def main():
default_database_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'databases')
args = parse_args()
if args.debug:
init_console_logger(3)
logging.info('Running Mob-typer 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()
if not os.path.isfile(args.infile):
logging.info('Error, fasta file does not exist')
sys.exit()
if not os.path.isdir(args.outdir):
os.mkdir(args.outdir, 0o755)
if not isinstance(args.num_threads, int):
logging.info('Error number of threads must be an integer, you specified "{}"'.format(args.num_threads))
database_dir = os.path.abspath(args.database_directory)
verify_init(logging, database_dir)
# Script arguments
input_fasta = args.infile
out_dir = args.outdir
num_threads = int(args.num_threads)
keep_tmp = args.keep_tmp
if database_dir == default_database_dir:
mob_ref = args.plasmid_mob
mpf_ref = args.plasmid_mpf
orit_ref = args.plasmid_orit
mash_db = args.plasmid_mash_db
replicon_ref = args.plasmid_replicons
else:
mob_ref = os.path.join(database_dir, 'mob.proteins.faa')
mpf_ref = os.path.join(database_dir, 'mpf.proteins.faa')
orit_ref = os.path.join(database_dir, 'orit.fas')
mash_db = os.path.join(database_dir, 'ncbi_plasmid_full_seqs.fas.msh')
replicon_ref = os.path.join(database_dir, 'rep.dna.fas')
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')
replicon_blast_results = os.path.join(tmp_dir, 'replicon_blast_results.txt')
mob_blast_results = os.path.join(tmp_dir, 'mobtyper_blast_results.txt')
mpf_blast_results = os.path.join(tmp_dir, 'mpf_blast_results.txt')
orit_blast_results = os.path.join(tmp_dir, 'orit_blast_results.txt')
if os.path.isfile(mob_blast_results):
os.remove(mob_blast_results)
if os.path.isfile(mpf_blast_results):
os.remove(mpf_blast_results)
if os.path.isfile(orit_blast_results):
os.remove(orit_blast_results)
if os.path.isfile(replicon_blast_results):
os.remove(replicon_blast_results)
report_file = os.path.join(out_dir, 'mobtyper_' + file_id + '_report.txt')
mash_file = os.path.join(tmp_dir, 'mash_' + file_id + '.txt')
# Input numeric params
min_rep_ident = float(args.min_rep_ident)
min_mob_ident = float(args.min_mob_ident)
min_ori_ident = float(args.min_ori_ident)
min_mpf_ident = float(args.min_mpf_ident)
idents = {'min_rep_ident': min_rep_ident, 'min_mob_ident': min_mob_ident, 'min_ori_ident': min_ori_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_ori_cov = float(args.min_ori_cov)
min_mpf_cov = float(args.min_mpf_cov)
covs = {'min_rep_cov': min_rep_cov, 'min_mob_cov': min_mob_cov, 'min_con_cov': min_ori_cov,
'min_rpp_cov': min_ori_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_ori_evalue = float(args.min_ori_evalue)
min_mpf_evalue = float(args.min_mpf_evalue)
evalues = {'min_rep_evalue': min_rep_evalue, 'min_mob_evalue': min_mob_evalue, 'min_con_evalue': min_ori_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)
check_dependencies(logging)
needed_dbs = [replicon_ref, mob_ref, mash_db, mpf_ref]
for db in needed_dbs:
if (not os.path.isfile(db)):
logging.info('Warning! Needed database missing "{}"'.format(db))
mob_suite.mob_init.main()
if not os.path.isdir(tmp_dir):
os.mkdir(tmp_dir, 0o755)
fix_fasta_header(input_fasta, fixed_fasta)
# run individual marker blasts
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))
found_replicons = dict()
for contig_id in replicon_contigs:
for hit in replicon_contigs[contig_id]:
acs, type = hit.split('|')
found_replicons[acs] = type
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))
found_mob = dict()
for contig_id in mob_contigs:
for hit in mob_contigs[contig_id]:
acs, type = hit.split('|')
found_mob[acs] = type
# print (found_mob)
logging.info('Running mpf blast on {}'.format(mob_ref))
mpf_contigs = getRepliconContigs(
mob_blast(mpf_ref, fixed_fasta, min_mpf_ident, min_mpf_cov, min_mpf_evalue, tmp_dir, mpf_blast_results, num_threads=num_threads))
found_mpf = dict()
for contig_id in mpf_contigs:
for hit in mpf_contigs[contig_id]:
acs, type = hit.split('|')
found_mpf[acs] = type
# print(found_mpf)
logging.info('Running orit blast on {}'.format(replicon_ref))
orit_contigs = getRepliconContigs(
replicon_blast(orit_ref, fixed_fasta, min_ori_ident, min_ori_cov, min_ori_evalue, tmp_dir, orit_blast_results,
num_threads=num_threads))
found_orit = dict()
for contig_id in orit_contigs:
for hit in orit_contigs[contig_id]:
acs, type = hit.split('|')
found_orit[acs] = type
# Get closest neighbor by mash distance
m = mash()
mash_distances = dict()
mashfile_handle = open(mash_file, 'w')
m.run_mash(mash_db, fixed_fasta, mashfile_handle)
mash_results = m.read_mash(mash_file)
mash_top_hit = getMashBestHit(mash_results)
results_fh = open(report_file, 'w')
results_fh.write("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")
if len(found_replicons) > 0:
rep_types = ",".join(list(found_replicons.values()))
rep_acs = ",".join(list(found_replicons.keys()))
else:
rep_types = "-"
rep_acs = "-"
if len(found_mob) > 0:
mob_types = ",".join(list(found_mob.values()))
mob_acs = ",".join(list(found_mob.keys()))
else:
mob_types = "-"
mob_acs = "-"
if len(found_mpf) > 0:
mpf_type = determine_mpf_type(found_mpf)
mpf_acs = ",".join(list(found_mpf.keys()))
else:
mpf_type = "-"
mpf_acs = "-"
if len(found_orit) > 0:
orit_types = ",".join(list(found_orit.values()))
orit_acs = ",".join(list(found_orit.keys()))
else:
orit_types = "-"
orit_acs = "-"
stats = calcFastaStats(fixed_fasta)
predicted_mobility = 'Non-mobilizable'
if mob_acs != '-' or orit_acs != '-':
predicted_mobility = 'Mobilizable'
if mob_acs != '-' and mpf_acs != '-':
predicted_mobility = 'Conjugative'
string = "{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}".format(file_id, stats['num_seq'],
stats['size'], stats['gc_content'],
rep_types, rep_acs, mob_types,
mob_acs, mpf_type, mpf_acs,
orit_types, orit_acs,
predicted_mobility,
mash_top_hit['top_hit'],
mash_top_hit['mash_hit_score'],
mash_top_hit['clustid'])
results_fh.write(string)
if not keep_tmp:
shutil.rmtree(tmp_dir)
print("{}".format(string))
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 Mob-typer 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()
if not os.path.isfile(args.infile):
logging.info('Error, fasta file does not exist')
sys.exit()
if not os.path.isdir(args.outdir):
os.mkdir(args.outdir, 0o755)
if not isinstance(args.num_threads, int):
logging.info(
'Error number of threads must be an integer, you specified "{}"'.
format(args.num_threads))
verify_init(logging)
# Script arguments
input_fasta = args.infile
input_fasta = args.infile
out_dir = args.outdir
num_threads = int(args.num_threads)
evalue = args.evalue
keep_tmp = args.keep_tmp
mob_ref = args.plasmid_mob
mpf_ref = args.plasmid_mpf
orit_ref = args.plasmid_orit
mash_db = args.plasmid_mash_db
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')
replicon_ref = args.plasmid_replicons
replicon_blast_results = os.path.join(tmp_dir,
'replicon_blast_results.txt')
mob_blast_results = os.path.join(tmp_dir, 'mobtyper_blast_results.txt')
mpf_blast_results = os.path.join(tmp_dir, 'mpf_blast_results.txt')
orit_blast_results = os.path.join(tmp_dir, 'orit_blast_results.txt')
if os.path.isfile(mob_blast_results):
os.remove(mob_blast_results)
if os.path.isfile(mpf_blast_results):
os.remove(mpf_blast_results)
if os.path.isfile(orit_blast_results):
os.remove(orit_blast_results)
if os.path.isfile(replicon_blast_results):
os.remove(replicon_blast_results)
report_file = os.path.join(out_dir, 'mobtyper_' + file_id + '_report.txt')
mash_file = os.path.join(tmp_dir, 'mash_' + file_id + '.txt')
if not os.path.isdir(tmp_dir):
os.mkdir(tmp_dir, 0o755)
fix_fasta_header(input_fasta, fixed_fasta)
# run individual marker blasts
logging.info('Running replicon blast on {}'.format(replicon_ref))
replicon_contigs = getRepliconContigs(
replicon_blast(replicon_ref,
fixed_fasta,
80,
80,
args.evalue,
tmp_dir,
replicon_blast_results,
num_threads=num_threads))
found_replicons = dict()
for contig_id in replicon_contigs:
for hit in replicon_contigs[contig_id]:
acs, type = hit.split('|')
found_replicons[acs] = type
# print(found_replicons)
logging.info('Running relaxase blast on {}'.format(mob_ref))
mob_contigs = getRepliconContigs(
mob_blast(mob_ref,
fixed_fasta,
80,
80,
args.evalue,
tmp_dir,
mob_blast_results,
num_threads=num_threads))
found_mob = dict()
for contig_id in mob_contigs:
for hit in mob_contigs[contig_id]:
acs, type = hit.split('|')
found_mob[acs] = type
# print (found_mob)
logging.info('Running mpf blast on {}'.format(mob_ref))
mpf_contigs = getRepliconContigs(
mob_blast(mpf_ref,
fixed_fasta,
85,
85,
args.evalue,
tmp_dir,
mpf_blast_results,
num_threads=num_threads))
found_mpf = dict()
for contig_id in mpf_contigs:
for hit in mpf_contigs[contig_id]:
acs, type = hit.split('|')
found_mpf[acs] = type
# print(found_mpf)
logging.info('Running orit blast on {}'.format(replicon_ref))
orit_contigs = getRepliconContigs(
replicon_blast(orit_ref,
fixed_fasta,
90,
90,
args.evalue,
tmp_dir,
orit_blast_results,
num_threads=num_threads))
found_orit = dict()
for contig_id in orit_contigs:
for hit in orit_contigs[contig_id]:
acs, type = hit.split('|')
found_orit[acs] = type
# print(found_orit)
# Get closest neighbor by mash distance
m = mash()
mash_distances = dict()
mashfile_handle = open(mash_file, 'w')
m.run_mash(mash_db, fixed_fasta, mashfile_handle)
mash_results = m.read_mash(mash_file)
mash_top_hit = getMashBestHit(mash_results)
results_fh = open(report_file, 'w')
results_fh.write("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")
if len(found_replicons) > 0:
rep_types = ",".join(list(found_replicons.values()))
rep_acs = ",".join(list(found_replicons.keys()))
else:
rep_types = "-"
rep_acs = "-"
if len(found_mob) > 0:
mob_types = ",".join(list(found_mob.values()))
mob_acs = ",".join(list(found_mob.keys()))
else:
mob_types = "-"
mob_acs = "-"
if len(found_mpf) > 0:
mpf_type = determine_mpf_type(found_mpf)
mpf_acs = ",".join(list(found_mpf.keys()))
else:
mpf_type = "-"
mpf_acs = "-"
if len(found_orit) > 0:
orit_types = ",".join(list(found_orit.values()))
orit_acs = ",".join(list(found_orit.keys()))
else:
orit_types = "-"
orit_acs = "-"
stats = calcFastaStats(fixed_fasta)
predicted_mobility = 'Non-mobilizable'
if mob_acs != '-' or orit_acs != '-':
predicted_mobility = 'Mobilizable'
if mob_acs != '-' and mpf_acs != '-':
predicted_mobility = 'Conjugative'
string = "{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}".format(
file_id, stats['num_seq'], stats['size'], stats['gc_content'],
rep_types, rep_acs, mob_types, mob_acs, mpf_type, mpf_acs, orit_types,
orit_acs, predicted_mobility, mash_top_hit['top_hit'],
mash_top_hit['mash_hit_score'], mash_top_hit['clustid'])
results_fh.write(string)
if not keep_tmp:
shutil.rmtree(tmp_dir)
print("{}".format(string))
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)
def main():
default_database_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'databases')
args = parse_args()
if args.debug:
logger = init_console_logger(3)
else:
logger = init_console_logger(2)
logger.info('Running Mob-typer version {}'.format(__version__))
if not args.outdir:
logger.info('Error, no output directory specified, please specify one')
sys.exit()
if not args.infile:
logger.info('Error, no fasta specified, please specify one')
sys.exit()
if not os.path.isfile(args.infile):
logger.info('Error, fasta file does not exist')
sys.exit()
if not os.path.isdir(args.outdir):
os.mkdir(args.outdir, 0o755)
if not isinstance(args.num_threads, int):
logger.info('Error number of threads must be an integer, you specified "{}"'.format(args.num_threads))
database_dir = os.path.abspath(args.database_directory)
verify_init(logger,database_dir)
# Script arguments
input_fasta = args.infile
out_dir = args.outdir
num_threads = int(args.num_threads)
keep_tmp = args.keep_tmp
if database_dir == default_database_dir:
mob_ref = args.plasmid_mob
mpf_ref = args.plasmid_mpf
orit_ref = args.plasmid_orit
mash_db = args.plasmid_mash_db
replicon_ref = args.plasmid_replicons
else:
mob_ref = os.path.join(database_dir, 'mob.proteins.faa')
mpf_ref = os.path.join(database_dir, 'mpf.proteins.faa')
orit_ref = os.path.join(database_dir, 'orit.fas')
mash_db = os.path.join(database_dir, 'ncbi_plasmid_full_seqs.fas.msh')
replicon_ref = os.path.join(database_dir, 'rep.dna.fas')
tmp_dir = os.path.join(out_dir, '__tmp')
file_id = os.path.basename(input_fasta)
#output_file_prefix = re.sub(r"\..*", "", file_id) # remove file extension by matching everything before dot
fixed_fasta = os.path.join(tmp_dir, 'fixed.input.fasta')
replicon_blast_results = os.path.join(tmp_dir, 'replicon_blast_results.txt')
mob_blast_results = os.path.join(tmp_dir, 'mobtyper_blast_results.txt')
mpf_blast_results = os.path.join(tmp_dir, 'mpf_blast_results.txt')
orit_blast_results = os.path.join(tmp_dir, 'orit_blast_results.txt')
if os.path.isfile(mob_blast_results):
os.remove(mob_blast_results)
if os.path.isfile(mpf_blast_results):
os.remove(mpf_blast_results)
if os.path.isfile(orit_blast_results):
os.remove(orit_blast_results)
if os.path.isfile(replicon_blast_results):
os.remove(replicon_blast_results)
report_file = os.path.join(out_dir, 'mobtyper_' + file_id + '_report.txt')
mash_file = os.path.join(tmp_dir, 'mash_' + file_id + '.txt')
# Input numeric params
min_rep_ident = float(args.min_rep_ident)
min_mob_ident = float(args.min_mob_ident)
min_ori_ident = float(args.min_ori_ident)
min_mpf_ident = float(args.min_mpf_ident)
idents = {'min_rep_ident': min_rep_ident, 'min_mob_ident': min_mob_ident, 'min_ori_ident': min_ori_ident}
for param in idents:
value = float(idents[param])
if value < 60:
logger.error("Error: {} is too low, please specify an integer between 70 - 100".format(param))
sys.exit(-1)
if value > 100:
logger.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_ori_cov = float(args.min_ori_cov)
min_mpf_cov = float(args.min_mpf_cov)
covs = {'min_rep_cov': min_rep_cov, 'min_mob_cov': min_mob_cov, 'min_con_cov': min_ori_cov,
'min_rpp_cov': min_ori_cov}
for param in covs:
value = float(covs[param])
if value < 60:
logger.error("Error: {} is too low, please specify an integer between 50 - 100".format(param))
sys.exit(-1)
if value > 100:
logger.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_ori_evalue = float(args.min_ori_evalue)
min_mpf_evalue = float(args.min_mpf_evalue)
evalues = {'min_rep_evalue': min_rep_evalue, 'min_mob_evalue': min_mob_evalue, 'min_con_evalue': min_ori_evalue}
for param in evalues:
value = float(evalues[param])
if value > 1:
logger.error("Error: {} is too high, please specify an float evalue between 0 to 1".format(param))
sys.exit(-1)
check_dependencies(logger)
needed_dbs = [replicon_ref, mob_ref, mash_db, mpf_ref]
for db in needed_dbs:
if (not os.path.isfile(db)):
logger.info('Warning! Needed database missing "{}"'.format(db))
mob_suite.mob_init.main()
if not os.path.isdir(tmp_dir):
os.mkdir(tmp_dir, 0o755)
fix_fasta_header(input_fasta, fixed_fasta)
# run individual marker blasts
logger.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))
found_replicons = dict()
for contig_id in replicon_contigs:
for hit in replicon_contigs[contig_id]:
acs, type = hit.split('|')
found_replicons[acs] = type
#print("These replicons are found")
#print(list(found_replicons.values()))
logger.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))
found_mob = dict()
for contig_id in mob_contigs:
for hit in mob_contigs[contig_id]:
acs, type = hit.split('|')
found_mob[acs] = type
#print ("These are relaxeses found")
#print (list(found_mob.values()))
logger.info('Running mpf blast on {}'.format(mob_ref))
mpf_contigs = getRepliconContigs(
mob_blast(mpf_ref, fixed_fasta, min_mpf_ident, min_mpf_cov, min_mpf_evalue, tmp_dir, mpf_blast_results, num_threads=num_threads))
found_mpf = dict()
for contig_id in mpf_contigs:
for hit in mpf_contigs[contig_id]:
acs, type = hit.split('|')
found_mpf[acs] = type
# print(found_mpf)
logger.info('Running orit blast on {}'.format(replicon_ref))
orit_contigs = getRepliconContigs(
replicon_blast(orit_ref, fixed_fasta, min_ori_ident, min_ori_cov, min_ori_evalue, tmp_dir, orit_blast_results,
num_threads=num_threads))
found_orit = dict()
for contig_id in orit_contigs:
for hit in orit_contigs[contig_id]:
acs, type = hit.split('|')
found_orit[acs] = type
# Get closest neighbor by mash distance in the entire plasmid database
m = mash()
#mash_distances = dict()
mashfile_handle = open(mash_file, 'w')
m.run_mash(mash_db, fixed_fasta, mashfile_handle)
mash_results = m.read_mash(mash_file)
mash_top_hit = getMashBestHit(mash_results)
# GET HOST RANGE
host_range_literature_report_df = pandas.DataFrame()
if args.host_range_detailed and found_replicons:
(host_range_refseq_rank, host_range_refseq_name, taxids, taxids_df, stats_host_range) = getRefSeqHostRange(
replicon_name_list=list(found_replicons.values()),
mob_cluster_id_list=[mash_top_hit['clustid']],
relaxase_name_acc_list=None,
relaxase_name_list=None,
matchtype="loose_match",hr_obs_data = loadHostRangeDB())
if '-' in taxids:
host_range_refseq_rank = None;
host_range_refseq_name = None
else:
refseqtree = getTaxonomyTree(taxids) #refseq tree
renderTree(
tree=refseqtree,
filename_prefix=args.outdir+"/"+file_id+"_refseqhostrange_")
#get literature report summary dataframe (might be more than 1 row if multiple replicons are present)
host_range_literature_report_df, littaxids = getLiteratureBasedHostRange(replicon_names = list(found_replicons.values()),
plasmid_lit_db = loadliteratureplasmidDB(),
input_seq = args.infile )
if littaxids:
littree = getTaxonomyTree(littaxids) #get literature tree
renderTree(
tree=littree,
filename_prefix=args.outdir+"/"+file_id+ "_literaturehostrange_")
#write hostrange reports
writeOutHostRangeReports(filename_prefix = args.outdir+"/"+file_id,
samplename=file_id,
replicon_name_list = list(found_replicons.values()),
mob_cluster_id_list = [mash_top_hit['clustid']],
relaxase_name_acc_list = None,
relaxase_name_list = None,
convergance_rank=host_range_refseq_rank,
convergance_taxonomy=host_range_refseq_name,
stats_host_range_dict=stats_host_range,
literature_hr_report=host_range_literature_report_df)
elif args.host_range_detailed and found_mob: #by MOB_accession numbers
(host_range_refseq_rank, host_range_refseq_name, taxids, taxids_df, stats_host_range) = getRefSeqHostRange(
replicon_name_list=None,
mob_cluster_id_list=[mash_top_hit['clustid']],
relaxase_name_acc_list=found_mob.keys(),
relaxase_name_list=None,
matchtype="loose_match", hr_obs_data=loadHostRangeDB())
refseqtree = getTaxonomyTree(taxids) # refseq tree
renderTree(
tree=refseqtree,
filename_prefix=args.outdir + "/" + file_id + "_refseqhostrange_")
writeOutHostRangeReports(filename_prefix=args.outdir + "/" + file_id,
samplename=file_id,
replicon_name_list=None,
mob_cluster_id_list=[mash_top_hit['clustid']],
relaxase_name_acc_list=None,
relaxase_name_list=None,
convergance_rank=host_range_refseq_rank,
convergance_taxonomy=host_range_refseq_name,
stats_host_range_dict=stats_host_range
)
#print(host_range_refseq_rank, host_range_refseq_name, taxids_df["Organism"])
else:
host_range_refseq_rank=None; host_range_refseq_name=None
#END HOST RANGE MODULE
if len(found_replicons) > 0:
found_replicons = OrderedDict(sorted(found_replicons.items(), key=itemgetter(1), reverse=False))
rep_types = ",".join(list(found_replicons.values()))
rep_acs = ",".join(list(found_replicons.keys()))
else:
rep_types = "-"
rep_acs = "-"
if len(found_mob) > 0:
found_mob = OrderedDict(sorted(found_mob.items(), key=itemgetter(1), reverse=False))
mob_types = ",".join(list(found_mob.values()))
mob_acs = ",".join(list(found_mob.keys()))
else:
mob_types = "-"
mob_acs = "-"
if len(found_mpf) > 0:
found_mpf = OrderedDict(sorted(found_mpf.items(), key=itemgetter(1), reverse=False))
mpf_type = determine_mpf_type(found_mpf)
mpf_acs = ",".join(list(found_mpf.keys()))
else:
mpf_type = "-"
mpf_acs = "-"
if len(found_orit) > 0:
found_orit = OrderedDict(sorted(found_orit.items(), key=itemgetter(1), reverse=False))
orit_types = ",".join(list(found_orit.values()))
orit_acs = ",".join(list(found_orit.keys()))
else:
orit_types = "-"
orit_acs = "-"
stats = calcFastaStats(fixed_fasta)
predicted_mobility = 'Non-mobilizable'
if mob_acs != '-' or orit_acs != '-':
predicted_mobility = 'Mobilizable'
if mob_acs != '-' and mpf_acs != '-':
predicted_mobility = 'Conjugative'
main_report_data_dict=collections.OrderedDict({"file_id":re.sub(r"\.(fasta|fa|fas){1,1}","",file_id), "num_contigs":stats['num_seq'], "total_length": stats['size'], "gc":stats['gc_content'],
"rep_type(s)": rep_types, "rep_type_accession(s)": rep_acs, "relaxase_type(s)":mob_types,
"relaxase_type_accession(s)": mob_acs, "mpf_type": mpf_type, "mpf_type_accession(s)": mpf_acs,
"orit_type(s)": orit_types, "orit_accession(s)": orit_acs, "PredictedMobility": predicted_mobility,
"mash_nearest_neighbor": mash_top_hit['top_hit'],"mash_neighbor_distance": mash_top_hit['mash_hit_score'],
"mash_neighbor_cluster": mash_top_hit['clustid'], "NCBI-HR-rank":"-","NCBI-HR-Name":"-",
"LitRepHRPlasmClass":"-","LitPredDBHRRank":"-","LitPredDBHRRankSciName":"-",
"LitRepHRRankInPubs":"-", "LitRepHRNameInPubs":"-","LitMeanTransferRate":"-",
"LitClosestRefAcc":"-", "LitClosestRefDonorStrain":"-",
"LitClosestRefRecipientStrain":"-","LitClosestRefTransferRate":"-", "LitClosestConjugTemp":"-",
"LitPMIDs":"-","LitPMIDsNumber":"-"})
main_report_mobtyper_df = pandas.DataFrame(columns=main_report_data_dict.keys())
#print(host_range_literature_report_collapsed_df)
if host_range_refseq_rank and host_range_refseq_name:
main_report_data_dict.update({"NCBI-HR-rank":host_range_refseq_rank,"NCBI-HR-Name":host_range_refseq_name})
if host_range_literature_report_df.empty == False:
if host_range_literature_report_df.shape[0] >= 2: #collapse host range repor more than 2 rows
host_range_literature_report_df = collapseLiteratureReport(host_range_literature_report_df)
main_report_data_dict.update({"LitRepHRPlasmClass":host_range_literature_report_df["LiteratureReportedHostRangePlasmidClass"].values[0],
"LitPredDBHRRank":host_range_literature_report_df["LiteraturePredictedHostRangeTreeRank"].values[0],
"LitPredDBHRRankSciName": host_range_literature_report_df["LiteraturePredictedHostRangeTreeRankSciName"].values[0],
"LitRepHRRankInPubs":host_range_literature_report_df["LiteratureReportedHostRangeRankInPubs"].values[0],
"LitRepHRNameInPubs": host_range_literature_report_df["LiteratureReportedHostRangeNameInPubs"].values[0],
"LitMeanTransferRate":host_range_literature_report_df["LiteratureMeanTransferRateRange"].values[0],
"LitClosestRefAcc":host_range_literature_report_df["LiteratureClosestRefrencePlasmidAcc"].values[0],
"LitClosestMashDist": host_range_literature_report_df["LiteratureClosestReferenceMashDistance"].values[0],
"LitClosestRefDonorStrain": host_range_literature_report_df["LiteratureClosestReferenceDonorStrain"].values[0],
"LitClosestRefRecipientStrain": host_range_literature_report_df["LiteratureClosestReferenceRecipientStrain"].values[0],
"LitClosestRefTransferRate": host_range_literature_report_df["LiteratureClosestReferenceTransferRate"].values[0],
"LitClosestConjugTemp": host_range_literature_report_df["LiteratureClosestReferenceConjugationTemperature"].values[0],
"LitPMIDs": host_range_literature_report_df["LiteraturePMIDs"].values[0],
"LitPMIDsNumber":host_range_literature_report_df["LiteraturePublicationsNumber"].values[0]
})
main_report_mobtyper_df = main_report_mobtyper_df.append(pandas.DataFrame([main_report_data_dict]),sort=False)
main_report_mobtyper_df.to_csv(report_file, sep="\t", mode="w",encoding="UTF-8",index=False)
if not keep_tmp:
shutil.rmtree(tmp_dir)
logger.info("Run completed")