def process(self):
# load genomes from genomes storage
self.load_genomes()
# check sanity
self.sanity_check()
# gen pan_db
self.generate_pan_db()
# get all amino acid sequences:
combined_aas_FASTA_path = self.get_output_file_path('combined-aas.fa')
self.genomes_storage.gen_combined_aa_sequences_FASTA(combined_aas_FASTA_path,
exclude_partial_gene_calls=self.exclude_partial_gene_calls)
# get unique amino acid sequences:
self.progress.new('Uniquing the output FASTA file')
self.progress.update('...')
unique_aas_FASTA_path, unique_aas_names_file_path, unique_aas_names_dict = utils.unique_FASTA_file(combined_aas_FASTA_path, store_frequencies_in_deflines=False)
self.progress.end()
self.run.info('Unique AA sequences FASTA', unique_aas_FASTA_path)
# run search
blastall_results = self.run_search(unique_aas_FASTA_path, unique_aas_names_dict)
# generate MCL input from filtered blastall_results
mcl_input_file_path = self.gen_mcl_input(blastall_results)
# get clusters from MCL
mcl_clusters = self.run_mcl(mcl_input_file_path)
# we have the raw gene clusters dict, but we need to re-format it for following steps
gene_clusters_dict = self.gen_gene_clusters_dict_from_mcl_clusters(mcl_clusters)
del mcl_clusters
# compute alignments for genes within each gene_cluster (or don't)
gene_clusters_dict = self.compute_alignments_for_gene_clusters(gene_clusters_dict)
# populate the pan db with results
gene_clusters_dict = self.process_gene_clusters(gene_clusters_dict)
# store gene clusters dict into the db
self.store_gene_clusters(gene_clusters_dict)
# generate a hierarchical clustering of gene clusters (or don't)
self.gen_hierarchical_clustering_of_gene_clusters()
# generate orderings of gene_clusters based on synteny of genes
self.gen_synteny_based_ordering_of_gene_clusters(gene_clusters_dict)
# populate layers additional data and orders
self.populate_layers_additional_data_and_orders()
# work with gene cluster homogeneity index
self.populate_gene_cluster_homogeneity_index(gene_clusters_dict)
# done
self.run.info('log file', self.run.log_file_path)
self.run.quit()
def gen_combined_aa_sequences_FASTA(self,
output_file_path,
exclude_partial_gene_calls=False):
self.run.info('Exclude partial gene calls',
exclude_partial_gene_calls,
nl_after=1)
genomes = self.get_genomes_dict()
total_num_aa_sequences = 0
total_num_excluded_aa_sequences = 0
output_file = open(output_file_path, 'w')
for genome_name in genomes:
self.progress.new('Storing aa sequences')
self.progress.update('%s ...' % genome_name)
genome_data = self.D(genome_name)
gene_caller_ids = sorted(
[int(i[0]) for i in list(genome_data.items())])
for gene_caller_id in gene_caller_ids:
partial = self.G(gene_caller_id, genome_data)['partial'].value
if exclude_partial_gene_calls and partial:
total_num_excluded_aa_sequences += 1
continue
aa_sequence = self.G(gene_caller_id,
genome_data)['aa_sequence'].value
output_file.write(
'>%s_%d\n' %
(genomes[genome_name]['genome_hash'], int(gene_caller_id)))
output_file.write('%s\n' % aa_sequence)
total_num_aa_sequences += 1
self.progress.end()
output_file.close()
self.progress.new('Uniquing the output FASTA file')
self.progress.update('...')
unique_aas_FASTA_path, unique_aas_names_file_path, unique_aas_names_dict = utils.unique_FASTA_file(
output_file_path, store_frequencies_in_deflines=False)
self.progress.end()
self.run.info('Unique AA sequences FASTA', output_file_path)
self.run.info('Num AA sequences reported',
'%s' % pp(total_num_aa_sequences),
nl_before=1)
self.run.info('Num excluded gene calls',
'%s' % pp(total_num_excluded_aa_sequences))
return unique_aas_FASTA_path, unique_aas_names_dict
def gen_combined_proteins_unique_FASTA(self):
self.progress.new('Storing combined protein sequences')
combined_proteins_FASTA_path = self.get_output_file_path(
'combined-proteins.fa')
output_file = open(combined_proteins_FASTA_path, 'w')
for genome_name in self.genomes:
g = self.genomes[genome_name]
self.progress.update('Working on %s ...' % genome_name)
for gene_caller_id in g['gene_caller_ids']:
output_file.write('>%s_%d\n' %
(g['genome_entry_hash'], gene_caller_id))
output_file.write(
'%s\n' %
self.protein_sequences_dict[genome_name][gene_caller_id])
output_file.close()
self.progress.end()
# unique the FASTA file
unique_proteins_FASTA_path, unique_proteins_names_file_path, unique_proteins_names_dict = utils.unique_FASTA_file(
combined_proteins_FASTA_path, store_frequencies_in_deflines=False)
self.run.info('Num unique protein sequences',
'%s' % pp(len(unique_proteins_names_dict)))
self.run.info('Combined protein sequences FASTA',
combined_proteins_FASTA_path)
self.run.info('Unique protein sequences FASTA',
unique_proteins_FASTA_path)
return unique_proteins_FASTA_path, unique_proteins_names_dict
def gen_combined_proteins_unique_FASTA(self):
self.progress.new('Storing combined protein sequences')
combined_proteins_FASTA_path = self.get_output_file_path('combined-proteins.fa')
output_file = open(combined_proteins_FASTA_path, 'w')
for genome_name in self.genomes:
g = self.genomes[genome_name]
self.progress.update('Working on %s ...' % genome_name)
for gene_caller_id in g['gene_caller_ids']:
output_file.write('>%s_%d\n' % (g['genome_entry_hash'], gene_caller_id))
output_file.write('%s\n' % self.protein_sequences_dict[genome_name][gene_caller_id])
output_file.close()
self.progress.end()
# unique the FASTA file
unique_proteins_FASTA_path, unique_proteins_names_file_path, unique_proteins_names_dict = utils.unique_FASTA_file(combined_proteins_FASTA_path, store_frequencies_in_deflines=False)
self.run.info('Num unique protein sequences', '%s' % pp(len(unique_proteins_names_dict)))
self.run.info('Combined protein sequences FASTA', combined_proteins_FASTA_path)
self.run.info('Unique protein sequences FASTA', unique_proteins_FASTA_path)
return unique_proteins_FASTA_path, unique_proteins_names_dict
