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')
