def test_log_probability_full(self):
file_name = os.path.join(cur_dir,"..","data/generated_contigs_test.fna")
f = fileinput.input(file_name)
c = list(SeqIO.parse(f,"fasta"))
f.close()
dna_c1g1 = dna.DNA(id = c[0].id, seq = str(c[0].seq))
dna_c2g1 = dna.DNA(id = c[1].id, seq = str(c[1].seq))
dna_c3g1 = dna.DNA(id = c[2].id, seq = str(c[2].seq))
dna_c1g2 = dna.DNA(id = c[-3].id, seq = str(c[-3].seq))
dna_c2g2 = dna.DNA(id = c[-2].id, seq = str(c[-2].seq))
dna_c3g2 = dna.DNA(id = c[-1].id, seq = str(c[-1].seq))
cluster1 = [dna_c1g1,dna_c2g1,dna_c3g1]
cluster2 = [dna_c1g2,dna_c2g2,dna_c3g2]
for contig in cluster1 + cluster2:
contig.calculate_signature()
parameters1 = model.fit_nonzero_parameters(cluster1)
parameters2 = model.fit_nonzero_parameters(cluster2)
# These testa are probably too shaky, due to the
# numerical optimization for finding the parameters
s1 = dna_c1g1
log_prob1 = model.log_probability(s1,parameters1)
assert_almost_equal(log_prob1/10000.0, -0.450, places = 1)
log_prob2 = model.log_probability(s1,parameters2)
assert_almost_equal(log_prob2/10000.0,-0.4676, places = 2)
s2 = dna_c1g2
log_prob3 = model.log_probability(s2,parameters1)
assert_almost_equal(log_prob3/10000.0,-0.517, places = 2)
log_prob4 = model.log_probability(s2,parameters2)
assert_almost_equal(log_prob4/10000.0,-0.483, places = 2)
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 test_log_probability_order(self):
file_name = os.path.join(cur_dir,"..","data/generated_contigs_test.fna")
f = fileinput.input(file_name)
c = list(SeqIO.parse(f,"fasta"))
f.close()
dna_c1g1 = dna.DNA(id = c[0].id, seq = str(c[0].seq))
dna_c2g1 = dna.DNA(id = c[1].id, seq = str(c[1].seq))
dna_c3g1 = dna.DNA(id = c[2].id, seq = str(c[2].seq))
dna_c1g2 = dna.DNA(id = c[-3].id, seq = str(c[-3].seq))
dna_c2g2 = dna.DNA(id = c[-2].id, seq = str(c[-2].seq))
dna_c3g2 = dna.DNA(id = c[-1].id, seq = str(c[-1].seq))
cluster1 = [dna_c1g1,dna_c2g1,dna_c3g1]
cluster2 = [dna_c1g2,dna_c2g2,dna_c3g2]
for contig in cluster1 + cluster2:
contig.calculate_signature()
parameters1 = model.fit_nonzero_parameters(cluster1)
parameters2 = model.fit_nonzero_parameters(cluster2)
log_prob1 = model.log_probability(dna_c1g1,parameters1)
log_prob2 = model.log_probability(dna_c1g1,parameters2)
assert_equal(log_prob1>log_prob2,True)
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')
