def run(self, aa_gene_files, evalue, per_identity, output_dir):
"""Apply reciprocal blast to all pairs of genomes in parallel.
Parameters
----------
aa_gene_files : list of str
Amino acid fasta files to process via reciprocal blast.
evalue : float
E-value threshold for reporting hits.
per_identity : float
Percent identity threshold for reporting hits.
output_dir : str
Directory to store blast results.
"""
# concatenate all gene files and create a single diamond database
self.logger.info(' Creating diamond database (be patient!).')
gene_file = os.path.join(output_dir, 'all_genes.faa')
concatenate_files(aa_gene_files, gene_file)
diamond_db = os.path.join(output_dir, 'all_genes')
diamond = Diamond(self.cpus)
diamond.make_database(gene_file, diamond_db)
# blast all genes against the database
self.logger.info('')
self.logger.info(' Identifying hits between all pairs of genomes (be patient!).')
hits_daa_file = os.path.join(output_dir, 'all_hits')
diamond.blastp(gene_file, diamond_db, evalue, per_identity, len(aa_gene_files) * 10, hits_daa_file)
# create flat hits table
self.logger.info(' Creating table with hits.')
hits_table_file = os.path.join(output_dir, 'all_hits.tsv')
diamond.view(hits_daa_file + '.daa', hits_table_file)
def run(self, scaffold_gene_file, stat_file, ref_genome_gene_files, db_file, evalue, per_identity, per_aln_len):
"""Create taxonomic profiles for a set of genomes.
Parameters
----------
scaffold_gene_file : str
Fasta file of genes on scaffolds in amino acid space.
stat_file : str
File with statistics for individual scaffolds.
ref_genome_gene_files : list of str
Fasta files of called genes on reference genomes of interest.
db_file : str
Database of competing reference genes.
evalue : float
E-value threshold of valid hits.
per_identity : float
Percent identity threshold of valid hits [0,100].
per_aln_len : float
Percent query coverage of valid hits [0, 100].
"""
# read statistics file
self.logger.info('Reading scaffold statistics.')
scaffold_stats = ScaffoldStats()
scaffold_stats.read(stat_file)
# perform homology searches
self.logger.info('Creating DIAMOND database for reference genomes.')
ref_gene_file = os.path.join(self.output_dir, 'ref_genes.faa')
concatenate_gene_files(ref_genome_gene_files, ref_gene_file)
diamond = Diamond(self.cpus)
ref_diamond_db = os.path.join(self.output_dir, 'ref_genes')
diamond.create_db(ref_gene_file, ref_diamond_db)
self.logger.info('Identifying homologs within reference genomes of interest (be patient!).')
self.diamond_dir = os.path.join(self.output_dir, 'diamond')
make_sure_path_exists(self.diamond_dir)
hits_ref_genomes = os.path.join(self.diamond_dir, 'ref_hits.tsv')
diamond.blastp(scaffold_gene_file, ref_diamond_db, evalue, per_identity, per_aln_len, 1, False, hits_ref_genomes)
self.logger.info('Identifying homologs within competing reference genomes (be patient!).')
hits_comp_ref_genomes = os.path.join(self.diamond_dir, 'competing_ref_hits.tsv')
diamond.blastp(scaffold_gene_file, db_file, evalue, per_identity, per_aln_len, 1, False, hits_comp_ref_genomes)
# get list of genes with a top hit to the reference genomes of interest
hits_to_ref = self._top_hits_to_reference(hits_ref_genomes, hits_comp_ref_genomes)
# get number of genes on each scaffold
num_genes_on_scaffold = defaultdict(int)
for seq_id, _seq in seq_io.read_seq(scaffold_gene_file):
scaffold_id = seq_id[0:seq_id.rfind('_')]
num_genes_on_scaffold[scaffold_id] += 1
# get hits to each scaffold
hits_to_scaffold = defaultdict(list)
for query_id, hit in hits_to_ref.items():
gene_id = query_id[0:query_id.rfind('~')]
scaffold_id = gene_id[0:gene_id.rfind('_')]
hits_to_scaffold[scaffold_id].append(hit)
# report summary stats for each scaffold
reference_out = os.path.join(self.output_dir, 'references.tsv')
fout = open(reference_out, 'w')
fout.write('Scaffold ID\tSubject genome IDs\tSubject scaffold IDs')
fout.write('\tGenome ID\tLength (bp)\tGC\tMean coverage')
fout.write('\t# genes\t# hits\t% genes\tAvg. align. length (bp)\tAvg. % identity\tAvg. e-value\tAvg. bitscore\n')
for scaffold_id, hits in hits_to_scaffold.items():
aln_len = []
perc_iden = []
evalue = []
bitscore = []
subject_scaffold_ids = defaultdict(int)
subject_bin_ids = defaultdict(int)
for hit in hits:
aln_len.append(hit.aln_length)
perc_iden.append(hit.perc_identity)
evalue.append(hit.evalue)
bitscore.append(hit.bitscore)
subject_bin_id, subject_gene_id = hit.subject_id.split('~')
subject_scaffold_id = subject_gene_id[0:subject_gene_id.rfind('_')]
subject_scaffold_ids[subject_scaffold_id] += 1
subject_bin_ids[subject_bin_id] += 1
sorted_subject_bin_ids = sorted(subject_bin_ids.items(),
key=operator.itemgetter(1),
reverse=True)
subject_bin_id_str = []
for bin_id, num_hits in sorted_subject_bin_ids:
subject_bin_id_str.append(bin_id + ':' + str(num_hits))
subject_bin_id_str = ','.join(subject_bin_id_str)
sorted_subject_scaffold_ids = sorted(subject_scaffold_ids.items(),
key=operator.itemgetter(1),
reverse=True)
subject_scaffold_id_str = []
for subject_id, num_hits in sorted_subject_scaffold_ids:
subject_scaffold_id_str.append(subject_id + ':' + str(num_hits))
subject_scaffold_id_str = ','.join(subject_scaffold_id_str)
fout.write('%s\t%s\t%s\t%s\t%.2f\t%d\t%d\t%.2f\t%d\t%.2f\t%.2g\t%.2f\n' % (
scaffold_id,
subject_bin_id_str,
subject_scaffold_id_str,
scaffold_stats.print_stats(scaffold_id),
mean(scaffold_stats.coverage(scaffold_id)),
num_genes_on_scaffold[scaffold_id],
len(hits),
len(hits) * 100.0 / num_genes_on_scaffold[scaffold_id],
mean(aln_len),
mean(perc_iden),
mean(evalue),
mean(bitscore)))
fout.close()
return reference_out
def _run_reciprocal_diamond(self, query_gene_file, target_gene_file,
evalue, per_identity, per_aln_len, max_hits,
sensitive, high_mem, tmp_dir, output_dir):
"""Perform similarity search of query genes against target genes, and reciprocal hits.
Parameters
----------
query_gene_file : str
File with all query proteins.
target_gene_file : str
File with all target proteins.
evalue : float
E-value threshold for reporting hits.
per_identity : float
Percent identity threshold for reporting hits.
per_aln_len : float
Percent query coverage threshold for reporting hits.
max_hits : int
Maximum number of hits to report per query sequences.
tmp_dir : str
Directory to store temporary files.
output_dir : str
Directory to store blast results.
"""
self.logger.info(
'Creating DIAMOND database of query proteins (be patient!).')
diamond = Diamond(self.cpus)
query_diamond_db = os.path.join(output_dir, 'query_genes')
diamond.create_db(query_gene_file, query_diamond_db)
self.logger.info(
'Creating DIAMOND database of target proteins (be patient!).')
target_diamond_db = os.path.join(output_dir, 'target_genes')
diamond.create_db(target_gene_file, target_diamond_db)
# blast query genes against target proteins
self.logger.info(
'Performing similarity sequence between query and target proteins (be patient!).'
)
if tmp_dir:
tmp_query_hits_table = tempfile.NamedTemporaryFile(
prefix='comparem_hits_', dir=tmp_dir, delete=False)
else:
tmp_query_hits_table = tempfile.NamedTemporaryFile(
prefix='comparem_hits_', delete=False)
tmp_query_hits_table.close()
query_hits_daa_file = os.path.join(output_dir, 'query_hits')
if high_mem:
diamond.blastp(query_gene_file,
target_diamond_db,
evalue,
per_identity,
per_aln_len,
max_hits,
sensitive,
tmp_query_hits_table.name,
'standard',
tmp_dir,
chunk_size=1,
block_size=8)
else:
diamond.blastp(query_gene_file, target_diamond_db, evalue,
per_identity, per_aln_len, max_hits, sensitive,
tmp_query_hits_table.name, 'standard', tmp_dir)
# get target genes hit by one or more query proteins
self.logger.info(
'Creating file with target proteins with similarity to query proteins.'
)
target_hit = set()
for line in open(tmp_query_hits_table.name):
line_split = line.split('\t')
target_hit.add(line_split[1])
target_genes_hits = os.path.join(output_dir, 'target_genes_hit.faa')
fout = open(target_genes_hits, 'w')
for seq_id, seq in seq_io.read_seq(target_gene_file):
if seq_id in target_hit:
fout.write('>' + seq_id + '\n')
fout.write(seq + '\n')
fout.close()
self.logger.info(
'Identified %d target proteins to be used in reciprocal search.' %
len(target_hit))
# perform reciprocal blast
self.logger.info(
'Performing reciprocal similarity sequence between target and query proteins (be patient!).'
)
if tmp_dir:
tmp_target_hits_table = tempfile.NamedTemporaryFile(
prefix='comparem_hits_', dir=tmp_dir, delete=False)
else:
tmp_target_hits_table = tempfile.NamedTemporaryFile(
prefix='comparem_hits_', delete=False)
tmp_target_hits_table.close()
if high_mem:
diamond.blastp(target_genes_hits,
query_diamond_db,
evalue,
per_identity,
per_aln_len,
max_hits,
sensitive,
tmp_target_hits_table.name,
'standard',
tmp_dir,
chunk_size=1,
block_size=8)
else:
diamond.blastp(target_genes_hits, query_diamond_db, evalue,
per_identity, per_aln_len, max_hits, sensitive,
tmp_target_hits_table.name, 'standard', tmp_dir)
# combine hit tables and sort
os.system('cat %s >> %s' %
(tmp_target_hits_table.name, tmp_query_hits_table.name))
os.remove(tmp_target_hits_table.name)
hits_table_file = os.path.join(output_dir, 'hits_sorted.tsv')
self._sort_hit_table(tmp_query_hits_table.name, hits_table_file)
def _run_self_diamond(self, query_gene_file, evalue, per_identity,
per_aln_len, max_hits, sensitive, high_mem, tmp_dir,
output_dir):
"""Perform similarity search of query genes against themselves.
Parameters
----------
query_gene_file : str
File with all query sequences.
evalue : float
E-value threshold for reporting hits.
per_identity : float
Percent identity threshold for reporting hits.
per_aln_len : float
Percent query coverage threshold for reporting hits.
max_hits : int
Maximum number of hits to report per query sequences.
tmp_dir : str
Directory to store temporary files.
output_dir : str
Directory to store blast results.
"""
self.logger.info('Creating DIAMOND database (be patient!).')
diamond_db = os.path.join(output_dir, 'query_genes')
diamond = Diamond(self.cpus)
diamond.create_db(query_gene_file, diamond_db)
# create flat hits table
if tmp_dir:
tmp_hits_table = tempfile.NamedTemporaryFile(
prefix='comparem_hits_', dir=tmp_dir, delete=False)
else:
tmp_hits_table = tempfile.NamedTemporaryFile(
prefix='comparem_hits_', delete=False)
tmp_hits_table.close()
# blast all genes against the database
self.logger.info(
'Performing self similarity sequence between genomes (be patient!).'
)
if high_mem:
diamond.blastp(query_gene_file,
diamond_db,
evalue,
per_identity,
per_aln_len,
max_hits,
sensitive,
tmp_hits_table.name,
'standard',
tmp_dir,
chunk_size=1,
block_size=8)
else:
diamond.blastp(query_gene_file, diamond_db, evalue, per_identity,
per_aln_len, max_hits, sensitive,
tmp_hits_table.name, 'standard', tmp_dir)
# sort hit table
hits_table_file = os.path.join(output_dir, 'hits_sorted.tsv')
self._sort_hit_table(tmp_hits_table.name, hits_table_file)
def _run_reciprocal_diamond(self, query_gene_file,
target_gene_file,
evalue,
per_identity,
per_aln_len,
max_hits,
sensitive,
high_mem,
tmp_dir,
output_dir):
"""Perform similarity search of query genes against target genes, and reciprocal hits.
Parameters
----------
query_gene_file : str
File with all query proteins.
target_gene_file : str
File with all target proteins.
evalue : float
E-value threshold for reporting hits.
per_identity : float
Percent identity threshold for reporting hits.
per_aln_len : float
Percent query coverage threshold for reporting hits.
max_hits : int
Maximum number of hits to report per query sequences.
tmp_dir : str
Directory to store temporary files.
output_dir : str
Directory to store blast results.
"""
self.logger.info('Creating DIAMOND database of query proteins (be patient!).')
diamond = Diamond(self.cpus)
query_diamond_db = os.path.join(output_dir, 'query_genes')
diamond.make_database(query_gene_file, query_diamond_db)
self.logger.info('Creating DIAMOND database of target proteins (be patient!).')
target_diamond_db = os.path.join(output_dir, 'target_genes')
diamond.make_database(target_gene_file, target_diamond_db)
# blast query genes against target proteins
self.logger.info('Performing similarity sequence between query and target proteins (be patient!).')
if tmp_dir:
tmp_query_hits_table = tempfile.NamedTemporaryFile(prefix='comparem_hits_', dir=tmp_dir, delete=False)
else:
tmp_query_hits_table = tempfile.NamedTemporaryFile(prefix='comparem_hits_', delete=False)
tmp_query_hits_table.close()
query_hits_daa_file = os.path.join(output_dir, 'query_hits')
if high_mem:
diamond.blastp(query_gene_file,
target_diamond_db,
evalue,
per_identity,
per_aln_len,
max_hits,
sensitive,
tmp_query_hits_table.name,
'standard',
tmp_dir,
chunk_size=1,
block_size=8)
else:
diamond.blastp(query_gene_file,
target_diamond_db,
evalue,
per_identity,
per_aln_len,
max_hits,
sensitive,
tmp_query_hits_table.name,
'standard',
tmp_dir)
# get target genes hit by one or more query proteins
self.logger.info('Creating file with target proteins with similarity to query proteins.')
target_hit = set()
for line in open(tmp_query_hits_table.name):
line_split = line.split('\t')
target_hit.add(line_split[1])
target_genes_hits = os.path.join(output_dir, 'target_genes_hit.faa')
fout = open(target_genes_hits, 'w')
for seq_id, seq in seq_io.read_seq(target_gene_file):
if seq_id in target_hit:
fout.write('>' + seq_id + '\n')
fout.write(seq + '\n')
fout.close()
self.logger.info('Identified %d target proteins to be used in reciprocal search.' % len(target_hit))
# perform reciprocal blast
self.logger.info('Performing reciprocal similarity sequence between target and query proteins (be patient!).')
if tmp_dir:
tmp_target_hits_table = tempfile.NamedTemporaryFile(prefix='comparem_hits_', dir=tmp_dir, delete=False)
else:
tmp_target_hits_table = tempfile.NamedTemporaryFile(prefix='comparem_hits_', delete=False)
tmp_target_hits_table.close()
if high_mem:
diamond.blastp(target_genes_hits,
query_diamond_db,
evalue,
per_identity,
per_aln_len,
max_hits,
sensitive,
tmp_target_hits_table.name,
'standard',
tmp_dir,
chunk_size=1,
block_size=8)
else:
diamond.blastp(target_genes_hits,
query_diamond_db,
evalue,
per_identity,
per_aln_len,
max_hits,
sensitive,
tmp_target_hits_table.name,
'standard',
tmp_dir)
# combine hit tables and sort
os.system('cat %s >> %s' % (tmp_target_hits_table.name, tmp_query_hits_table.name))
os.remove(tmp_target_hits_table.name)
hits_table_file = os.path.join(output_dir, 'hits_sorted.tsv')
self._sort_hit_table(tmp_query_hits_table.name, hits_table_file)
def _run_self_diamond(self, query_gene_file,
evalue,
per_identity,
per_aln_len,
max_hits,
sensitive,
high_mem,
tmp_dir,
output_dir):
"""Perform similarity search of query genes against themselves.
Parameters
----------
query_gene_file : str
File with all query sequences.
evalue : float
E-value threshold for reporting hits.
per_identity : float
Percent identity threshold for reporting hits.
per_aln_len : float
Percent query coverage threshold for reporting hits.
max_hits : int
Maximum number of hits to report per query sequences.
tmp_dir : str
Directory to store temporary files.
output_dir : str
Directory to store blast results.
"""
self.logger.info('Creating DIAMOND database (be patient!).')
diamond_db = os.path.join(output_dir, 'query_genes')
diamond = Diamond(self.cpus)
diamond.make_database(query_gene_file, diamond_db)
# create flat hits table
if tmp_dir:
tmp_hits_table = tempfile.NamedTemporaryFile(prefix='comparem_hits_', dir=tmp_dir, delete=False)
else:
tmp_hits_table = tempfile.NamedTemporaryFile(prefix='comparem_hits_', delete=False)
tmp_hits_table.close()
# blast all genes against the database
self.logger.info('Performing self similarity sequence between genomes (be patient!).')
if high_mem:
diamond.blastp(query_gene_file,
diamond_db,
evalue,
per_identity,
per_aln_len,
max_hits,
sensitive,
tmp_hits_table.name,
'standard',
tmp_dir,
chunk_size=1,
block_size=8)
else:
diamond.blastp(query_gene_file,
diamond_db,
evalue,
per_identity,
per_aln_len,
max_hits,
sensitive,
tmp_hits_table.name,
'standard',
tmp_dir)
# sort hit table
hits_table_file = os.path.join(output_dir, 'hits_sorted.tsv')
self._sort_hit_table(tmp_hits_table.name, hits_table_file)
def run(self, query_proteins, db_file, custom_db_file, taxonomy_file,
custom_taxonomy_file, evalue, per_identity, per_aln_len,
max_matches, homology_search, min_per_taxa, consensus, min_per_bp,
use_trimAl, restrict_taxon, msa_program, tree_program, prot_model,
skip_rooting, output_dir):
"""Infer a gene tree for homologs genes identified by blast.
Workflow for inferring a gene tree from sequences identified as being
homologs to a set of query proteins. Homologs are identified using BLASTP
and a set of user-defined parameters.
Parameters
----------
query_proteins : str
Fasta file containing query proteins.
db_file : str
BLAST database of reference proteins.
custom_db_file : str
Custom database of proteins.
taxonomy_file : str
Taxonomic assignment of each reference genomes.
custom_taxonomy_file : str
Taxonomic assignment of genomes in custom database.
evalue : float
E-value threshold used to define homolog.
per_identity : float
Percent identity threshold used to define a homolog.
per_aln_len : float
Alignment length threshold used to define a homolog.
max_matches : int
Maximum matches per query protein.
metadata : dict[genome_id] -> metadata dictionary
Metadata for genomes.
homology_search : str
Type of homology search to perform.
min_per_taxa : float
Minimum percentage of taxa required to retain a column.
consensus : float
Minimum percentage of the same amino acid required to retain column.
min_per_bp : float
Minimum percentage of base pairs required to keep trimmed sequence.
use_trimAl : boolean
Filter columns using trimAl.
restrict_taxon : str
Restrict alignment to specific taxonomic group (e.g., k__Archaea).
msa_program : str
Program to use for multiple sequence alignment ['mafft', 'muscle'].
tree_program : str
Program to use for tree inference ['fasttree', 'raxml'].
prot_model : str
Protein substitution model for tree inference ['WAG', 'LG', 'AUTO'].
skip_rooting : boolean
Skip midpoint rooting if True.
output_dir : str
Directory to store results.
"""
# validate query sequence names for use with GeneTreeTk
validate_seq_ids(query_proteins)
# read taxonomy file
self.logger.info('Reading taxonomy file.')
taxonomy = Taxonomy().read(taxonomy_file)
if custom_taxonomy_file:
custom_taxonomy = Taxonomy().read(custom_taxonomy_file)
taxonomy.update(custom_taxonomy)
# report distribution of query genes
mean_len, max_len, min_len, p10, p50, p90 = self._gene_distribution(
query_proteins)
self.logger.info(
'Query gene lengths: min, mean, max = %d, %.1f, %d | p10, p50, p90 = %.1f, %.1f, %.1f'
% (min_len, mean_len, max_len, p10, p50, p90))
# identify homologs using BLASTP
self.logger.info('Identifying homologs using %s.' % homology_search)
blast = Blast(self.cpus)
blast_output = os.path.join(output_dir, 'reference_hits.tsv')
if homology_search == 'diamond':
diamond = Diamond(self.cpus)
diamond.blastp(query_proteins,
db_file,
evalue,
per_identity,
per_aln_len,
max_matches,
blast_output,
output_fmt='custom')
else:
blast.blastp(query_proteins,
db_file,
blast_output,
evalue,
max_matches,
output_fmt='custom',
task=homology_search)
homologs = blast.identify_homologs(blast_output, evalue, per_identity,
per_aln_len)
self.logger.info('Identified %d homologs in reference database.' %
len(homologs))
custom_homologs = None
if custom_db_file:
custom_blast_output = os.path.join(output_dir, 'custom_hits.tsv')
if homology_search == 'diamond':
diamond = Diamond(self.cpus)
diamond.blastp(query_proteins,
custom_db_file,
evalue,
per_identity,
per_aln_len,
max_matches,
custom_blast_output,
output_fmt='custom')
else:
blast.blastp(query_proteins,
custom_db_file,
custom_blast_output,
evalue,
max_matches,
output_fmt='custom',
task=homology_search)
custom_homologs = blast.identify_homologs(custom_blast_output,
evalue, per_identity,
per_aln_len)
self.logger.info('Identified %d homologs in custom database.' %
len(custom_homologs))
# restrict homologs to specific taxonomic group
if restrict_taxon:
self.logger.info('Restricting homologs to %s.' % restrict_taxon)
restricted_homologs = {}
for query_id, hit in homologs.iteritems():
genome_id = hit.subject_id.split('~')[0]
if restrict_taxon in taxonomy[genome_id]:
restricted_homologs[query_id] = hit
self.logger.info(
'%d of %d homologs in reference database are from the specified group.'
% (len(restricted_homologs), len(homologs)))
homologs = restricted_homologs
if len(homologs) == 0:
self.logger.error(
'Too few homologs were identified. Gene tree cannot be inferred.'
)
sys.exit()
# extract homologs
self.logger.info(
'Extracting homologs and determining local gene context.')
db_homologs_tmp = os.path.join(output_dir, 'homologs_db.tmp')
gene_precontext, gene_postcontext = self.extract_homologs_and_context(
homologs.keys(), db_file, db_homologs_tmp)
# report gene length distribution of homologs
mean_len, max_len, min_len, p10, p50, p90 = self._gene_distribution(
db_homologs_tmp)
self.logger.info(
'Homolog gene lengths: min, mean, max = %d, %.1f, %d | p10, p50, p90 = %.1f, %.1f, %.1f'
% (min_len, mean_len, max_len, p10, p50, p90))
# concatenate homologs with initial query genes
homolog_ouput_tmp = os.path.join(output_dir, 'homologs.faa.tmp')
if custom_homologs:
custom_db_homologs_tmp = os.path.join(output_dir,
'custom_homologs_db.tmp')
custom_gene_precontext, custom_gene_postcontext = self.extract_homologs_and_context(
custom_homologs.keys(), custom_db_file, custom_db_homologs_tmp)
gene_precontext.update(custom_gene_precontext)
gene_postcontext.update(custom_gene_postcontext)
homologs.update(custom_homologs)
concatenate_files(
[query_proteins, db_homologs_tmp, custom_db_homologs_tmp],
homolog_ouput_tmp)
os.remove(custom_db_homologs_tmp)
else:
concatenate_files([query_proteins, db_homologs_tmp],
homolog_ouput_tmp)
os.remove(db_homologs_tmp)
# remove stop codons
homolog_ouput = os.path.join(output_dir, 'homologs.faa')
self._remove_stop_codons(homolog_ouput_tmp, homolog_ouput)
os.remove(homolog_ouput_tmp)
# infer multiple sequence alignment
msa = MsaWorkflow(self.cpus)
trimmed_msa_output = msa.run(homolog_ouput, min_per_taxa, consensus,
min_per_bp, use_trimAl, msa_program,
output_dir)
# infer tree
tw = TreeWorkflow(self.cpus)
tree_output = tw.run(trimmed_msa_output, tree_program, prot_model,
skip_rooting, output_dir)
# create tax2tree consensus map and decorate tree
self.logger.info('Decorating internal tree nodes with tax2tree.')
output_taxonomy_file = os.path.join(output_dir, 'taxonomy.tsv')
fout = open(output_taxonomy_file, 'w')
for homolog_id in homologs.keys():
genome_id = homolog_id.split('~')[0]
t = taxonomy.get(genome_id, None)
if t:
fout.write(homolog_id + '\t' + ';'.join(t) + '\n')
fout.close()
t2t_tree = os.path.join(output_dir, 'homologs.tax2tree.tree')
cmd = 't2t decorate -m %s -t %s -o %s' % (output_taxonomy_file,
tree_output, t2t_tree)
os.system(cmd)
# create tree with leaf nodes given as genome accessions
tree = dendropy.Tree.get_from_path(t2t_tree,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
for leaf in tree.leaf_node_iter():
leaf.taxon.label = leaf.taxon.label.split('~')[0]
genome_tree = os.path.join(output_dir,
'homologs.tax2tree.genome_accessions.tree')
tree.write_to_path(genome_tree,
schema='newick',
suppress_rooting=True,
unquoted_underscores=True)
# setup metadata for ARB file
src_dir = os.path.dirname(os.path.realpath(__file__))
version_file = open(os.path.join(src_dir, 'VERSION'))
metadata = {}
metadata['genetreetk_version'] = version_file.read().strip()
metadata['genetreetk_query_proteins'] = query_proteins
metadata['genetreetk_db_file'] = db_file
metadata['genetreetk_taxonomy_file'] = taxonomy_file
metadata['genetreetk_blast_evalue'] = str(evalue)
metadata['genetreetk_blast_per_identity'] = str(per_identity)
metadata['genetreetk_blast_per_aln_len'] = str(per_aln_len)
metadata['genetreetk_blast_max_matches'] = str(max_matches)
metadata['genetreetk_homology_search'] = homology_search
metadata['genetreetk_msa_min_per_taxa'] = str(min_per_taxa)
metadata['genetreetk_msa_consensus'] = str(consensus)
metadata['genetreetk_msa_min_per_bp'] = str(min_per_bp)
metadata['genetreetk_msa_program'] = msa_program
metadata['genetreetk_tree_program'] = tree_program
metadata['genetreetk_tree_prot_model'] = prot_model
# create ARB metadata file
self.logger.info('Creating ARB metadata file.')
arb_metadata_file = os.path.join(output_dir, 'arb.metadata.txt')
self.create_arb_metadata(homologs, trimmed_msa_output, taxonomy,
metadata, gene_precontext, gene_postcontext,
arb_metadata_file)
def run(self, scaffold_gene_file, stat_file, ref_genome_gene_files, db_file, evalue, per_identity,):
"""Create taxonomic profiles for a set of genomes.
Parameters
----------
scaffold_gene_file : str
Fasta file of genes on scaffolds in amino acid space.
stat_file : str
File with statistics for individual scaffolds.
ref_genome_gene_files : list of str
Fasta files of called genes on reference genomes of interest.
db_file : str
Database of competing reference genes.
evalue : float
E-value threshold used by blast.
per_identity: float
Percent identity threshold used by blast.
"""
# read statistics file
self.logger.info('')
self.logger.info(' Reading scaffold statistics.')
scaffold_stats = ScaffoldStats()
scaffold_stats.read(stat_file)
# perform homology searches
self.logger.info('')
self.logger.info(' Creating diamond database for reference genomes.')
ref_gene_file = os.path.join(self.output_dir, 'ref_genes.faa')
concatenate_gene_files(ref_genome_gene_files, ref_gene_file)
diamond = Diamond(self.cpus)
ref_diamond_db = os.path.join(self.output_dir, 'ref_genes')
diamond.make_database(ref_gene_file, ref_diamond_db)
self.logger.info(' Identifying homologs within reference genomes of interest (be patient!).')
self.diamond_dir = os.path.join(self.output_dir, 'diamond')
make_sure_path_exists(self.diamond_dir)
hits_ref_genomes_daa = os.path.join(self.diamond_dir, 'ref_hits')
diamond.blastp(scaffold_gene_file, ref_diamond_db, evalue, per_identity, 1, hits_ref_genomes_daa)
hits_ref_genomes = os.path.join(self.diamond_dir, 'ref_hits.tsv')
diamond.view(hits_ref_genomes_daa + '.daa', hits_ref_genomes)
self.logger.info(' Identifying homologs within competing reference genomes (be patient!).')
hits_comp_ref_genomes_daa = os.path.join(self.diamond_dir, 'competing_ref_hits')
diamond.blastp(scaffold_gene_file, db_file, evalue, per_identity, 1, hits_comp_ref_genomes_daa)
hits_comp_ref_genomes = os.path.join(self.diamond_dir, 'competing_ref_hits.tsv')
diamond.view(hits_comp_ref_genomes_daa + '.daa', hits_comp_ref_genomes)
# get list of genes with a top hit to the reference genomes of interest
hits_to_ref = self._top_hits_to_reference(hits_ref_genomes, hits_comp_ref_genomes)
# get number of genes on each scaffold
num_genes_on_scaffold = defaultdict(int)
for seq_id, _seq in seq_io.read_seq(scaffold_gene_file):
scaffold_id = seq_id[0:seq_id.rfind('_')]
num_genes_on_scaffold[scaffold_id] += 1
# get hits to each scaffold
hits_to_scaffold = defaultdict(list)
for query_id, hit in hits_to_ref.iteritems():
gene_id = query_id[0:query_id.rfind('~')]
scaffold_id = gene_id[0:gene_id.rfind('_')]
hits_to_scaffold[scaffold_id].append(hit)
# report summary stats for each scaffold
reference_out = os.path.join(self.output_dir, 'references.tsv')
fout = open(reference_out, 'w')
fout.write('Scaffold id\tSubject scaffold ids\tSubject genome ids')
fout.write('\tGenome id\tLength (bp)\tGC\tMean coverage')
fout.write('\t# genes\t# hits\t% genes\tAvg. align. length (bp)\tAvg. % identity\tAvg. e-value\tAvg. bitscore\n')
for scaffold_id, hits in hits_to_scaffold.iteritems():
aln_len = []
perc_iden = []
evalue = []
bitscore = []
subject_scaffold_ids = defaultdict(int)
subject_bin_ids = defaultdict(int)
for hit in hits:
aln_len.append(hit.aln_length)
perc_iden.append(hit.perc_identity)
evalue.append(hit.evalue)
bitscore.append(hit.bitscore)
subject_id, subject_bin_id = hit.subject_id.split('~')
subject_scaffold_id = subject_id[0:subject_id.rfind('_')]
subject_scaffold_ids[subject_scaffold_id] += 1
subject_bin_ids[subject_bin_id] += 1
subject_scaffold_id_str = []
for subject_id, num_hits in subject_scaffold_ids.iteritems():
subject_scaffold_id_str.append(subject_id + ':' + str(num_hits))
subject_scaffold_id_str = ','.join(subject_scaffold_id_str)
subject_bin_id_str = []
for bin_id, num_hits in subject_bin_ids.iteritems():
subject_bin_id_str.append(bin_id + ':' + str(num_hits))
subject_bin_id_str = ','.join(subject_bin_id_str)
fout.write('%s\t%s\t%s\t%s\t%.2f\t%d\t%d\t%.2f\t%d\t%.2f\t%.2g\t%.2f\n' % (
scaffold_id,
subject_scaffold_id_str,
subject_bin_id_str,
scaffold_stats.print_stats(scaffold_id),
mean(scaffold_stats.coverage(scaffold_id)),
num_genes_on_scaffold[scaffold_id],
len(hits),
len(hits) * 100.0 / num_genes_on_scaffold[scaffold_id],
mean(aln_len),
mean(perc_iden),
mean(evalue),
mean(bitscore)))
fout.close()
return reference_out