def genome_domain(self, identity_dir, prefix):
"""Determine domain of User genomes based on identified marker genes."""
bac_count = defaultdict(int)
ar_count = defaultdict(int)
# Load the marker files for each domain
ar53_marker_file = CopyNumberFileAR53(identity_dir, prefix)
ar53_marker_file.read()
bac120_marker_file = CopyNumberFileBAC120(identity_dir, prefix)
bac120_marker_file.read()
# Get the number of single copy markers for each domain
for out_d, marker_summary in ((ar_count, ar53_marker_file),
(bac_count, bac120_marker_file)):
for genome_id in marker_summary.genomes:
out_d[genome_id] = len(
marker_summary.get_single_copy_hits(genome_id))
bac_gids = set()
ar_gids = set()
bac_ar_diff = {}
for gid in bac_count:
arc_aa_per = (ar_count[gid] * 100.0 / Config.AR_MARKER_COUNT)
bac_aa_per = (bac_count[gid] * 100.0 / Config.BAC_MARKER_COUNT)
if bac_aa_per >= arc_aa_per:
bac_gids.add(gid)
else:
ar_gids.add(gid)
if abs(bac_aa_per - arc_aa_per) <= 10:
bac_ar_diff[gid] = {
'bac120': round(bac_aa_per, 1),
'ar53': round(arc_aa_per, 1)
}
return bac_gids, ar_gids, bac_ar_diff
def _report_identified_marker_genes(self, gene_dict, outdir, prefix):
"""Report statistics for identified marker genes."""
# Summarise the copy number of each AR122 and BAC120 markers.
tln_summary_file = TlnTableSummaryFile(outdir, prefix)
ar122_copy_number_file = CopyNumberFileAR122(outdir, prefix)
bac120_copy_number_file = CopyNumberFileBAC120(outdir, prefix)
# Process each genome.
for db_genome_id, info in sorted(gene_dict.items()):
cur_marker_dir = os.path.join(outdir, DIR_MARKER_GENE)
pfam_tophit_file = TopHitPfamFile(cur_marker_dir, db_genome_id)
tigr_tophit_file = TopHitTigrFile(cur_marker_dir, db_genome_id)
pfam_tophit_file.read()
tigr_tophit_file.read()
# Summarise each of the markers for this genome.
ar122_copy_number_file.add_genome(db_genome_id,
info.get("aa_gene_path"),
pfam_tophit_file,
tigr_tophit_file)
bac120_copy_number_file.add_genome(db_genome_id,
info.get("aa_gene_path"),
pfam_tophit_file,
tigr_tophit_file)
# Write the best translation table to disk for this genome.
tln_summary_file.add_genome(db_genome_id,
info.get("best_translation_table"))
# Write each of the summary files to disk.
ar122_copy_number_file.write()
bac120_copy_number_file.write()
tln_summary_file.write()
# Create a symlink to store the summary files in the root.
symlink_f(
PATH_BAC120_MARKER_SUMMARY.format(prefix=prefix),
os.path.join(
outdir,
os.path.basename(
PATH_BAC120_MARKER_SUMMARY.format(prefix=prefix))))
symlink_f(
PATH_AR122_MARKER_SUMMARY.format(prefix=prefix),
os.path.join(
outdir,
os.path.basename(
PATH_AR122_MARKER_SUMMARY.format(prefix=prefix))))
symlink_f(
PATH_TLN_TABLE_SUMMARY.format(prefix=prefix),
os.path.join(
outdir,
os.path.basename(
PATH_TLN_TABLE_SUMMARY.format(prefix=prefix))))
def _report_identified_marker_genes(self, gene_dict, outdir, prefix,
write_single_copy_genes):
"""Report statistics for identified marker genes."""
# Summarise the copy number of each AR53 and BAC120 markers.
tln_summary_file = TlnTableSummaryFile(outdir, prefix)
ar53_copy_number_file = CopyNumberFileAR53(outdir, prefix)
bac120_copy_number_file = CopyNumberFileBAC120(outdir, prefix)
# Process each genome.
for db_genome_id, info in tqdm_log(sorted(gene_dict.items()),
unit='genome'):
cur_marker_dir = os.path.join(outdir, DIR_MARKER_GENE)
pfam_tophit_file = TopHitPfamFile(cur_marker_dir, db_genome_id)
tigr_tophit_file = TopHitTigrFile(cur_marker_dir, db_genome_id)
pfam_tophit_file.read()
tigr_tophit_file.read()
# Summarise each of the markers for this genome.
ar53_copy_number_file.add_genome(db_genome_id,
info.get("aa_gene_path"),
pfam_tophit_file,
tigr_tophit_file)
bac120_copy_number_file.add_genome(db_genome_id,
info.get("aa_gene_path"),
pfam_tophit_file,
tigr_tophit_file)
# Write the best translation table to disk for this genome.
tln_summary_file.add_genome(db_genome_id,
info.get("best_translation_table"))
# Write each of the summary files to disk.
ar53_copy_number_file.write()
bac120_copy_number_file.write()
tln_summary_file.write()
# Create a symlink to store the summary files in the root.
# symlink_f(PATH_BAC120_MARKER_SUMMARY.format(prefix=prefix),
# os.path.join(outdir, os.path.basename(PATH_BAC120_MARKER_SUMMARY.format(prefix=prefix))))
# symlink_f(PATH_AR53_MARKER_SUMMARY.format(prefix=prefix),
# os.path.join(outdir, os.path.basename(PATH_AR53_MARKER_SUMMARY.format(prefix=prefix))))
# symlink_f(PATH_TLN_TABLE_SUMMARY.format(prefix=prefix),
# os.path.join(outdir, os.path.basename(PATH_TLN_TABLE_SUMMARY.format(prefix=prefix))))
symlink_f(
PATH_FAILS.format(prefix=prefix),
os.path.join(outdir,
os.path.basename(PATH_FAILS.format(prefix=prefix))))
# Write the single copy AR53/BAC120 FASTA files to disk.
if write_single_copy_genes:
fasta_dir = os.path.join(outdir, DIR_IDENTIFY_FASTA)
self.logger.info(
f'Writing unaligned single-copy genes to: {fasta_dir}')
# Iterate over each domain.
marker_doms = list()
marker_doms.append(
(Config.AR53_MARKERS['PFAM'] + Config.AR53_MARKERS['TIGRFAM'],
ar53_copy_number_file, 'ar53'))
marker_doms.append((Config.BAC120_MARKERS['PFAM'] +
Config.BAC120_MARKERS['TIGRFAM'],
bac120_copy_number_file, 'bac120'))
for marker_names, marker_file, marker_d in marker_doms:
# Create the domain-specific subdirectory.
fasta_d_dir = os.path.join(fasta_dir, marker_d)
make_sure_path_exists(fasta_d_dir)
# Iterate over each marker.
for marker_name in marker_names:
marker_name = marker_name.rstrip(r'\.[HMMhmm]')
marker_path = os.path.join(fasta_d_dir,
f'{marker_name}.fa')
to_write = list()
for genome_id in sorted(gene_dict):
unq_hits = marker_file.get_single_copy_hits(genome_id)
if marker_name in unq_hits:
to_write.append(f'>{genome_id}')
to_write.append(unq_hits[marker_name]['seq'])
if len(to_write) > 0:
with open(marker_path, 'w') as fh:
fh.write('\n'.join(to_write))
def align(self,
identify_dir,
skip_gtdb_refs,
taxa_filter,
min_perc_aa,
custom_msa_filters,
skip_trimming,
rnd_seed,
cols_per_gene,
min_consensus,
max_consensus,
min_per_taxa,
out_dir,
prefix,
outgroup_taxon,
genomes_to_process=None):
"""Align marker genes in genomes."""
# read genomes that failed identify steps to skip them
failed_genomes_file = os.path.join(
os.path.join(identify_dir, PATH_FAILS.format(prefix=prefix)))
if os.path.isfile(failed_genomes_file):
with open(failed_genomes_file) as fgf:
failed_genomes = [row.split()[0] for row in fgf]
else:
failed_genomes = list()
# If the user is re-running this step, check if the identify step is consistent.
genomic_files = self._path_to_identify_data(identify_dir,
identify_dir != out_dir)
if genomes_to_process is not None and len(genomic_files) != len(
genomes_to_process):
if list(
set(genomic_files.keys()) - set(genomes_to_process.keys())
).sort() != failed_genomes.sort():
self.logger.error(
'{} are not present in the input list of genome to process.'
.format(
list(
set(genomic_files.keys()) -
set(genomes_to_process.keys()))))
raise InconsistentGenomeBatch(
'You are attempting to run GTDB-Tk on a non-empty directory that contains extra '
'genomes not present in your initial identify directory. Remove them, or run '
'GTDB-Tk on a new directory.')
# If this is being run as a part of classify_wf, copy the required files.
if identify_dir != out_dir:
identify_path = os.path.join(out_dir, DIR_IDENTIFY)
make_sure_path_exists(identify_path)
copy(
CopyNumberFileBAC120(identify_dir, prefix).path, identify_path)
copy(CopyNumberFileAR53(identify_dir, prefix).path, identify_path)
copy(TlnTableSummaryFile(identify_dir, prefix).path, identify_path)
# Create the align intermediate directory.
make_sure_path_exists(os.path.join(out_dir, DIR_ALIGN_INTERMEDIATE))
# Write out files with marker information
ar53_marker_info_file = MarkerInfoFileAR53(out_dir, prefix)
ar53_marker_info_file.write()
bac120_marker_info_file = MarkerInfoFileBAC120(out_dir, prefix)
bac120_marker_info_file.write()
# Determine what domain each genome belongs to.
bac_gids, ar_gids, _bac_ar_diff = self.genome_domain(
identify_dir, prefix)
if len(bac_gids) + len(ar_gids) == 0:
raise GTDBTkExit(f'Unable to assign a domain to any genomes, '
f'please check the identify marker summary file, '
f'and verify genome quality.')
# # Create a temporary directory that will be used to generate each of the alignments.
# with tempfile.TemporaryDirectory(prefix='gtdbtk_tmp_') as dir_tmp_arc, \
# tempfile.TemporaryDirectory(prefix='gtdbtk_tmp_') as dir_tmp_bac:
#
# cur_gid_dict = {x: genomic_files[x] for x in ar_gids}
# self.logger.info(f'Collecting marker sequences from {len(cur_gid_dict):,} '
# f'genomes identified as archaeal.')
# align.concat_single_copy_hits(dir_tmp_arc,
# cur_gid_dict,
# ar53_marker_info_file)
#
self.logger.info(
f'Aligning markers in {len(genomic_files):,} genomes with {self.cpus} CPUs.'
)
dom_iter = ((bac_gids, Config.CONCAT_BAC120, Config.MASK_BAC120,
"bac120", 'bacterial', CopyNumberFileBAC120),
(ar_gids, Config.CONCAT_AR53, Config.MASK_AR53, "ar53",
'archaeal', CopyNumberFileAR53))
gtdb_taxonomy = Taxonomy().read(self.taxonomy_file)
for gids, msa_file, mask_file, marker_set_id, domain_str, copy_number_f in dom_iter:
# No genomes identified as this domain.
if len(gids) == 0:
continue
self.logger.info(
f'Processing {len(gids):,} genomes identified as {domain_str}.'
)
if marker_set_id == 'bac120':
marker_info_file = bac120_marker_info_file
marker_filtered_genomes = os.path.join(
out_dir,
PATH_BAC120_FILTERED_GENOMES.format(prefix=prefix))
marker_msa_path = os.path.join(
out_dir, PATH_BAC120_MSA.format(prefix=prefix))
marker_user_msa_path = os.path.join(
out_dir, PATH_BAC120_USER_MSA.format(prefix=prefix))
else:
marker_info_file = ar53_marker_info_file
marker_filtered_genomes = os.path.join(
out_dir, PATH_AR53_FILTERED_GENOMES.format(prefix=prefix))
marker_msa_path = os.path.join(
out_dir, PATH_AR53_MSA.format(prefix=prefix))
marker_user_msa_path = os.path.join(
out_dir, PATH_AR53_USER_MSA.format(prefix=prefix))
cur_genome_files = {
gid: f
for gid, f in genomic_files.items() if gid in gids
}
if skip_gtdb_refs:
gtdb_msa = {}
else:
gtdb_msa = self._msa_filter_by_taxa(msa_file, gtdb_taxonomy,
taxa_filter,
outgroup_taxon)
gtdb_msa_mask = os.path.join(Config.MASK_DIR, mask_file)
# Generate the user MSA.
user_msa = align.align_marker_set(cur_genome_files,
marker_info_file, copy_number_f,
self.cpus)
if len(user_msa) == 0:
self.logger.warning(
f'Identified {len(user_msa):,} single copy {domain_str} hits.'
)
continue
# Write the individual marker alignments to disk
if self.debug:
self._write_individual_markers(user_msa, marker_set_id,
marker_info_file.path, out_dir,
prefix)
# filter columns without sufficient representation across taxa
if skip_trimming:
self.logger.info(
'Skipping custom filtering and selection of columns.')
pruned_seqs = {}
trimmed_seqs = merge_two_dicts(gtdb_msa, user_msa)
elif custom_msa_filters:
aligned_genomes = merge_two_dicts(gtdb_msa, user_msa)
self.logger.info(
'Performing custom filtering and selection of columns.')
trim_msa = TrimMSA(
cols_per_gene, min_perc_aa / 100.0, min_consensus / 100.0,
max_consensus / 100.0, min_per_taxa / 100.0, rnd_seed,
os.path.join(out_dir, f'filter_{marker_set_id}'))
trimmed_seqs, pruned_seqs = trim_msa.trim(
aligned_genomes, marker_info_file.path)
if trimmed_seqs:
self.logger.info(
'Filtered MSA from {:,} to {:,} AAs.'.format(
len(list(aligned_genomes.values())[0]),
len(list(trimmed_seqs.values())[0])))
self.logger.info(
'Filtered {:,} genomes with amino acids in <{:.1f}% of columns in filtered MSA.'
.format(len(pruned_seqs), min_perc_aa))
filtered_user_genomes = set(pruned_seqs).intersection(user_msa)
if len(filtered_user_genomes):
self.logger.info(
f'Filtered genomes include {len(filtered_user_genomes)} user submitted genomes.'
)
else:
self.logger.log(
Config.LOG_TASK,
f'Masking columns of {domain_str} multiple sequence alignment using canonical mask.'
)
trimmed_seqs, pruned_seqs = self._apply_mask(
gtdb_msa, user_msa, gtdb_msa_mask, min_perc_aa / 100.0)
self.logger.info(
'Masked {} alignment from {:,} to {:,} AAs.'.format(
domain_str, len(list(user_msa.values())[0]),
len(list(trimmed_seqs.values())[0])))
if min_perc_aa > 0:
self.logger.info(
'{:,} {} user genomes have amino acids in <{:.1f}% of columns in filtered MSA.'
.format(len(pruned_seqs), domain_str, min_perc_aa))
# write out filtering information
with open(marker_filtered_genomes, 'w') as fout:
for pruned_seq_id, pruned_seq in pruned_seqs.items():
if len(pruned_seq) == 0:
perc_alignment = 0
else:
valid_bases = sum(
[1 for c in pruned_seq if c.isalpha()])
perc_alignment = valid_bases * 100.0 / len(pruned_seq)
fout.write(
f'{pruned_seq_id}\tInsufficient number of amino acids in MSA ({perc_alignment:.1f}%)\n'
)
# write out MSAs
if not skip_gtdb_refs:
self.logger.info(
f'Creating concatenated alignment for {len(trimmed_seqs):,} '
f'{domain_str} GTDB and user genomes.')
self._write_msa(trimmed_seqs,
marker_msa_path,
gtdb_taxonomy,
zip_output=True)
trimmed_user_msa = {
k: v
for k, v in trimmed_seqs.items() if k in user_msa
}
if len(trimmed_user_msa) > 0:
self.logger.info(
f'Creating concatenated alignment for {len(trimmed_user_msa):,} '
f'{domain_str} user genomes.')
self._write_msa(trimmed_user_msa,
marker_user_msa_path,
gtdb_taxonomy,
zip_output=True)
else:
self.logger.info(
f'All {domain_str} user genomes have been filtered out.')
def _run_multi_align(self, db_genome_id, path, marker_set_id):
"""
Returns the concatenated marker sequence for a specific genome
:param db_genome_id: Selected genome
:param path: Path to the genomic fasta file for the genome
:param marker_set_id: Unique ID of marker set to use for alignment
"""
cur_marker_dir = os.path.dirname(os.path.dirname(path))
pfam_tophit_file = TopHitPfamFile(cur_marker_dir, db_genome_id)
tigr_tophit_file = TopHitTigrFile(cur_marker_dir, db_genome_id)
pfam_tophit_file.read()
tigr_tophit_file.read()
if marker_set_id == 'bac120':
copy_number_file = CopyNumberFileBAC120('/dev/null', None)
elif marker_set_id == 'ar122':
copy_number_file = CopyNumberFileAR122('/dev/null', None)
else:
raise GTDBTkException('Unknown marker set.')
copy_number_file.add_genome(db_genome_id, path, pfam_tophit_file,
tigr_tophit_file)
single_copy_hits = copy_number_file.get_single_copy_hits(db_genome_id)
# gather information for all marker genes
marker_paths = {
"PFAM":
os.path.join(self.pfam_hmm_dir, 'individual_hmms'),
"TIGRFAM":
os.path.join(os.path.dirname(self.tigrfam_hmm_dir),
'individual_hmms')
}
marker_dict_original = {}
if marker_set_id == "bac120":
for db_marker in sorted(self.bac120_markers):
marker_dict_original.update({
marker.replace(".HMM", "").replace(".hmm", ""):
os.path.join(marker_paths[db_marker], marker)
for marker in self.bac120_markers[db_marker]
})
elif marker_set_id == "ar122":
for db_marker in sorted(self.ar122_markers):
marker_dict_original.update({
marker.replace(".HMM", "").replace(".hmm", ""):
os.path.join(marker_paths[db_marker], marker)
for marker in self.ar122_markers[db_marker]
})
elif marker_set_id == "rps23":
for db_marker in sorted(self.rps23_markers):
marker_dict_original.update({
marker.replace(".HMM", "").replace(".hmm", ""):
os.path.join(marker_paths[db_marker], marker)
for marker in self.rps23_markers[db_marker]
})
# Iterate over each of the expected markers and store the gene sequence.
gene_dict = dict()
result_align = dict()
for marker_id, marker_path in marker_dict_original.items():
hit = single_copy_hits.get(marker_id)
if hit:
# print(marker_id)
gene_dict[marker_id] = {
"marker_path": marker_path,
"gene": hit['hit'].gene_id,
"gene_seq": hit['seq'],
"bitscore": hit['hit'].bit_score
}
else:
hmm_len = self._get_hmm_size(marker_path)
result_align[marker_id] = '-' * hmm_len
# Align the markers.
result_align.update(self._run_align(gene_dict, db_genome_id))
# we concatenate the aligned markers together and associate them with
# the genome.
return ''.join([x[1] for x in sorted(result_align.items())])
def setUp(self):
self.dir_tmp = tempfile.mkdtemp(prefix='gtdbtk_tmp_')
self.cn = CopyNumberFileBAC120(self.dir_tmp, 'tst')