def test_read_contigs_file(self): cur_dir = os.path.dirname(__file__) file_name = os.path.join(cur_dir, "fixtures/generated_contigs_test.fna") open_file = open(file_name, "r") contigs = read_contigs_file(open_file) assert_equal(len(contigs), 29) assert_equal(contigs[0].id, "Ehrlichia_canis_Jake_uid58071") assert_equal(contigs[0].contig_id, 1000) assert_equal(contigs[0].family, "Anaplasmataceae")
def main(contigs_file,taxonomy_file, dir_path, kmer_length, contig_length, algorithm): groups = [] DNA.generate_kmer_hash(kmer_length) contigs = read_contigs_file(contigs_file,start_position=True) # Divide genomes into groups, one for each genus meta_genomes = genome_info_from_parsed_taxonomy_file(taxonomy_file) # Fetch sequence for each genome genomes = read_FASTA_files_no_groups(meta_genomes, dir_path) genome_part_l = 10000 for genome in genomes: genome.calculate_signature() genome.parts = genome.split_seq(genome_part_l) for part in genome.parts: part.calculate_signature() genome.pseudo_par = model.fit_nonzero_parameters(\ genome.parts, algorithm = algorithm) scores = [] for contig in contigs: contig.calculate_signature() for genome in genomes: if contig.id == genome.id: s = int(contig.start_position) start_part_index = s/genome_part_l end_part_index = (s+contig_length)/genome_part_l if start_part_index == end_part_index: i = start_part_index temp_pseudo_par = model.fit_nonzero_parameters(\ genome.parts[0:i]+genome.parts[i+1:], algorithm=algorithm) else: i1 = start_part_index i2 = end_part_index temp_pseudo_par = model.fit_nonzero_parameters(\ genome.parts[0:i1]+genome.parts[i2+1:], algorithm=algorithm) p_val = model.log_probability(\ contig, temp_pseudo_par) else: p_val = model.log_probability(\ contig, genome.pseudo_par) scores.append(\ Score(p_val, contig, genome, contig.contig_id)) sys.stdout.write("p_value\tcontig_family\tcontig_genus\tcontig_species\tcontig_genome\tcompare_family\tcompare_genus\tcompare_species\tcompare_genome\tcontig_id" + os.linesep) for score in scores: sys.stdout.write(str(score) + '\n')
def main(contigs_file, taxonomy_file, dir_path, kmer_length, dir_structure, taxonomy_info_in_contigs): groups = [] DNA.generate_kmer_hash(kmer_length) contigs = read_contigs_file(contigs_file, taxonomy_info=taxonomy_info_in_contigs) # Divide genomes into groups, one for each genus meta_genomes = genome_info_from_parsed_taxonomy_file(taxonomy_file) # Fetch sequence for each genome genomes = read_FASTA_files_no_groups(meta_genomes, dir_path, dir_structure=dir_structure) for genome in genomes: genome.calculate_signature() genome.pseudo_par = mn.fit_nonzero_parameters([genome]) scores = [] for contig in contigs: contig.calculate_signature() for genome in genomes: if contig.id == genome.id: temp_genome = deepcopy(genome) temp_genome.signature.subtract(contig.signature) temp_pseudo_par = mn.fit_nonzero_parameters([temp_genome]) p_val = mn.log_probability(contig, temp_pseudo_par) else: p_val = mn.log_probability(contig, genome.pseudo_par) scores.append(Score(p_val, contig, genome, contig.contig_id, taxonomy_info=taxonomy_info_in_contigs)) if taxonomy_info_in_contigs: sys.stdout.write( "p_value\tcontig_family\tcontig_genus\tcontig_species\tcontig_genome\tcompare_family\tcompare_genus\tcompare_species\tcompare_genome\tcontig_id" + os.linesep ) else: sys.stdout.write( "p_value\t\tcontig_genome\tcompare_family\tcompare_genus\tcompare_species\tcompare_genome\tcontig_id" + os.linesep ) for score in scores: sys.stdout.write(str(score) + "\n")
def main(contigs_file,taxonomy_file, dir_path, kmer_length, contig_length): groups = [] DNA.generate_kmer_hash(kmer_length) contigs = read_contigs_file(contigs_file,start_position=True) # Divide genomes into groups, one for each genus meta_genomes = genome_info_from_parsed_taxonomy_file(taxonomy_file) # Fetch sequence for each genome genomes = read_FASTA_files_no_groups(meta_genomes, dir_path) genome_part_l = 10000 for genome in genomes: genome.calculate_signature() genome.parts = genome.split_seq(genome_part_l) for part in genome.parts: part.calculate_signature() alpha_fit = model.fit_nonzero_parameters_full_output(\ genome.parts) sys.stderr.write(str(alpha_fit)+'\n') genome.pseudo_par = alpha_fit[0] scores = [] for contig in contigs: contig.calculate_signature() contig.pseudo_counts_array = np.fromiter(contig.pseudo_counts,np.dtype('u4'),DNA.kmer_hash_count).reshape((1,DNA.kmer_hash_count)) for genome in genomes: p_val = model.log_probability(\ contig, genome.pseudo_par, pseudo_counts_supplied=True) scores.append(\ Score(p_val, contig, genome, contig.contig_id)) sys.stdout.write("p_value\tcontig_family\tcontig_genus\tcontig_species\tcontig_genome\tcompare_family\tcompare_genus\tcompare_species\tcompare_genome\tcontig_id" + os.linesep) for score in scores: sys.stdout.write(str(score) + '\n')
def main(contigs_file,contig_time_series_file, genome_time_series_file, taxonomy_file,dir_path, contig_length, total_read_count,assembly_length,first_data,last_data): DNA.generate_kmer_hash(2) contigs = read_contigs_file(contigs_file,start_position=True) contig_time_series_df = read_time_series(contig_time_series_file) if len(contigs)!=len(contig_time_series_df.index): raise TypeError("The number of contigs and time series does not match") for contig in contigs: contig.mapping_reads = contig_time_series_df[contig_time_series_df.contig_id == contig.contig_id] # Divide genomes into groups, one for each genus meta_genomes = genome_info_from_parsed_taxonomy_file(taxonomy_file) # Fetch sequence for each genome genomes = read_FASTA_files_no_groups(meta_genomes, dir_path) # Fetch time series for each genome read_time_series_file_genomes(genomes, genome_time_series_file) for genome in genomes: genome.pseudo_par = model.fit_nonzero_parameters([genome],total_read_count) scores = [] for contig in contigs: for genome in genomes: p_val = model.log_probability(\ contig, genome.pseudo_par, total_read_count,assembly_length) scores.append(\ Score(p_val, contig, genome, contig.contig_id)) sys.stdout.write("p_value\tcontig_family\tcontig_genus\tcontig_species\tcontig_genome\tcompare_family\tcompare_genus\tcompare_species\tcompare_genome\tcontig_id" + os.linesep) for score in scores: sys.stdout.write(str(score) + '\n')