def test_run_pick_de_novo_otus_muscle(self):
"""run_pick_de_novo_otus w muscle generates expected results
"""
self.params['assign_taxonomy'] = \
{'id_to_taxonomy_fp': self.test_data['refseqs_tax'][0],
'reference_seqs_fp': self.test_data['refseqs'][0]}
self.params['align_seqs'] = {'alignment_method': 'muscle'}
self.params['filter_alignment'] = \
{'suppress_lane_mask_filter': None,
'entropy_threshold': '0.10'}
run_pick_de_novo_otus(self.test_data['seqs'][0],
self.test_out,
call_commands_serially,
self.params,
self.qiime_config,
parallel=False,
status_update_callback=no_status_updates)
input_file_basename = splitext(split(self.test_data['seqs'][0])[1])[0]
otu_map_fp = join(self.test_out, 'uclust_picked_otus',
'%s_otus.txt' % input_file_basename)
alignment_fp = join(self.test_out, 'muscle_aligned_seqs',
'%s_rep_set_aligned.fasta' % input_file_basename)
taxonomy_assignments_fp = join(
self.test_out, 'uclust_assigned_taxonomy',
'%s_rep_set_tax_assignments.txt' % input_file_basename)
otu_table_fp = join(self.test_out, 'otu_table.biom')
tree_fp = join(self.test_out, 'rep_set.tre')
input_seqs = LoadSeqs(self.test_data['seqs'][0],
format='fasta',
aligned=False)
# Number of OTUs falls within a range that was manually
# confirmed
otu_map_lines = list(open(otu_map_fp))
num_otus = len(otu_map_lines)
otu_map_otu_ids = [o.split()[0] for o in otu_map_lines]
self.assertEqual(num_otus, 14)
# all otus get taxonomy assignments
taxonomy_assignment_lines = list(open(taxonomy_assignments_fp))
self.assertEqual(len(taxonomy_assignment_lines), num_otus)
# all OTUs align
aln = LoadSeqs(alignment_fp)
self.assertTrue(aln.getNumSeqs(), num_otus)
# all OTUs in tree
tree = LoadTree(tree_fp)
self.assertEqual(len(tree.tips()), num_otus)
# check that the two final output files have non-zero size
self.assertTrue(getsize(tree_fp) > 0)
self.assertTrue(getsize(otu_table_fp) > 0)
# Check that the log file is created and has size > 0
log_fp = glob(join(self.test_out, 'log*.txt'))[0]
self.assertTrue(getsize(log_fp) > 0)
# parse the otu table
otu_table = parse_biom_table(open(otu_table_fp, 'U'))
expected_sample_ids = [
'f1', 'f2', 'f3', 'f4', 'p1', 'p2', 't1', 't2', 'not16S.1'
]
# sample IDs are as expected
self.assertEqualItems(otu_table.SampleIds, expected_sample_ids)
# expected OTUs
self.assertEqualItems(otu_table.ObservationIds, otu_map_otu_ids)
# number of sequences in the full otu table equals the number of
# input sequences
number_seqs_in_otu_table = sum(
[v.sum() for v in otu_table.iterSampleData()])
self.assertEqual(number_seqs_in_otu_table, input_seqs.getNumSeqs())
def test_run_pick_de_novo_otus_parallel(self):
"""run_pick_de_novo_otus generates expected results in parallel
"""
self.params['assign_taxonomy'] = \
{'id_to_taxonomy_fp': self.test_data['refseqs_tax'][0],
'reference_seqs_fp': self.test_data['refseqs'][0]}
self.params['align_seqs'] = \
{'template_fp': self.test_data['refseqs_aligned'][0]}
self.params['filter_alignment'] = \
{'lane_mask_fp': self.test_data['refseqs_aligned_lanemask'][0]}
actual_tree_fp, actual_otu_table_fp = run_pick_de_novo_otus(
self.test_data['seqs'][0],
self.test_out,
call_commands_serially,
self.params,
self.qiime_config,
parallel=True,
status_update_callback=no_status_updates)
input_file_basename = splitext(split(self.test_data['seqs'][0])[1])[0]
otu_map_fp = join(self.test_out, 'uclust_picked_otus',
'%s_otus.txt' % input_file_basename)
alignment_fp = join(self.test_out, 'pynast_aligned_seqs',
'%s_rep_set_aligned.fasta' % input_file_basename)
failures_fp = join(self.test_out, 'pynast_aligned_seqs',
'%s_rep_set_failures.fasta' % input_file_basename)
taxonomy_assignments_fp = join(
self.test_out, 'uclust_assigned_taxonomy',
'%s_rep_set_tax_assignments.txt' % input_file_basename)
otu_table_fp = join(self.test_out, 'otu_table.biom')
tree_fp = join(self.test_out, 'rep_set.tre')
self.assertEqual(actual_tree_fp, tree_fp)
self.assertEqual(actual_otu_table_fp, otu_table_fp)
input_seqs = LoadSeqs(self.test_data['seqs'][0],
format='fasta',
aligned=False)
# Number of OTUs falls within a range that was manually
# confirmed
otu_map_lines = list(open(otu_map_fp))
num_otus = len(otu_map_lines)
otu_map_otu_ids = [o.split()[0] for o in otu_map_lines]
self.assertEqual(num_otus, 14)
# all otus get taxonomy assignments
taxonomy_assignment_lines = list(open(taxonomy_assignments_fp))
self.assertEqual(len(taxonomy_assignment_lines), num_otus)
# number of seqs which aligned + num of seqs which failed to
# align sum to the number of OTUs
aln = LoadSeqs(alignment_fp)
failures = LoadSeqs(failures_fp, aligned=False)
self.assertTrue(aln.getNumSeqs() + failures.getNumSeqs(), num_otus)
# number of tips in the tree equals the number of sequences that
# aligned
tree = LoadTree(tree_fp)
self.assertEqual(len(tree.tips()), aln.getNumSeqs())
# parse the otu table
otu_table = parse_biom_table(open(otu_table_fp, 'U'))
expected_sample_ids = [
'f1', 'f2', 'f3', 'f4', 'p1', 'p2', 't1', 't2', 'not16S.1'
]
# sample IDs are as expected
self.assertEqualItems(otu_table.SampleIds, expected_sample_ids)
# otu ids are as expected
self.assertEqualItems(otu_table.ObservationIds, otu_map_otu_ids)
# number of sequences in the full otu table equals the number of
# input sequences
number_seqs_in_otu_table = sum(
[v.sum() for v in otu_table.iterSampleData()])
self.assertEqual(number_seqs_in_otu_table, input_seqs.getNumSeqs())
# Check that the log file is created and has size > 0
log_fp = glob(join(self.test_out, 'log*.txt'))[0]
self.assertTrue(getsize(log_fp) > 0)
def test_run_pick_de_novo_otus_parallel(self):
"""run_pick_de_novo_otus generates expected results in parallel
"""
self.params['assign_taxonomy'] = \
{'id_to_taxonomy_fp':self.test_data['refseqs_tax'][0],
'reference_seqs_fp':self.test_data['refseqs'][0]}
self.params['align_seqs'] = \
{'template_fp':self.test_data['refseqs_aligned'][0]}
self.params['filter_alignment'] = \
{'lane_mask_fp':self.test_data['refseqs_aligned_lanemask'][0]}
actual_tree_fp, actual_otu_table_fp = run_pick_de_novo_otus(
self.test_data['seqs'][0],
self.test_out,
call_commands_serially,
self.params,
self.qiime_config,
parallel=True,
status_update_callback=no_status_updates)
input_file_basename = splitext(split(self.test_data['seqs'][0])[1])[0]
otu_map_fp = join(self.test_out,'uclust_picked_otus',
'%s_otus.txt' % input_file_basename)
alignment_fp = join(self.test_out,
'pynast_aligned_seqs','%s_rep_set_aligned.fasta' %
input_file_basename)
failures_fp = join(self.test_out,
'pynast_aligned_seqs','%s_rep_set_failures.fasta' %
input_file_basename)
taxonomy_assignments_fp = join(self.test_out,
'uclust_assigned_taxonomy','%s_rep_set_tax_assignments.txt' %
input_file_basename)
otu_table_fp = join(self.test_out,'otu_table.biom')
tree_fp = join(self.test_out,'rep_set.tre')
self.assertEqual(actual_tree_fp,tree_fp)
self.assertEqual(actual_otu_table_fp,otu_table_fp)
input_seqs = LoadSeqs(self.test_data['seqs'][0],
format='fasta',
aligned=False)
# Number of OTUs falls within a range that was manually
# confirmed
otu_map_lines = list(open(otu_map_fp))
num_otus = len(otu_map_lines)
otu_map_otu_ids = [o.split()[0] for o in otu_map_lines]
self.assertEqual(num_otus,14)
# all otus get taxonomy assignments
taxonomy_assignment_lines = list(open(taxonomy_assignments_fp))
self.assertEqual(len(taxonomy_assignment_lines),num_otus)
# number of seqs which aligned + num of seqs which failed to
# align sum to the number of OTUs
aln = LoadSeqs(alignment_fp)
failures = LoadSeqs(failures_fp,aligned=False)
self.assertTrue(aln.getNumSeqs() + failures.getNumSeqs(),num_otus)
# number of tips in the tree equals the number of sequences that
# aligned
tree = LoadTree(tree_fp)
self.assertEqual(len(tree.tips()),aln.getNumSeqs())
# parse the otu table
otu_table = parse_biom_table(open(otu_table_fp,'U'))
expected_sample_ids = ['f1','f2','f3','f4','p1','p2','t1','t2','not16S.1']
# sample IDs are as expected
self.assertEqualItems(otu_table.SampleIds,expected_sample_ids)
# otu ids are as expected
self.assertEqualItems(otu_table.ObservationIds,otu_map_otu_ids)
# number of sequences in the full otu table equals the number of
# input sequences
number_seqs_in_otu_table = sum([v.sum() for v in otu_table.iterSampleData()])
self.assertEqual(number_seqs_in_otu_table,input_seqs.getNumSeqs())
# Check that the log file is created and has size > 0
log_fp = glob(join(self.test_out,'log*.txt'))[0]
self.assertTrue(getsize(log_fp) > 0)
def test_run_pick_de_novo_otus_muscle(self):
"""run_pick_de_novo_otus w muscle generates expected results
"""
self.params['assign_taxonomy'] = \
{'id_to_taxonomy_fp':self.test_data['refseqs_tax'][0],
'reference_seqs_fp':self.test_data['refseqs'][0]}
self.params['align_seqs'] = {'alignment_method':'muscle'}
self.params['filter_alignment'] = \
{'suppress_lane_mask_filter':None,
'entropy_threshold':'0.10'}
run_pick_de_novo_otus(
self.test_data['seqs'][0],
self.test_out,
call_commands_serially,
self.params,
self.qiime_config,
parallel=False,
status_update_callback=no_status_updates)
input_file_basename = splitext(split(self.test_data['seqs'][0])[1])[0]
otu_map_fp = join(self.test_out,'uclust_picked_otus',
'%s_otus.txt' % input_file_basename)
alignment_fp = join(self.test_out,
'muscle_aligned_seqs','%s_rep_set_aligned.fasta' %
input_file_basename)
taxonomy_assignments_fp = join(self.test_out,
'uclust_assigned_taxonomy','%s_rep_set_tax_assignments.txt' %
input_file_basename)
otu_table_fp = join(self.test_out,'otu_table.biom')
tree_fp = join(self.test_out,'rep_set.tre')
input_seqs = LoadSeqs(self.test_data['seqs'][0],
format='fasta',
aligned=False)
# Number of OTUs falls within a range that was manually
# confirmed
otu_map_lines = list(open(otu_map_fp))
num_otus = len(otu_map_lines)
otu_map_otu_ids = [o.split()[0] for o in otu_map_lines]
self.assertEqual(num_otus,14)
# all otus get taxonomy assignments
taxonomy_assignment_lines = list(open(taxonomy_assignments_fp))
self.assertEqual(len(taxonomy_assignment_lines),num_otus)
# all OTUs align
aln = LoadSeqs(alignment_fp)
self.assertTrue(aln.getNumSeqs(),num_otus)
# all OTUs in tree
tree = LoadTree(tree_fp)
self.assertEqual(len(tree.tips()),num_otus)
# check that the two final output files have non-zero size
self.assertTrue(getsize(tree_fp) > 0)
self.assertTrue(getsize(otu_table_fp) > 0)
# Check that the log file is created and has size > 0
log_fp = glob(join(self.test_out,'log*.txt'))[0]
self.assertTrue(getsize(log_fp) > 0)
# parse the otu table
otu_table = parse_biom_table(open(otu_table_fp,'U'))
expected_sample_ids = ['f1','f2','f3','f4','p1','p2','t1','t2','not16S.1']
# sample IDs are as expected
self.assertEqualItems(otu_table.SampleIds,expected_sample_ids)
# expected OTUs
self.assertEqualItems(otu_table.ObservationIds,otu_map_otu_ids)
# number of sequences in the full otu table equals the number of
# input sequences
number_seqs_in_otu_table = sum([v.sum() for v in otu_table.iterSampleData()])
self.assertEqual(number_seqs_in_otu_table,input_seqs.getNumSeqs())
from cogent import LoadTree
tree_f = argv[1]
taxa_f = open(argv[2], "U")
output_tree = argv[3]
taxa_depth = int(argv[4])
labels_map = {}
for line in taxa_f:
curr_label = line.split('\t')[0]
# Need to remove a number of characters that can interfere with tree loading/display
curr_taxa = ".".join(
(line.split('\t')[1].split(';')[0:taxa_depth])).replace(
' ', '').replace('(', '').replace(')',
'').replace(':', '').replace(
"'", '').replace('#', '')
labels_map[curr_label] = curr_taxa
tr = LoadTree(tree_f)
tips = tr.tips()
for curr_tip in tips:
curr_name = curr_tip.Name.replace(';', '')
curr_tip.Name = "%s;" % labels_map[curr_name]
tr.writeToFile(output_tree)
