def run():
args = parse_arguments()
message = '# CAT v{0}.'.format(about.__version__)
shared.give_user_feedback(message, args.log_file, args.quiet,
show_time=False)
# Check at which state to start.
step_list = []
if not args.proteins_fasta and not args.alignment_file:
message = (
'\n'
'BAT is running. Protein prediction, alignment, and bin '
'classification are carried out.')
shared.give_user_feedback(message, args.log_file, args.quiet,
show_time=False)
step_list.append('predict_proteins')
step_list.append('align')
elif args.proteins_fasta and not args.alignment_file:
message = (
'\n'
'BAT is running. Since a predicted protein fasta is supplied, '
'only alignment and bin classification are carried out.')
shared.give_user_feedback(message, args.log_file, args.quiet,
show_time=False)
step_list.append('align')
elif args.proteins_fasta and args.alignment_file:
message = (
'\n'
'BAT is running. Since a predicted protein fasta and '
'alignment file are supplied, only bin classification is '
'carried out.')
shared.give_user_feedback(message, args.log_file, args.quiet,
show_time=False)
elif not args.proteins_fasta and args.alignment_file:
message = (
'if you want BAT to directly classify a set of bins, you '
'should not only supply a DIAMOND alignment table but also a '
'concatenated predicted protein fasta file with argument '
'[-p / --proteins].')
shared.give_user_feedback(message, args.log_file, args.quiet,
error=True)
sys.exit(1)
step_list.append('classify')
# Print variables.
message = (
'Rarw!\n\n'
'Supplied command: {0}\n\n'
'Bin folder: {1}\n'
'Taxonomy folder: {2}\n'
'Database folder: {3}\n'
'Parameter r: {4}\n'
'Parameter f: {5}\n'
'Log file: {6}\n\n'
'-----------------\n'.format(
' '.join(sys.argv),
args.bin_folder,
args.taxonomy_folder,
args.database_folder,
int(args.r),
float(args.f),
args.log_file))
shared.give_user_feedback(message, args.log_file, args.quiet,
show_time=False)
# Check binaries, output files, taxonomy folder and database folder, and
# set variables.
message = 'Doing some pre-flight checks first.'
shared.give_user_feedback(message, args.log_file, args.quiet,
show_time=False)
errors = []
errors.append(
check.check_bin_folder(
args.bin_folder, args.bin_suffix, args.log_file, args.quiet))
errors.append(
check.check_out_prefix(args.out_prefix, args.log_file, args.quiet))
if 'predict_proteins' in step_list:
errors.append(
check.check_prodigal_binaries(
args.path_to_prodigal, args.log_file, args.quiet))
setattr(args,
'concatenated_fasta',
'{0}.concatenated.fasta'.format(args.out_prefix))
setattr(args,
'proteins_fasta',
'{0}.concatenated.predicted_proteins.faa'.format(
args.out_prefix))
setattr(args,
'proteins_gff',
'{0}.concatenated.predicted_proteins.gff'.format(
args.out_prefix))
if not args.force:
errors.append(
check.check_output_file(
args.concatenated_fasta, args.log_file, args.quiet))
errors.append(
check.check_output_file(
args.proteins_fasta, args.log_file, args.quiet))
errors.append(
check.check_output_file(
args.proteins_gff, args.log_file, args.quiet))
if 'align' in step_list:
errors.append(
check.check_diamond_binaries(
args.path_to_diamond, args.log_file, args.quiet))
setattr(args,
'alignment_file',
'{0}.concatenated.alignment.diamond'.format(args.out_prefix))
if not args.force:
errors.append(
check.check_output_file(
args.alignment_file, args.log_file, args.quiet))
errors.append(
check.check_folders_for_run(
args.taxonomy_folder,
args.nodes_dmp,
args.names_dmp,
args.database_folder,
args.diamond_database,
args.fastaid2LCAtaxid_file,
args.taxids_with_multiple_offspring_file,
step_list,
args.log_file,
args.quiet))
setattr(args,
'bin2classification_output_file',
'{0}.bin2classification.txt'.format(args.out_prefix))
setattr(args,
'ORF2LCA_output_file',
'{0}.ORF2LCA.txt'.format(args.out_prefix))
if not args.force:
errors.append(
check.check_output_file(
args.bin2classification_output_file,
args.log_file,
args.quiet))
errors.append(
check.check_output_file(
args.ORF2LCA_output_file, args.log_file, args.quiet))
if 'predict_proteins' not in step_list:
errors.append(
check.check_fasta(
args.proteins_fasta, args.log_file, args.quiet))
if 'align' in step_list:
errors.append(
check.check_top(args.top, args.r, args.log_file, args.quiet))
# Print all variables.
shared.print_variables(args, step_list)
if True in errors:
sys.exit(1)
message = 'Ready to fly!\n\n-----------------\n'
shared.give_user_feedback(message, args.log_file, args.quiet,
show_time=False)
# Start BAT.
(bin2contigs, contig_names) = import_bins(
args.bin_folder, args.bin_suffix, args.log_file, args.quiet)
if 'predict_proteins' in step_list:
make_concatenated_fasta(
args.concatenated_fasta,
bin2contigs,
args.bin_folder,
args.log_file,
args.quiet)
shared.run_prodigal(
args.path_to_prodigal,
args.concatenated_fasta,
args.proteins_fasta,
args.proteins_gff,
args.log_file,
args.quiet)
contig2ORFs = shared.import_ORFs(
args.proteins_fasta, args.log_file, args.quiet)
check.check_whether_ORFs_are_based_on_contigs(
contig_names, contig2ORFs, args.log_file, args.quiet)
if 'align' in step_list:
shared.run_diamond(args)
(ORF2hits,
all_hits) = shared.parse_tabular_alignment(
args.alignment_file,
args.one_minus_r,
args.log_file,
args.quiet)
(taxid2parent,
taxid2rank) = tax.import_nodes(
args.nodes_dmp, args.log_file, args.quiet)
fastaid2LCAtaxid = tax.import_fastaid2LCAtaxid(
args.fastaid2LCAtaxid_file, all_hits, args.log_file, args.quiet)
taxids_with_multiple_offspring = tax.import_taxids_with_multiple_offspring(
args.taxids_with_multiple_offspring_file,
args.log_file,
args.quiet)
message = 'BAT is flying! Files {0} and {1} are created.'.format(
args.bin2classification_output_file, args.ORF2LCA_output_file)
shared.give_user_feedback(message, args.log_file, args.quiet)
n_classified_bins = 0
with open(args.bin2classification_output_file, 'w') as outf1, open(args.ORF2LCA_output_file, 'w') as outf2:
outf1.write('# bin\tclassification\treason\tlineage\tlineage scores\n')
outf2.write('# ORF\tbin\tnumber of hits\tlineage\ttop bit-score\n')
for bin_ in sorted(bin2contigs):
LCAs_ORFs = []
for contig in sorted(bin2contigs[bin_]):
if contig not in contig2ORFs:
continue
for ORF in contig2ORFs[contig]:
if ORF not in ORF2hits:
outf2.write('{0}\t{1}\tORF has no hit to database\n'
''.format(ORF, bin_))
continue
n_hits = len(ORF2hits[ORF])
(taxid,
top_bitscore) = tax.find_LCA_for_ORF(
ORF2hits[ORF],
fastaid2LCAtaxid,
taxid2parent)
if taxid.startswith('no taxid found'):
outf2.write('{0}\t{1}\t{2}\t{3}\t{4}\n'.format(
ORF, bin_, n_hits, taxid, top_bitscore))
else:
lineage = tax.find_lineage(taxid, taxid2parent)
if not args.no_stars:
lineage = tax.star_lineage(
lineage, taxids_with_multiple_offspring)
outf2.write('{0}\t{1}\t{2}\t{3}\t{4}\n'.format(
ORF,
bin_,
n_hits,
';'.join(lineage[::-1]),
top_bitscore))
LCAs_ORFs.append((taxid, top_bitscore),)
if len(LCAs_ORFs) == 0:
outf1.write('{0}\tno taxid assigned\tno hits to database\n'
''.format(bin_))
continue
(lineages,
lineages_scores,
based_on_n_ORFs) = tax.find_weighted_LCA(
LCAs_ORFs, taxid2parent, args.f)
if lineages == 'no ORFs with taxids found.':
outf1.write('{0}\tno taxid assigned\t'
'hits not found in taxonomy files\n'.format(bin_))
continue
if lineages == 'no lineage whitelisted.':
outf1.write(
'{0}\tno taxid assigned\t'
'no lineage reached minimum bit-score support\n'
''.format(bin_))
continue
# The bin has a valid classification.
n_classified_bins += 1
total_n_ORFs = sum([len(contig2ORFs[contig]) for
contig in bin2contigs[bin_] if contig in contig2ORFs])
for (i, lineage) in enumerate(lineages):
if not args.no_stars:
lineage = tax.star_lineage(
lineage, taxids_with_multiple_offspring)
scores = ['{0:.2f}'.format(score) for
score in lineages_scores[i]]
if len(lineages) == 1:
# There is only one classification.
outf1.write(
'{0}\t'
'taxid assigned\t'
'based on {1}/{2} ORFs\t'
'{3}\t'
'{4}\n'.format(
bin_,
based_on_n_ORFs,
total_n_ORFs,
';'.join(lineage[::-1]),
';'.join(scores[::-1])))
else:
# There are multiple classifications.
outf1.write(
'{0}\t'
'taxid assigned ({1}/{2})\t'
'based on {3}/{4} ORFs\t'
'{5}\t'
'{6}\n'.format(
bin_,
i + 1,
len(lineages),
based_on_n_ORFs,
total_n_ORFs,
';'.join(lineage[::-1]),
';'.join(scores[::-1])))
message = ('\n-----------------\n\n'
'{0} BAT is done! {1:,d}/{2:,d} bins have taxonomy assigned.'
''.format(shared.timestamp(), n_classified_bins, len(bin2contigs)))
shared.give_user_feedback(message, args.log_file, args.quiet,
show_time=False)
if args.f < 0.5:
message = ('\nWARNING: since f is set to smaller than 0.5, one bin '
'may have multiple classifications.')
shared.give_user_feedback(message, args.log_file, args.quiet,
show_time=False)
return
def prepare(step_list, args):
shared.print_variables(args, step_list)
if not os.path.isdir(args.taxonomy_folder):
os.mkdir(args.taxonomy_folder)
message = 'Taxonomy folder {0} is created.'.format(
args.taxonomy_folder)
shared.give_user_feedback(message, args.log_file, args.quiet)
if not os.path.isdir(args.database_folder):
os.mkdir(args.database_folder)
message = 'Database folder {0} is created.'.format(
args.database_folder)
shared.give_user_feedback(message, args.log_file, args.quiet)
if 'download_taxonomy_files' in step_list:
download_taxonomy_files(args.taxonomy_folder, args.date, args.log_file,
args.quiet)
setattr(args, 'nodes_dmp', '{0}nodes.dmp'.format(args.taxonomy_folder))
if 'download_prot_accession2taxid_file' in step_list:
setattr(
args, 'prot_accession2taxid_file',
'{0}{1}.prot.accession2taxid.FULL.gz'.format(
args.taxonomy_folder, args.date))
download_prot_accession2taxid_file(args.prot_accession2taxid_file,
args.date, args.log_file,
args.quiet)
if 'download_nr' in step_list:
setattr(args, 'nr_file', '{0}{1}.nr.gz'.format(args.database_folder,
args.date))
download_nr(args.nr_file, args.log_file, args.quiet)
if 'make_diamond_database' in step_list:
setattr(args, 'diamond_database_prefix',
'{0}{1}.nr'.format(args.database_folder, args.date))
make_diamond_database(args.path_to_diamond, args.nr_file,
args.diamond_database_prefix, args.nproc,
args.log_file, args.quiet, args.verbose)
if ('make_fastaid2LCAtaxid_file' in step_list
or 'make_taxids_with_multiple_offspring_file' in step_list):
taxid2parent, taxid2rank = tax.import_nodes(args.nodes_dmp,
args.log_file, args.quiet)
if 'make_fastaid2LCAtaxid_file' in step_list:
setattr(
args, 'fastaid2LCAtaxid_file',
'{0}{1}.nr.fastaid2LCAtaxid'.format(args.database_folder,
args.date))
make_fastaid2LCAtaxid_file(args.nodes_dmp, args.fastaid2LCAtaxid_file,
args.nr_file,
args.prot_accession2taxid_file,
taxid2parent, args.log_file, args.quiet)
if 'make_taxids_with_multiple_offspring_file' in step_list:
setattr(
args, 'taxids_with_multiple_offspring_file',
'{0}{1}.nr.taxids_with_multiple_offspring'.format(
args.database_folder, args.date))
taxid2offspring = find_offspring(args.nodes_dmp,
args.fastaid2LCAtaxid_file,
taxid2parent, args.log_file,
args.quiet)
write_taxids_with_multiple_offspring_file(
args.taxids_with_multiple_offspring_file, taxid2offspring,
args.log_file, args.quiet)
message = ('\n-----------------\n\n'
'{0} CAT prepare is done!'.format(shared.timestamp()))
shared.give_user_feedback(message,
args.log_file,
args.quiet,
show_time=False)
if args.nr_file:
message = 'You may remove {0} now.'.format(args.nr_file)
shared.give_user_feedback(message,
args.log_file,
args.quiet,
show_time=False)
message = ('\nSupply the following arguments to CAT or BAT if you want to '
'use this database:\n'
'-d / --database_folder {0}\n'
'-t / --taxonomy_folder {1}'.format(args.database_folder,
args.taxonomy_folder))
shared.give_user_feedback(message,
args.log_file,
args.quiet,
show_time=False)
return