id in pcl_clusters:
pcl_clusters[contig_id] = dict()
pcl_clusters[contig_id]["Novel_" + str(clust_id)] = 0
seq_clusters["Novel_" +
str(clust_id)][contig_id] = contig_seqs[contig_id]
clust_id += 1
# Add sequences with known relaxases regardless of whether they belong to a mcl cluster
count_replicons = dict()
for contig_id in replicon_contigs:
if not contig_id in pcl_clusters:
if contig_id in contig_seqs:
if not clust_id in seq_clusters:
seq_clusters["Novel_" + str(clust_id)] = dict()
if not contig_id in pcl_clusters:
pcl_clusters[contig_id] = dict()
pcl_clusters[contig_id]["Novel_" + str(clust_id)] = dict()
seq_clusters["Novel_" +
str(clust_id)][contig_id] = contig_seqs[contig_id]
clust_id += 1
refined_clusters = dict()
# split out circular sequences from each other
replicon_clusters = dict()
for contig_id in replicon_contigs:
for hit_id in replicon_contigs[contig_id]:
id, rep_type = hit_id.split('|')
cluster = list(pcl_clusters[contig_id].keys())[0]
if not cluster in replicon_clusters:
replicon_clusters[cluster] = 0
replicon_clusters[cluster] += 1
for id in seq_clusters:
cluster = seq_clusters[id]
if not id in refined_clusters:
refined_clusters[id] = dict()
for contig_id in cluster:
if contig_id in circular_contigs and len(cluster) > 1 and (
id in replicon_clusters and replicon_clusters[id] > 1):
if not clust_id in refined_clusters:
refined_clusters["Novel_" + str(clust_id)] = dict()
refined_clusters["Novel_" +
str(clust_id)][contig_id] = cluster[contig_id]
clust_id += 1
continue
refined_clusters[id][contig_id] = cluster[contig_id]
seq_clusters = refined_clusters
m = mash()
mash_distances = dict()
mash_top_dists = dict()
contig_report = list()
results_fh = open(contig_report_file, 'w')
results_fh.write("file_id\tcluster_id\tcontig_id\tcontig_length\tcircularity_status\trep_type\t" \
"rep_type_accession\trelaxase_type\trelaxase_type_accession\tmash_nearest_neighbor\t"
" mash_neighbor_distance\trepetitive_dna_id\tmatch_type\tscore\tcontig_match_start\tcontig_match_end\n")
filter_list = dict()
counter = 0
for cluster in seq_clusters:
clusters = seq_clusters[cluster]
total_cluster_length = 0
count_seqs = len(clusters)
count_rep = 0
count_small = 0
temp = dict()
for contig_id in clusters:
temp[contig_id] = ''
if contig_id in repetitive_contigs:
count_rep += 1
length = len(clusters[contig_id])
total_cluster_length += length
if length < 3000:
count_small += 1
if count_rep == count_seqs or (
float(count_rep) / count_seqs * 100 > 50
and count_small == count_seqs) or total_cluster_length < 1500:
continue
for contig_id in temp:
filter_list[contig_id] = ''
cluster_file = os.path.join(tmp_dir,
'clust_' + str(cluster) + '.fasta')
mash_file = os.path.join(tmp_dir, 'clust_' + str(cluster) + '.txt')
write_fasta_dict(clusters, cluster_file)
mashfile_handle = open(mash_file, 'w')
m.run_mash(mash_db, cluster_file, mashfile_handle)
mash_results = m.read_mash(mash_file)
mash_top_hit = getMashBestHit(mash_results)
# delete low scoring clusters
if float(mash_top_hit['mash_hit_score']) > 0.05:
skip = True
for contig_id in clusters:
if contig_id in replicon_contigs:
skip = False
break
if contig_id in circular_contigs:
skip = False
break
if contig_id in mob_contigs:
skip = False
break
if skip:
for contig_id in clusters:
del (filter_list[contig_id])
continue
new_clust_file = None
if os.path.isfile(cluster_file):
if float(mash_top_hit['mash_hit_score']) < 0.05:
cluster = mash_top_hit['clustid']
new_clust_file = os.path.join(out_dir,
'plasmid_' + cluster + ".fasta")
else:
cluster = 'novel_' + str(counter)
new_clust_file = os.path.join(out_dir,
'plasmid_' + cluster + ".fasta")
counter += 1
if os.path.isfile(new_clust_file):
temp_fh = open(cluster_file, 'r')
data = temp_fh.read()
temp_fh.close()
temp_fh = open(new_clust_file, 'a')
temp_fh.write(data)
temp_fh.close()
mash_file = os.path.join(tmp_dir,
'clust_' + str(cluster) + '.txt')
mashfile_handle = open(mash_file, 'w')
m.run_mash(mash_db, cluster_file, mashfile_handle)
mash_results = m.read_mash(mash_file)
mash_top_hit = getMashBestHit(mash_results)
else:
os.rename(cluster_file, new_clust_file)
if new_clust_file is not None:
plasmid_files[new_clust_file] = ''
for contig_id in clusters:
found_replicon_string = ''
found_replicon_id_string = ''
found_mob_string = ''
found_mob_id_string = ''
contig_status = 'Incomplete'
if contig_id in circular_contigs:
contig_status = 'Circular'
if contig_id in replicon_contigs:
rep_ids = dict()
rep_hit_ids = dict()
for hit_id in replicon_contigs[contig_id]:
id, rep_type = hit_id.split('|')
rep_ids[rep_type] = ''
rep_hit_ids[id] = ''
found_replicon_string = ','.join(list(rep_ids.keys()))
found_replicon_id_string = ','.join(list(rep_hit_ids.keys()))
if contig_id in mob_contigs:
mob_ids = dict()
mob_hit_ids = dict()
for hit_id in mob_contigs[contig_id]:
id, mob_type = hit_id.split('|')
mob_ids[mob_type] = ''
mob_hit_ids[id] = ''
found_mob_string = ','.join(list(mob_ids.keys()))
found_mob_id_string = ','.join(list(mob_hit_ids.keys()))
rep_dna_info = "\t\t\t\t"
if contig_id in repetitive_dna:
rep_dna_info = repetitive_dna[contig_id]
results_fh.write(
"{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format(
file_id, cluster, contig_id, len(clusters[contig_id]),
contig_status, found_replicon_string,
found_replicon_id_string, found_mob_string,
found_mob_id_string, mash_top_hit['top_hit'],
mash_top_hit['mash_hit_score'], rep_dna_info))
chr_contigs = dict()
for contig_id in contig_seqs:
if contig_id not in filter_list:
chr_contigs[contig_id] = contig_seqs[contig_id]
rep_dna_info = "\t\t\t\t"
if contig_id in repetitive_dna:
rep_dna_info = repetitive_dna[contig_id]
contig_status = 'Incomplete'
if contig_id in circular_contigs:
contig_status = 'Circular'
results_fh.write(
"{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format(
file_id, 'chromosome', contig_id,
len(contig_seqs[contig_id]), contig_status, '', '', '', '',
'', '', rep_dna_info))
results_fh.close()
write_fasta_dict(chr_contigs, chromosome_file)
if args.run_typer:
mobtyper_results = "file_id\tnum_contigs\ttotal_length\tgc\t" \
"rep_type(s)\trep_type_accession(s)\t" \
"relaxase_type(s)\trelaxase_type_accession(s)\t" \
"mpf_type\tmpf_type_accession(s)\t" \
"orit_type(s)\torit_accession(s)\tPredictedMobility\t" \
"mash_nearest_neighbor\tmash_neighbor_distance\tmash_neighbor_cluster\n"
for file in plasmid_files:
mobtyper_results = mobtyper_results + "{}".format(
run_mob_typer(file, out_dir, str(num_threads)))
fh = open(mobtyper_results_file, 'w')
fh.write(mobtyper_results)
fh.close()
if not keep_tmp:
shutil.rmtree(tmp_dir)