def tblastn(input_fasta, blastdb, min_ident, min_covs, evalue, out_dir, blast_results_file, logging, num_threads=1,
min_covhsp=25, seq_id_file=None):
blast_runner = BlastRunner(input_fasta, out_dir)
blast_runner.run_tblastn(query_fasta_path=input_fasta, blast_task='megablast', db_path=blastdb,
db_type='protein', min_cov=min_covs, min_ident=min_ident, evalue=evalue,
blast_outfile=blast_results_file,
num_threads=num_threads, seq_id_file=seq_id_file, logging=logging)
if os.path.getsize(blast_results_file) == 0:
os.remove(blast_results_file)
return False
blast_df = BlastReader(blast_results_file, logging).df
blast_df = blast_df.loc[blast_df['pident'] >= min_ident]
blast_df = blast_df.loc[blast_df['qcovs'] >= min_covs]
blast_df = blast_df.loc[blast_df['qcovhsp'] >= min_covhsp]
blast_df = blast_df.loc[blast_df['evalue'] <= evalue]
blast_df = fixStart(blast_df)
blast_df = blast_df.sort_values(['sseqid', 'sstart', 'send', 'bitscore'], ascending=[True, True, True, False])
blast_df = blast_df.reset_index(drop=True)
blast_df.to_csv(blast_results_file, sep='\t', header=True, line_terminator='\n', index=False)
return True
def blastn(input_fasta, blastdb, min_ident, min_cov, evalue, min_length, out_dir, blast_results_file, logging,
seq_filterfile=None, num_threads=1, max_length=400000, min_hsp_cov=1):
blast_runner = BlastRunner(input_fasta, out_dir)
blast_runner.run_blast(query_fasta_path=input_fasta, blast_task='megablast', db_path=blastdb,
db_type='nucl', min_cov=min_cov, min_ident=min_ident, evalue=evalue,
blast_outfile=blast_results_file, logging=logging, num_threads=num_threads, word_size=11,
seq_id_file=seq_filterfile)
if os.path.getsize(blast_results_file) == 0:
os.remove(blast_results_file)
return False
blast_df = BlastReader(blast_results_file, logging).df
blast_df = blast_df.loc[blast_df['length'] >= min_length]
blast_df = blast_df.loc[blast_df['qlen'] <= max_length]
blast_df = blast_df.loc[blast_df['qcovs'] >= min_cov]
blast_df = blast_df.loc[blast_df['qcovhsp'] >= min_hsp_cov]
blast_df = blast_df.loc[blast_df['evalue'] <= evalue]
blast_df = blast_df.loc[blast_df['pident'] >= min_ident]
blast_df = blast_df.reset_index(drop=True)
blast_df = fixStart(blast_df)
blast_df.to_csv(blast_results_file, sep='\t', header=True, line_terminator='\n', index=False)
return True
def repetitive_blast(input_fasta,
ref_db,
min_ident,
min_cov,
evalue,
min_length,
tmp_dir,
blast_results_file,
num_threads=1):
blast_runner = BlastRunner(input_fasta, tmp_dir)
#blast_runner.makeblastdb(ref_db, 'nucl')
blast_runner.run_blast(query_fasta_path=input_fasta,
blast_task='megablast',
db_path=ref_db,
db_type='nucl',
min_cov=min_cov,
min_ident=min_ident,
evalue=evalue,
blast_outfile=blast_results_file,
num_threads=num_threads)
if os.path.getsize(blast_results_file) == 0:
return dict()
blast_df = BlastReader(blast_results_file).df
blast_df = blast_df.loc[blast_df['length'] >= min_length]
blast_df = blast_df.loc[blast_df['pident'] >= min_ident]
blast_df = blast_df.loc[blast_df['qcovs'] >= min_cov]
blast_df = blast_df.loc[blast_df['qcovhsp'] >= 25]
blast_df = fixStart(blast_df)
blast_df = blast_df.sort_values(['sseqid', 'sstart', 'send', 'bitscore'],
ascending=[True, True, True, False])
blast_df = blast_df.reset_index(drop=True)
contig_list = dict()
for index, row in blast_df.iterrows():
if not row['qseqid'] in contig_list:
contig_list[row['qseqid']] = {
'id': row['sseqid'],
'score': row['bitscore'],
'contig_start': row['sstart'],
'contig_end': row['send']
}
else:
if contig_list[row['qseqid']]['score'] > row['bitscore']:
contig_list[row['qseqid']] = {
'id': row['sseqid'],
'score': row['bitscore'],
'contig_start': row['sstart'],
'contig_end': row['send']
}
return contig_list
def main():
logging = init_console_logger(2)
logging.info('Initilizating databases...this will take some time')
#Find available threads and use the maximum number available for mash sketch but cap it at 32
num_threads = multiprocessing.cpu_count()
if num_threads > 32:
num_threads = 32
database_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)),'databases/')
zip_file = os.path.join(database_directory,'data.zip')
plasmid_database_fasta_file = os.path.join(database_directory,'ncbi_plasmid_full_seqs.fas')
repetitive_fasta_file = os.path.join(database_directory,'repetitive.dna.fas')
mash_db_file = os.path.join(database_directory,'ncbi_plasmid_full_seqs.fas.msh')
download_to_file('https://ndownloader.figshare.com/articles/5841882?private_link=a4c92dd84f17b2cefea6',zip_file)
extract(zip_file,database_directory)
os.remove(zip_file)
files = [f for f in listdir(database_directory) if isfile(join(database_directory, f))]
for file in files:
if file.endswith('gz'):
extract(os.path.join(database_directory,file), database_directory)
#Initilize blast and mash daatabases
blast_runner = BlastRunner(repetitive_fasta_file, database_directory)
blast_runner.makeblastdb(repetitive_fasta_file, 'nucl')
blast_runner = BlastRunner(plasmid_database_fasta_file, database_directory)
blast_runner.makeblastdb(plasmid_database_fasta_file, 'nucl')
mObj = mash()
mObj.mashsketch(plasmid_database_fasta_file,mash_db_file,num_threads=num_threads)
status_file = os.path.join(database_directory,'status.txt')
with gzip.open(status_file, 'w') as f:
f.write("Download date: {}".format(datetime.datetime.today().strftime('%Y-%m-%d')))
f.close()
def contig_blast(input_fasta, plasmid_db, min_ident, min_cov, evalue, min_length, tmp_dir, blast_results_file,
num_threads=1):
blast_runner = None
filtered_blast = os.path.join(tmp_dir, 'filtered_blast.txt')
blast_runner = BlastRunner(input_fasta, tmp_dir)
blast_runner.run_blast(query_fasta_path=input_fasta, blast_task='megablast', db_path=plasmid_db,
db_type='nucl', min_cov=min_cov, min_ident=min_ident, evalue=evalue,
blast_outfile=blast_results_file, num_threads=num_threads, word_size=11)
if os.path.getsize(blast_results_file) == 0:
fh = open(filtered_blast, 'w', encoding="utf-8")
fh.write('')
fh.close()
return dict()
blast_df = BlastReader(blast_results_file).df
blast_df = blast_df.loc[blast_df['length'] >= min_length]
blast_df = blast_df.loc[blast_df['qlen'] <= 400000]
blast_df = blast_df.loc[blast_df['qlen'] >= min_length]
blast_df = blast_df.loc[blast_df['qcovs'] >= min_cov]
blast_df = blast_df.reset_index(drop=True)
blast_df.to_csv(filtered_blast, sep='\t', header=False, line_terminator='\n', index=False)
def mob_blast(input_fasta,
ref_db,
min_ident,
min_cov,
evalue,
tmp_dir,
blast_results_file,
overlap=5,
num_threads=1):
num_threads = 1
blast_runner = BlastRunner(input_fasta, tmp_dir)
blast_runner.makeblastdb(ref_db, 'nucl')
blast_runner.run_tblastn(query_fasta_path=input_fasta,
blast_task='megablast',
db_path=ref_db,
db_type='nucl',
min_cov=min_cov,
min_ident=min_ident,
evalue=evalue,
blast_outfile=blast_results_file,
num_threads=num_threads)
if os.path.getsize(blast_results_file) == 0:
return dict()
blast_df = BlastReader(blast_results_file).df
blast_df = blast_df.loc[blast_df['pident'] >= min_ident]
blast_df = blast_df.loc[blast_df['qcovs'] >= min_cov]
blast_df = blast_df.loc[blast_df['qcovhsp'] >= 25]
blast_df = fixStart(blast_df)
blast_df = blast_df.sort_values(['sseqid', 'sstart', 'send', 'bitscore'],
ascending=[True, True, True, False])
blast_df = blast_df.reset_index(drop=True)
blast_df = filter_overlaping_records(blast_df, overlap, 'sseqid', 'sstart',
'send', 'bitscore')
prev_size = 0
size = str(len(blast_df))
while size != prev_size:
blast_df = filter_overlaping_records(blast_df, overlap, 'sseqid',
'sstart', 'send', 'bitscore')
prev_size = size
size = str(len(blast_df))
#print(blast_df)
return blast_df
def run_blast(self,
input_fasta,
output_path,
blast_results_file,
logging,
min_cov=1,
min_ident=1,
evalue=1,
num_threads=1,
min_length=25):
blast_runner = BlastRunner(input_fasta, output_path)
blast_runner.makeblastdb(input_fasta, 'nucl', logging)
blast_runner.run_blast(query_fasta_path=input_fasta,
blast_task='megablast',
db_path=input_fasta,
db_type='nucl',
min_cov=min_cov,
min_ident=min_ident,
evalue=evalue,
blast_outfile=blast_results_file,
num_threads=num_threads,
word_size=11,
logging=logging)
if os.path.getsize(blast_results_file) == 0:
fh = open(blast_results_file, 'w', encoding="utf-8")
fh.write('')
fh.close()
return dict()
blast_df = BlastReader(blast_results_file, logging).df
blast_df = blast_df.loc[blast_df['length'] >= min_length]
blast_df = blast_df.reset_index(drop=True)
blast_df.to_csv(blast_results_file,
sep='\t',
header=False,
line_terminator='\n',
index=False)
def main():
args = arguments()
database_directory = os.path.abspath(args.database_directory)
if os.path.exists(database_directory) == False:
os.mkdir(database_directory)
else:
logger.info("Database directory folder already exists at {}".format(
database_directory))
# Helper function to simplify adding database_directory to everything
prepend_db_dir = functools.partial(os.path.join, database_directory)
lockfilepath = os.path.join(database_directory, ".lock")
status_file = prepend_db_dir('status.txt')
if os.path.exists(lockfilepath) == False:
try:
open(file=lockfilepath, mode="w").close()
logger.info("Placed lock file at {}".format(lockfilepath))
except Exception as e:
logger.error(
"Failed to place a lock file at {}. Database diretory can not be accessed. Wrong path?"
.format(lockfilepath))
logger.error("{}".format(e))
pass
else:
while os.path.exists(lockfilepath):
elapsed_time = time.time() - os.path.getmtime(lockfilepath)
logger.info(
"Lock file found at {}. Waiting for other processes to finish database init ..."
.format(lockfilepath))
logger.info(
"Elapsed time {} min. Will continue processing after 16 min mark."
.format(int(elapsed_time / 60)))
if elapsed_time >= 1000:
logger.info(
"Elapsed time {} min. Assuming previous process completed all init steps. Continue ..."
.format(int(elapsed_time / 60)))
try: #if previous process failed, no processes are running and > 16 min passed since the lock was created
os.remove(lockfilepath)
except: #continue if file was removed by other process
pass
break
time.sleep(60) #recheck every 1 min if lock file was removed
logger.info(
"Lock file no longer exists. Assuming init process completed successfully"
)
return 0
logger.info('Initializing databases...this will take some time')
# Find available threads and use the maximum number available for mash sketch but cap it at 32
num_threads = min(multiprocessing.cpu_count(), 32)
if not os.path.exists(database_directory):
os.makedirs(database_directory)
zip_file = prepend_db_dir('data.tar.gz')
plasmid_database_fasta_file = prepend_db_dir('ncbi_plasmid_full_seqs.fas')
repetitive_fasta_file = prepend_db_dir('repetitive.dna.fas')
mash_db_file = prepend_db_dir('ncbi_plasmid_full_seqs.fas.msh')
logger.info('Downloading databases...this will take some time')
for db_mirror in config['db_mirrors']:
try:
logger.info('Trying mirror {}'.format(db_mirror))
download_to_file(db_mirror, zip_file)
break
except Exception as e:
logger.error(
"Download failed with error {}. Removing lock file".format(
str(e)))
os.remove(lockfilepath)
sys.exit(-1)
logger.info(
"Downloading databases successful, now building databases at {}".
format(database_directory))
extract(zip_file, database_directory)
files = [
prepend_db_dir(f) for f in os.listdir(database_directory)
if f.endswith('.gz')
]
for file in files:
extract(file, database_directory)
#Initialize blast and mash databases
try:
logger.info('Building repetitive mask database')
blast_runner = BlastRunner(repetitive_fasta_file, database_directory)
blast_runner.makeblastdb(repetitive_fasta_file, 'nucl', logger)
logger.info('Building complete plasmid database')
blast_runner = BlastRunner(plasmid_database_fasta_file,
database_directory)
blast_runner.makeblastdb(plasmid_database_fasta_file, 'nucl', logger,
True)
logger.info('Sketching complete plasmid database')
mObj = mash()
mObj.mashsketch(plasmid_database_fasta_file,
mash_db_file,
num_threads=num_threads)
except Exception as e:
logger.error(
'Downloading databases failed, please check your internet connection and retry'
)
logger.error(
"Process failed with error {}. Removing lock file".format(e))
os.remove(lockfilepath)
sys.exit(-1)
try:
logger.info("Init ete3 library ...")
ete3taxadbpath = os.path.abspath(
os.path.join(database_directory, "taxa.sqlite"))
ncbi = NCBITaxa()
ncbi.dbfile = ete3taxadbpath
ncbi.update_taxonomy_database()
except Exception as e:
logger.error(
"Init of ete3 library failed with error {}. Removing lock file".
format(e))
os.remove(lockfilepath)
sys.exit(-1)
try:
os.remove(os.path.join(os.getcwd(), "taxdump.tar.gz"))
logger.info(
"Removed residual taxdump.tar.gz as ete3 is not doing proper cleaning job."
)
except:
pass
with open(status_file, 'w') as f:
download_date = datetime.datetime.today().strftime('%Y-%m-%d')
f.write("Download date: {}. Removing lock file.".format(download_date))
try:
os.remove(lockfilepath)
except:
logger.warning(
"Lock file is already removed by some other process.")
pass
logger.info("MOB init completed successfully")
return 0
def main():
args = parse_args()
logging = init_console_logger(3)
logging.info('Running Mob-Suite Clustering toolkit v. {}'.format(__version__))
logging.info('Processing fasta file {}'.format(args.infile))
logging.info('Analysis directory {}'.format(args.outdir))
input_fasta = args.infile
if not os.path.isfile(input_fasta):
logging.error('Error, input fasta specified does not exist: {}'.format(input_fasta ))
sys.exit()
out_dir = args.outdir
num_threads = args.num_threads
if not os.path.isdir(out_dir):
os.mkdir(out_dir, 0o755)
tmp_dir = os.path.join(out_dir, '__tmp')
if not os.path.isdir(tmp_dir):
os.mkdir(tmp_dir, 0o755)
mode = str(args.mode).lower()
if mode not in ('update','build'):
logging.error('Error you have not entered a valid mode of build or update, you entered: {}'.format(mode))
print(('Error you have not entered a valid mode of build or update, you entered: {}'.format(mode)))
sys.exit()
header = ('id', 0.05, 0.0001)
tmp_cluster_file = os.path.join(out_dir, 'clusters.txt')
tmp_ref_fasta_file = os.path.join(tmp_dir, 'references_tmp.fasta')
update_fasta = os.path.join(out_dir, 'references_updated.fasta')
if mode == 'update':
if args.ref_cluster_file is None:
logging.error('Reference fasta file must be specified, please check help for parameter reference')
sys.exit()
ref_fasta = args.ref_fasta_file
if not os.path.isfile(ref_fasta ):
logging.error('Reference fasta file specified does not exist: {}'.format(ref_fasta))
sys.exit()
if args.ref_cluster_file is None:
logging.error('Reference cluster file must be specified, please check help for parameter reference')
sys.exit()
ref_cluster_file = args.ref_cluster_file
if not os.path.isfile(ref_cluster_file):
logging.error('Reference cluster file specified does not exist: {}'.format(ref_cluster_file))
sys.exit()
if args.ref_mash_db is None:
logging.error('Reference mash sketch file must be specified, please check help for parameter reference')
sys.exit()
ref_mash_db = args.ref_mash_db
if not os.path.isfile(ref_mash_db):
logging.error('Reference mash file specified does not exist: {}'.format(ref_mash_db))
sys.exit()
logging.info('Running mob-cluster in update mode with input file: {}'.format(input_fasta))
logging.info('Running mob-cluster in update mode with output directory: {}'.format(out_dir))
logging.info('Running mob-cluster in update mode on reference fasta file: {}'.format(ref_fasta))
logging.info('Reading previous cluster reference assignments from : {}'.format(ref_cluster_file))
shutil.copy(ref_cluster_file, tmp_cluster_file)
shutil.copy(ref_fasta, tmp_ref_fasta_file)
update_existing(input_fasta, tmp_dir, ref_mash_db, tmp_cluster_file, header, tmp_ref_fasta_file, update_fasta)
if args.overwrite:
shutil.move(update_fasta,ref_fasta)
shutil.move(tmp_cluster_file,ref_cluster_file)
mash_db_file = "{}.msh".format(input_fasta)
mObj = mash()
mObj.mashsketch(input_fasta, mash_db_file, num_threads=num_threads)
blast_runner = BlastRunner(ref_fasta, '')
blast_runner.makeblastdb(ref_fasta, 'nucl')
else:
mashObj = mash()
mashObj.mashsketch(input_fasta,input_fasta+".msh",num_threads=num_threads)
distance_matrix_file = os.path.join(tmp_dir,'mash_dist_matrix.txt')
mashfile_handle = open(distance_matrix_file,'w')
mashObj.run_mash(input_fasta+'.msh', input_fasta+'.msh', mashfile_handle,table=True,num_threads=num_threads)
clust_assignments = build_cluster_db(distance_matrix_file, (0.05, 0.0001))
writeClusterAssignments(tmp_cluster_file, header, clust_assignments)
clust_dict = selectCluster(clust_assignments, 1)
shutil.copy(input_fasta, tmp_ref_fasta_file)
updateFastaFile(tmp_ref_fasta_file ,update_fasta, clust_dict)
def main():
default_database_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'databases')
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--database_directory',
default=default_database_dir,
help='Directory to download databases to. Defaults to {}'.format(default_database_dir))
args = parser.parse_args()
logging = init_console_logger(2)
logging.info('Initilizating databases...this will take some time')
#Find available threads and use the maximum number available for mash sketch but cap it at 32
num_threads = multiprocessing.cpu_count()
if num_threads > 32:
num_threads = 32
# For some reason absolute paths don't work - enforce absolute path.
database_directory = os.path.abspath(args.database_directory)
if not os.path.exists(database_directory):
os.makedirs(database_directory)
zip_file = os.path.join(database_directory,'data.zip')
plasmid_database_fasta_file = os.path.join(database_directory,'ncbi_plasmid_full_seqs.fas')
repetitive_fasta_file = os.path.join(database_directory,'repetitive.dna.fas')
mash_db_file = os.path.join(database_directory,'ncbi_plasmid_full_seqs.fas.msh')
logging.info('Downloading databases...this will take some time')
db_mirrors = ['https://share.corefacility.ca/index.php/s/oeufkw5HyKz0X5I/download',
'https://ndownloader.figshare.com/articles/5841882/versions/1']
for db_mirror in db_mirrors:
logging.info('Trying mirror {}'.format(db_mirror))
download_to_file(db_mirror, zip_file)
if os.path.exists(zip_file) and os.path.getsize(zip_file) > 50000:
break #do not try other mirror
if (not os.path.isfile(zip_file)):
logging.error('Downloading databases failed, please check your internet connection and retry')
sys.exit(-1)
else:
logging.info('Downloading databases successful, now building databases')
extract(zip_file,database_directory)
os.remove(zip_file)
files = [f for f in listdir(database_directory) if isfile(join(database_directory, f))]
for file in files:
if file.endswith('gz'):
extract(os.path.join(database_directory,file), database_directory)
#Initilize blast and mash daatabases
logging.info('Building repetive mask database')
blast_runner = BlastRunner(repetitive_fasta_file, database_directory)
blast_runner.makeblastdb(repetitive_fasta_file, 'nucl')
logging.info('Building complete plasmid database')
blast_runner = BlastRunner(plasmid_database_fasta_file, database_directory)
blast_runner.makeblastdb(plasmid_database_fasta_file, 'nucl')
logging.info('Sketching complete plasmid database')
mObj = mash()
mObj.mashsketch(plasmid_database_fasta_file,mash_db_file,num_threads=num_threads)
status_file = os.path.join(database_directory,'status.txt')
with open(status_file, 'w') as f:
f.write("Download date: {}".format(datetime.datetime.today().strftime('%Y-%m-%d')))
f.close()
def main():
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__))
logger.info('Processing fasta file {}'.format(args.infile))
if not os.path.isfile(args.infile):
logger.info('Error, fasta file does not exist {}'.format(args.infile))
sys.exit()
if not args.analysis_dir:
tmp_dir = tempfile.TemporaryDirectory(dir=tempfile.gettempdir()).name
else:
tmp_dir = args.analysis_dir
if not os.path.isdir(tmp_dir):
os.mkdir(tmp_dir, 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)
if args.sample_id is None:
sample_id = re.sub(r"\.(fasta|fa|fas){1,1}", "",
os.path.basename(args.infile))
else:
sample_id = args.sample_id
# Script arguments
input_fasta = args.infile
report_file = args.out_file
num_threads = int(args.num_threads)
keep_tmp = args.keep_tmp
if args.multi:
multi = True
else:
multi = False
if not (args.primary_cluster_dist >= 0 and args.primary_cluster_dist <= 1):
logging.error(
'Error distance thresholds must be between 0 - 1: {}'.format(
args.primary_cluster_dist))
sys.exit()
else:
primary_distance = float(args.primary_cluster_dist)
if not (args.secondary_cluster_dist >= 0
and args.secondary_cluster_dist <= 1):
logging.error(
'Error distance thresholds must be between 0 - 1: {}'.format(
args.secondary_cluster_dist))
sys.exit()
else:
secondary_distance = float(args.secondary_cluster_dist)
if database_dir == default_database_dir:
mob_ref = args.plasmid_mob
mash_db = args.plasmid_mash_db
replicon_ref = args.plasmid_replicons
plasmid_meta = args.plasmid_meta
mpf_ref = args.plasmid_mpf
plasmid_orit = args.plasmid_orit
verify_init(logger, database_dir)
else:
mob_ref = os.path.join(database_dir, 'mob.proteins.faa')
mash_db = os.path.join(database_dir, 'ncbi_plasmid_full_seqs.fas.msh')
replicon_ref = os.path.join(database_dir, 'rep.dna.fas')
plasmid_meta = os.path.join(database_dir, 'clusters.txt')
mpf_ref = os.path.join(database_dir, 'mpf.proteins.faa')
plasmid_orit = os.path.join(database_dir, 'orit.fas')
LIT_PLASMID_TAXONOMY_FILE = os.path.join(
database_dir, "host_range_literature_plasmidDB.txt")
NCBI_PLASMID_TAXONOMY_FILE = plasmid_meta
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')
repetitive_blast_results = os.path.join(tmp_dir,
'repetitive_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)
# 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_rep_ident)
min_mpf_ident = float(args.min_mob_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_rep_cov)
min_mpf_cov = float(args.min_mob_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_rep_evalue)
min_mpf_evalue = float(args.min_mob_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)
# Test that ETE3 db is ok and lock process check
dbstatus = ETE3_db_status_check(1, ETE3_LOCK_FILE, ETE3DBTAXAFILE, logging)
if dbstatus == False:
logging.error(
"Exiting due to lock file not removed: {}".format(ETE3_LOCK_FILE))
sys.exit(-1)
# Get cluster information
reference_sequence_meta = read_sequence_info(plasmid_meta,
MOB_CLUSTER_INFO_HEADER)
# initilize master record tracking
fix_fasta_header(input_fasta, fixed_fasta)
contig_seqs = read_fasta_dict(fixed_fasta)
contig_info = {}
for id in contig_seqs:
seq = contig_seqs[id]
contig_info[id] = {}
for feature in MOB_TYPER_REPORT_HEADER:
contig_info[id][feature] = ''
contig_info[id]['md5'] = calc_md5(seq)
contig_info[id]['gc'] = GC(seq)
contig_info[id]['size'] = len(seq)
contig_info[id]['contig_id'] = id
contig_info[id]['sample_id'] = sample_id
# Makeblastdb
blast_runner = BlastRunner(fixed_fasta, tmp_dir)
build_success = blast_runner.makeblastdb(fixed_fasta,
'nucl',
logging=logging)
if build_success == False:
logging.error(
"Could not build blast database, check error messages..cannot continue"
)
sys.exit()
# run individual marker blasts
contig_info = identify_biomarkers(contig_info, fixed_fasta, tmp_dir, 25, logging, \
replicon_ref, min_rep_ident, min_rep_cov, min_rep_evalue, replicon_blast_results, \
mob_ref, min_mob_ident, min_mob_cov, min_mob_evalue, mob_blast_results, \
mpf_ref, min_mpf_ident, min_mpf_cov, min_mpf_evalue, mpf_blast_results, \
None, None, None, None, \
plasmid_orit, orit_blast_results, repetitive_blast_results, \
num_threads=1)
m = mash()
mobtyper_results = []
mash_input_fasta = fixed_fasta + '.msh'
ncbi = dict_from_alt_key_list(
read_file_to_dict(NCBI_PLASMID_TAXONOMY_FILE,
MOB_CLUSTER_INFO_HEADER,
separater="\t"), "sample_id")
lit = dict_from_alt_key_list(
read_file_to_dict(LIT_PLASMID_TAXONOMY_FILE,
LIT_PLASMID_TAXONOMY_HEADER,
separater="\t"), "sample_id")
if multi:
m.mashsketch(input_fasta=fixed_fasta,
output_path=mash_input_fasta,
sketch_ind=True,
num_threads=num_threads)
mash_results = parseMash(
m.run_mash(reference_db=mash_db,
input_fasta=mash_input_fasta,
table=False,
num_threads=num_threads))
for seq_id in mash_results:
record = {}
for field in MOB_TYPER_REPORT_HEADER:
if field in contig_info[seq_id]:
record[field] = contig_info[seq_id][field]
else:
record[field] = ''
record['sample_id'] = seq_id
record['num_contigs'] = 1
distances = OrderedDict(
sorted(mash_results[seq_id].items(),
key=itemgetter(1),
reverse=False))
for mash_neighbor_id in distances:
dist = distances[mash_neighbor_id]
if mash_neighbor_id not in reference_sequence_meta:
continue
else:
record['mash_nearest_neighbor'] = mash_neighbor_id
record['mash_neighbor_distance'] = dist
record['primary_cluster_id'] = reference_sequence_meta[
mash_neighbor_id]['primary_cluster_id']
record['secondary_cluster_id'] = reference_sequence_meta[
mash_neighbor_id]['secondary_cluster_id']
record[
'mash_neighbor_identification'] = reference_sequence_meta[
mash_neighbor_id]['organism']
break
mobtyper_results.append(record)
else:
m.mashsketch(input_fasta=fixed_fasta,
output_path=mash_input_fasta,
sketch_ind=False,
num_threads=num_threads)
mash_results = parseMash(
m.run_mash(reference_db=mash_db,
input_fasta=mash_input_fasta,
table=False,
num_threads=num_threads))
record = {}
for field in MOB_TYPER_REPORT_HEADER:
record[field] = ''
record['sample_id'] = sample_id
fastaSeqStats = calcFastaStats(fixed_fasta)
record['md5'] = fastaSeqStats['md5']
record['total_length'] = fastaSeqStats['size']
record['num_contigs'] = fastaSeqStats['num_seq']
record['gc'] = fastaSeqStats['gc_content']
record['mash_nearest_neighbor'] = '-'
record['mash_neighbor_distance'] = 1
record['primary_cluster_id'] = '-'
record['secondary_cluster_id'] = '-'
record['mash_neighbor_identification'] = '-'
for seq_id in mash_results:
distances = OrderedDict(
sorted(mash_results[seq_id].items(),
key=itemgetter(1),
reverse=False))
mash_neighbor_id = next(iter(distances))
dist = distances[mash_neighbor_id]
if mash_neighbor_id not in reference_sequence_meta:
continue
record['mash_nearest_neighbor'] = mash_neighbor_id
record['mash_neighbor_distance'] = dist
record['primary_cluster_id'] = reference_sequence_meta[
mash_neighbor_id]['primary_cluster_id']
record['secondary_cluster_id'] = reference_sequence_meta[
mash_neighbor_id]['secondary_cluster_id']
record['mash_neighbor_identification'] = reference_sequence_meta[
mash_neighbor_id]['organism']
record['rep_type(s)'] = []
record['rep_type_accession(s)'] = []
record['relaxase_type(s)'] = []
record['relaxase_type_accession(s)'] = []
record['mpf_type'] = []
record['mpf_type_accession(s)'] = []
record['orit_type(s)'] = []
record['orit_accession(s)'] = []
for seq_id in contig_info:
record['rep_type(s)'].append(contig_info[seq_id]['rep_type(s)'])
record['rep_type_accession(s)'].append(
contig_info[seq_id]['rep_type_accession(s)'])
record['relaxase_type(s)'].append(
contig_info[seq_id]['relaxase_type(s)'])
record['relaxase_type_accession(s)'].append(
contig_info[seq_id]['relaxase_type_accession(s)'])
record['mpf_type'].append(contig_info[seq_id]['mpf_type'])
record['mpf_type_accession(s)'].append(
contig_info[seq_id]['mpf_type_accession(s)'])
record['orit_type(s)'].append(contig_info[seq_id]['orit_type(s)'])
record['orit_accession(s)'].append(
contig_info[seq_id]['orit_accession(s)'])
for field in record:
tmp = []
if record[field] == None:
continue
if isinstance(record[field], list):
length = len(record[field])
for i in range(0, length):
tmp += record[field][i].split(',')
elif isinstance(record[field], str) and len(record[field]) > 0:
tmp += record[field].split(',')
if len(tmp) > 0:
record[field] = []
for d in tmp:
if len(d) > 0:
record[field].append(d)
mobtyper_results.append(record)
for i in range(0, len(mobtyper_results)):
record = mobtyper_results[i]
bio_markers = sort_biomarkers({
0: {
'types': record['rep_type(s)'],
'acs': record['rep_type_accession(s)']
},
1: {
'types': record['relaxase_type(s)'],
'acs': record['relaxase_type_accession(s)']
},
2: {
'types': record['mpf_type'],
'acs': record['mpf_type_accession(s)']
},
3: {
'types': record['orit_type(s)'],
'acs': record['orit_accession(s)']
},
})
record['rep_type(s)'] = bio_markers[0]['types']
record['rep_type_accession(s)'] = bio_markers[0]['acs']
record['relaxase_type(s)'] = bio_markers[1]['types']
record['relaxase_type_accession(s)'] = bio_markers[1]['acs']
record['mpf_type'] = bio_markers[2]['types']
record['mpf_type_accession(s)'] = bio_markers[2]['acs']
record['orit_type(s)'] = bio_markers[3]['types']
record['orit_accession(s)'] = bio_markers[3]['acs']
if (isinstance(record['mash_neighbor_distance'], float) or isinstance(
record['mash_neighbor_distance'],
int)) and record['mash_neighbor_distance'] <= primary_distance:
mob_cluster_id = record['primary_cluster_id']
else:
mob_cluster_id = None
#Patches that sometimes results are concatonated into strings if contigs are merged into a single results
if isinstance(record['rep_type(s)'], list):
record['rep_type(s)'] = ",".join(record['rep_type(s)'])
if isinstance(record['relaxase_type_accession(s)'], list):
record['relaxase_type_accession(s)'] = ",".join(
record['relaxase_type_accession(s)'])
host_range = hostrange(record['rep_type(s)'].split(','),
record['relaxase_type_accession(s)'].split(','),
mob_cluster_id, ncbi, lit)
for field in host_range:
record[field] = host_range[field]
if isinstance(record['mpf_type'], list):
record['mpf_type'] = determine_mpf_type(record['mpf_type'])
elif isinstance(record['mpf_type'], str):
record['mpf_type'] = determine_mpf_type(
record['mpf_type'].split(','))
for field in record:
if isinstance(record[field], list):
record[field] = ",".join(record[field])
record['predicted_mobility'] = 'non-mobilizable'
if len(record['relaxase_type(s)']) > 0 and len(record['mpf_type']):
record['predicted_mobility'] = 'conjugative'
elif len(record['relaxase_type(s)']) > 0 or len(
record['orit_type(s)']) > 0:
record['predicted_mobility'] = 'mobilizable'
mobtyper_results[i] = record
writeReport(mobtyper_results, MOB_TYPER_REPORT_HEADER, report_file)
if not keep_tmp:
shutil.rmtree(tmp_dir)
logger.info(
"MOB-typer completed and results written to {}".format(report_file))
def main():
args = parse_args()
if args.debug:
logging = init_console_logger(3)
else:
logging = init_console_logger(2)
logging.info(
'Running Mob-Suite Clustering toolkit v. {}'.format(__version__))
logging.info('Processing fasta file {}'.format(args.infile))
logging.info('Analysis directory {}'.format(args.outdir))
check_dependencies(logging)
input_fasta = args.infile
if not os.path.isfile(input_fasta):
logging.error('Error, input fasta specified does not exist: {}'.format(
input_fasta))
sys.exit()
mob_typer_report_file = args.mob_typer_file
if not os.path.isfile(mob_typer_report_file):
logging.error(
'Error, input metadata file specified does not exist: {}'.format(
mob_typer_report_file))
sys.exit()
mode = str(args.mode).lower()
if mode not in ('update', 'build'):
logging.error(
'Error you have not entered a valid mode of build or update, you entered: {}'
.format(mode))
sys.exit()
out_dir = args.outdir
num_threads = args.num_threads
if not (args.primary_cluster_dist >= 0 and args.primary_cluster_dist <= 1):
logging.error(
'Error distance thresholds must be between 0 - 1: {}'.format(
args.primary_cluster_dist))
sys.exit()
else:
primary_distance = args.primary_cluster_dist
if not (args.secondary_cluster_dist >= 0
and args.secondary_cluster_dist <= 1):
logging.error(
'Error distance thresholds must be between 0 - 1: {}'.format(
args.secondary_cluster_dist))
sys.exit()
else:
secondary_distance = args.secondary_cluster_dist
if not os.path.isdir(out_dir):
logging.info('Creating directory {}'.format(args.outdir))
os.mkdir(out_dir, 0o755)
tmp_dir = os.path.join(out_dir, '__tmp')
if not os.path.isdir(tmp_dir):
logging.info('Creating directory {}'.format(args.outdir))
os.mkdir(tmp_dir, 0o755)
taxonomy_file = args.taxonomy
records = read_file_to_dict(mob_typer_report_file,
MOB_TYPER_REPORT_HEADER,
separater="\t")
seq_ids = []
new_seq_info = {}
duplicate_keys = []
for record in records:
seq_ids.append(record['sample_id'])
if not record['sample_id'] in new_seq_info:
new_seq_info[record['sample_id']] = record
else:
duplicate_keys.append(record['sample_id'])
if len(duplicate_keys) > 0:
logging.error(
"Duplicate sequence identifiers in fasta file. Please make every sequence id unique in the input file before using this tool"
)
logging.error("Duplicate sequence ids: {}".format(
",".join(duplicate_keys)))
sys.exit()
record_identifications = read_file_to_dict(taxonomy_file,
['sample_id', 'organism'],
separater="\t")
organisms = []
for record in record_identifications:
organism = record['organism']
if organism == 'unknown' or organism == '' or organism == 'Unknown':
organism = 'Bacteria'
organisms.append(organism)
seq_id = record['sample_id']
if seq_id in new_seq_info:
new_seq_info[seq_id]['organism'] = organism
taxids = NamesToTaxIDs(organisms)
del (organisms)
for seq_id in new_seq_info:
organism = new_seq_info[seq_id]['organism']
if organism in taxids:
new_seq_info[seq_id]['taxid'] = taxids[organism][0]
else:
new_seq_info[seq_id]['taxid'] = 2
if len(new_seq_info) == 0:
logging.error(
'Error no MOB-typer results for sequences. Sequences must be typed with MOB-typer first'
)
sys.exit()
fasta_dict = read_fasta_dict(input_fasta)
if len(fasta_dict) == 0:
logging.error(
'Error no sequences found in input fasta: {}..cannot continue'.
format(input_fasta))
sys.exit()
key_set_1 = set(seq_ids)
key_set_2 = set(list(fasta_dict.keys()))
if len(list(key_set_1 ^ key_set_2)) > 0:
logging.error(
'Error MOB-typer results: {} and input fasta: {} do not have the same set of identifiers, these must match in order to proceed'
.format(mob_typer_report_file, input_fasta))
logging.error(
'Keys present in MOB-typer results: {} and not in input fasta: {} are: {}'
.format(mob_typer_report_file, input_fasta,
list(key_set_1 - key_set_2)))
logging.error(
'Keys present in MOB-typer results: {} and not in input fasta: {} are: {}'
.format(mob_typer_report_file, input_fasta,
list(key_set_2 - key_set_1)))
sys.exit()
tmp_cluster_file = os.path.join(out_dir, 'clusters.txt')
tmp_ref_fasta_file = os.path.join(tmp_dir, 'references_tmp.fasta')
update_fasta = os.path.join(out_dir, 'references_updated.fasta')
# Sketch and calculate distances within update sequences
if len(fasta_dict) > 1:
mashObj = mash()
mashObj.mashsketch(input_fasta,
input_fasta + ".msh",
num_threads=num_threads)
distance_matrix_file = os.path.join(tmp_dir, 'mash_dist_matrix.txt')
mashfile_handle = open(distance_matrix_file, 'w', encoding="utf-8")
mashfile_handle.write(
mashObj.run_mash(input_fasta + '.msh',
input_fasta + '.msh',
table=True,
num_threads=num_threads).decode())
mashfile_handle.close()
clust_assignments = build_cluster_db(
distance_matrix_file, (primary_distance, secondary_distance))
else:
seq_id = next(iter(fasta_dict))
clust_assignments = {seq_id: [0, 1]}
logging.info('Running MOB-cluster in {} mode'.format(mode))
if mode == 'update':
if args.ref_cluster_file is None:
logging.error(
'Reference fasta file must be specified, please check help for parameter reference'
)
sys.exit()
ref_fasta = args.ref_fasta_file
if not os.path.isfile(ref_fasta):
logging.error(
'Reference fasta file specified does not exist: {}'.format(
ref_fasta))
sys.exit()
if args.ref_cluster_file is None:
logging.error(
'Reference cluster file must be specified, please check help for parameter reference'
)
sys.exit()
ref_cluster_file = args.ref_cluster_file
if not os.path.isfile(ref_cluster_file):
logging.error(
'Reference cluster file specified does not exist: {}'.format(
ref_cluster_file))
sys.exit()
mob_cluster_seq_info = read_sequence_info(ref_cluster_file,
MOB_CLUSTER_INFO_HEADER)
logging.info(
'Running mob-cluster in update mode with input file: {}'.format(
input_fasta))
logging.info(
'Running mob-cluster in update mode with output directory: {}'.
format(out_dir))
logging.info(
'Running mob-cluster in update mode on reference fasta file: {}'.
format(ref_fasta))
logging.info(
'Reading previous cluster reference assignments from : {}'.format(
ref_cluster_file))
shutil.copy(ref_cluster_file, tmp_cluster_file)
shutil.copy(ref_fasta, tmp_ref_fasta_file)
logging.info('Creating new cluster assignments')
new_seq_info = update_existing_db(new_seq_info, mob_cluster_seq_info,
clust_assignments, primary_distance,
secondary_distance, num_threads)
cluster_assignments = {**mob_cluster_seq_info, **new_seq_info}
logging.info(
'Writting cluster assignments to : {}'.format(tmp_cluster_file))
writeClusterAssignments(tmp_cluster_file, MOB_CLUSTER_INFO_HEADER,
cluster_assignments)
shutil.copy(tmp_ref_fasta_file, os.path.join(out_dir, update_fasta))
else:
cluster_acs = convert_num_to_acs(clust_assignments)
for id in cluster_acs:
primary_key = cluster_acs[id][0]
secondary_key = cluster_acs[id][1]
new_seq_info[id]['primary_cluster_id'] = primary_key
new_seq_info[id]['primary_dist'] = primary_distance
new_seq_info[id]['secondary_cluster_id'] = secondary_key
new_seq_info[id]['secondary_dist'] = secondary_distance
writeClusterAssignments(tmp_cluster_file, MOB_CLUSTER_INFO_HEADER,
new_seq_info)
shutil.copy(input_fasta, update_fasta)
logging.info("Sketching new fasta {}".format(update_fasta))
mash_db_file = "{}.msh".format(update_fasta)
mObj = mash()
mObj.mashsketch(update_fasta, mash_db_file, num_threads=num_threads)
logging.info("Building blastdb {}".format(update_fasta))
blast_runner = BlastRunner(update_fasta, '')
blast_runner.makeblastdb(update_fasta, 'nucl', logging=logging)
logging.info("Removing temporary directory")
shutil.rmtree(tmp_dir)
logging.info(
"MOB-cluster completed, analysis results written to {}".format(
out_dir))
