def shogun_bt2_capitalist(input, output, bt2_indx, reference_fasta, reference_map, extract_ncbi_tid, depth, threads): verify_make_dir(output) fna_files = [os.path.join(input, filename) for filename in os.listdir(input) if filename.endswith('.fna')] for fna_file in fna_files: sam_outf = os.path.join(output, '.'.join(str(os.path.basename(fna_file)).split('.')[:-1]) + '.sam') print(bowtie2_align(fna_file, sam_outf, bt2_indx, num_threads=threads)) tree = NCBITree() begin, end = extract_ncbi_tid.split(',') sam_files = [os.path.join(output, filename) for filename in os.listdir(output) if filename.endswith('.sam')] lca_maps = {} for sam_file in sam_files: lca_map = {} for qname, rname in yield_alignments_from_sam_inf(sam_file): ncbi_tid = int(find_between(rname, begin, end)) if qname in lca_map: current_ncbi_tid = lca_map[qname] if current_ncbi_tid: if current_ncbi_tid != ncbi_tid: lca_map[qname] = tree.lowest_common_ancestor(ncbi_tid, current_ncbi_tid) else: lca_map[qname] = ncbi_tid lca_map = valmap(lambda x: tree.green_genes_lineage(x, depth=depth), lca_map) # filter out null values lca_maps['.'.join(os.path.basename(sam_file).split('.')[:-1])] = reverse_collision_dict(lca_map) for basename in lca_maps.keys(): lca_maps[basename] = valmap(lambda val: (basename, val), lca_maps[basename]) lca_map_2 = defaultdict(list) for basename in lca_maps.keys(): for key, val in lca_maps[basename].items(): if key: lca_map_2[key].append(val) fna_faidx = {} for fna_file in fna_files: fna_faidx[os.path.basename(fna_file)[:-4]] = pyfaidx.Fasta(fna_file) dict_reference_map = defaultdict(list) with open(reference_map) as inf: tsv_in = csv.reader(inf, delimiter='\t') for line in tsv_in: dict_reference_map[';'.join(line[1].split('; '))].append(line[0]) # reverse the dict to feed into embalmer references_faidx = pyfaidx.Fasta(reference_fasta) tmpdir = tempfile.mkdtemp() with open(os.path.join(output, 'embalmer_out.txt'), 'w') as embalmer_cat: for key in lca_map_2.keys(): queries_fna_filename = os.path.join(tmpdir, 'queries.fna') references_fna_filename = os.path.join(tmpdir, 'reference.fna') output_filename = os.path.join(tmpdir, 'output.txt') with open(queries_fna_filename, 'w') as queries_fna: for basename, headers in lca_map_2[key]: for header in headers: record = fna_faidx[basename][header][:] queries_fna.write('>filename|%s|%s\n%s\n' % (basename, record.name, record.seq)) with open(references_fna_filename, 'w') as references_fna: for i in dict_reference_map[key]: record = references_faidx[i][:] references_fna.write('>%s\n%s\n' % (record.name, record.seq)) embalmer_align(queries_fna_filename, references_fna_filename, output_filename) with open(output_filename) as embalmer_out: for line in embalmer_out: embalmer_cat.write(line) os.remove(queries_fna_filename) os.remove(references_fna_filename) os.remove(output_filename) os.rmdir(tmpdir) sparse_ncbi_dict = defaultdict(dict) # build query by NCBI_TID DataFrame with open(os.path.join(output, 'embalmer_out.txt')) as embalmer_cat: embalmer_csv = csv.reader(embalmer_cat, delimiter='\t') for line in embalmer_csv: # line[0] = qname, line[1] = rname, line[2] = %match ncbi_tid = np.int(find_between(line[1], begin, end)) sparse_ncbi_dict[line[0]][ncbi_tid] = np.float(line[2]) df = pd.DataFrame.from_dict(sparse_ncbi_dict) df.to_csv(os.path.join(output, 'strain_alignments.csv'))
def shogun_utree_capitalist(input, output, utree_indx, reference_fasta, reference_map, extract_ncbi_tid, threads): verify_make_dir(output) basenames = [os.path.basename(filename)[:-4] for filename in os.listdir(input) if filename.endswith('.fna')] for basename in basenames: fna_file = os.path.join(input, basename + '.fna') tsv_outf = os.path.join(output, basename + '.utree.tsv') if not os.path.isfile(tsv_outf): print(utree_search(utree_indx, fna_file, tsv_outf)) else: print("Found the output file \"%s\". Skipping the alignment phase for this file." % tsv_outf) embalmer_outf = os.path.join(output, 'embalmer_out.txt') # Indexing for emblalmer if not os.path.isfile(embalmer_outf): lca_maps = defaultdict(lambda: defaultdict(list)) for basename in basenames: utree_tsv = os.path.join(output, basename + '.utree.tsv') with open(utree_tsv) as inf: tsv_parser = csv.reader(inf, delimiter='\t') for line in tsv_parser: if line[1]: lca_maps[';'.join(line[1].split('; '))][basename].append(line[0]) fna_faidx = {} for basename in basenames: fna_faidx[basename] = pyfaidx.Fasta(os.path.join(input, basename + '.fna')) dict_reference_map = defaultdict(list) with open(reference_map) as inf: tsv_in = csv.reader(inf, delimiter='\t') for line in tsv_in: dict_reference_map[';'.join(line[1].split('; '))].append(line[0]) # reverse the dict to feed into embalmer references_faidx = pyfaidx.Fasta(reference_fasta) tmpdir = tempfile.mkdtemp() print(tmpdir) with open(embalmer_outf, 'w') as embalmer_cat: for species in lca_maps.keys(): queries_fna_filename = os.path.join(tmpdir, 'queries.fna') references_fna_filename = os.path.join(tmpdir, 'reference.fna') output_filename = os.path.join(tmpdir, 'output.txt') with open(queries_fna_filename, 'w') as queries_fna: for basename in lca_maps[species].keys(): for header in lca_maps[species][basename]: record = fna_faidx[basename][header][:] queries_fna.write('>filename|%s|%s\n%s\n' % (basename, record.name, record.seq)) with open(references_fna_filename, 'w') as references_fna: for i in dict_reference_map[species]: record = references_faidx[i][:] references_fna.write('>%s\n%s\n' % (record.name, record.seq)) print(embalmer_align(queries_fna_filename, references_fna_filename, output_filename)) with open(output_filename) as embalmer_out: for line in embalmer_out: embalmer_cat.write(line) os.remove(queries_fna_filename) os.remove(references_fna_filename) os.remove(output_filename) os.rmdir(tmpdir) else: print("Found the output file \"%s\". Skipping the strain alignment phase for this file." % embalmer_outf) # Convert the results from embalmer into CSV sparse_ncbi_dict = defaultdict(dict) begin, end = extract_ncbi_tid.split(',') # build query by NCBI_TID DataFrame with open(embalmer_outf) as embalmer_cat: embalmer_csv = csv.reader(embalmer_cat, delimiter='\t') for line in embalmer_csv: # line[0] = qname, line[1] = rname, line[2] = %match ncbi_tid = np.int(find_between(line[1], begin, end)) sparse_ncbi_dict[line[0]][ncbi_tid] = np.float(line[2]) df = pd.DataFrame.from_dict(sparse_ncbi_dict) df.to_csv(os.path.join(output, 'strain_alignments.csv'))