def generate_lane_mask(infile, entropy_threshold, existing_mask=None):
""" Generates lane mask dynamically by calculating base frequencies
infile: open file object for aligned fasta file
entropy_threshold: float value that designates the percentage of entropic
positions to be removed, i.e., 0.10 means the 10% most entropic positions
are removed.
"""
aln = Alignment.from_fasta_records(parse_fasta(infile), DNA)
uncertainty = aln.position_entropies(nan_on_non_standard_chars=False)
uncertainty_sorted = sorted(uncertainty)
cutoff_index = int(
round((len(uncertainty_sorted) - 1) * (1 - entropy_threshold)))
max_uncertainty = uncertainty_sorted[cutoff_index]
# This correction is for small datasets with a small possible number of
# uncertainty values.
highest_certainty = min(uncertainty_sorted)
lane_mask = ""
for base in uncertainty:
if base >= max_uncertainty and base != highest_certainty:
lane_mask += "0"
else:
lane_mask += "1"
return lane_mask
def getResult(self, aln_path, *args, **kwargs):
"""Returns alignment from sequences.
Currently does not allow parameter tuning of program and uses
default parameters -- this is bad and should be fixed.
#TODO: allow command-line access to important aln params.
"""
module = self.Params['Module']
# standard qiime says we just consider the first word as the unique ID
# the rest of the defline of the fasta alignment often doesn't match
# the otu names in the otu table
with open(aln_path) as aln_f:
seqs = Alignment.from_fasta_records(
parse_fasta(aln_f, label_to_name=lambda x: x.split()[0]), DNA)
# This ugly little line of code lets us pass a skbio Alignment when a
# a cogent alignment is expected.
seqs.getIntMap = seqs.int_map
result = module.build_tree_from_alignment(seqs)
try:
root_method = kwargs['root_method']
if root_method == 'midpoint':
result = root_midpt(result)
elif root_method == 'tree_method_default':
pass
except KeyError:
pass
return result
def remove_outliers(seqs, num_stds, fraction_seqs_for_stats=.95):
""" remove sequences very different from the majority consensus
given aligned sequences, will:
1. calculate a majority consensus (most common symbol at each position
of the alignment);
2. compute the mean/std edit distance of each seq to the consensus;
3. discard sequences whose edit dist is greater than the cutoff, which is
defined as being `num_stds` greater than the mean.
"""
# load the alignment and compute the consensus sequence
aln = Alignment.from_fasta_records(parse_fasta(seqs), DNA)
consensus_seq = aln.majority_consensus()
# compute the hamming distance between all sequences in the alignment
# and the consensus sequence
dists_to_consensus = [s.distance(consensus_seq) for s in aln]
# compute the average and standard deviation distance from the consensus
average_distance = mean(dists_to_consensus)
std_distance = std(dists_to_consensus)
# compute the distance cutoff
dist_cutoff = average_distance + num_stds * std_distance
# for all sequences, determine if they're distance to the consensus
# is less then or equal to the cutoff distance. if so, add the sequence's
# identifier to the list of sequence identifiers to keep
seqs_to_keep = []
for seq_id, dist_to_consensus in izip(aln.ids(), dists_to_consensus):
if dist_to_consensus <= dist_cutoff:
seqs_to_keep.append(seq_id)
# filter the alignment to only keep the sequences identified in the step
# above
filtered_aln = aln.subalignment(seqs_to_keep=seqs_to_keep)
# and return the filtered alignment
return filtered_aln
def remove_outliers(seqs, num_stds, fraction_seqs_for_stats=.95):
""" remove sequences very different from the majority consensus
given aligned sequences, will:
1. calculate a majority consensus (most common symbol at each position
of the alignment);
2. compute the mean/std edit distance of each seq to the consensus;
3. discard sequences whose edit dist is greater than the cutoff, which is
defined as being `num_stds` greater than the mean.
"""
# load the alignment and compute the consensus sequence
aln = Alignment.from_fasta_records(parse_fasta(seqs), DNA)
consensus_seq = aln.majority_consensus()
# compute the hamming distance between all sequences in the alignment
# and the consensus sequence
dists_to_consensus = [s.distance(consensus_seq) for s in aln]
# compute the average and standard deviation distance from the consensus
average_distance = mean(dists_to_consensus)
std_distance = std(dists_to_consensus)
# compute the distance cutoff
dist_cutoff = average_distance + num_stds * std_distance
# for all sequences, determine if they're distance to the consensus
# is less then or equal to the cutoff distance. if so, add the sequence's
# identifier to the list of sequence identifiers to keep
seqs_to_keep = []
for seq_id, dist_to_consensus in izip(aln.ids(), dists_to_consensus):
if dist_to_consensus <= dist_cutoff:
seqs_to_keep.append(seq_id)
# filter the alignment to only keep the sequences identified in the step
# above
filtered_aln = aln.subalignment(seqs_to_keep=seqs_to_keep)
# and return the filtered alignment
return filtered_aln
def getResult(self, aln_path, *args, **kwargs):
"""Returns alignment from sequences.
Currently does not allow parameter tuning of program and uses
default parameters -- this is bad and should be fixed.
#TODO: allow command-line access to important aln params.
"""
module = self.Params['Module']
# standard qiime says we just consider the first word as the unique ID
# the rest of the defline of the fasta alignment often doesn't match
# the otu names in the otu table
with open(aln_path) as aln_f:
seqs = Alignment.from_fasta_records(
parse_fasta(aln_f, label_to_name=lambda x: x.split()[0]),
DNA)
# This ugly little line of code lets us pass a skbio Alignment when a
# a cogent alignment is expected.
seqs.getIntMap = seqs.int_map
result = module.build_tree_from_alignment(seqs)
try:
root_method = kwargs['root_method']
if root_method == 'midpoint':
result = root_midpt(result)
elif root_method == 'tree_method_default':
pass
except KeyError:
pass
return result
def generate_lane_mask(infile, entropy_threshold, existing_mask=None):
""" Generates lane mask dynamically by calculating base frequencies
infile: open file object for aligned fasta file
entropy_threshold: float value that designates the percentage of entropic
positions to be removed, i.e., 0.10 means the 10% most entropic positions
are removed.
"""
aln = Alignment.from_fasta_records(parse_fasta(infile), DNA)
uncertainty = aln.position_entropies(nan_on_non_standard_chars=False)
uncertainty_sorted = sorted(uncertainty)
cutoff_index = int(round((len(uncertainty_sorted) - 1) *
(1 - entropy_threshold)))
max_uncertainty = uncertainty_sorted[cutoff_index]
# This correction is for small datasets with a small possible number of
# uncertainty values.
highest_certainty = min(uncertainty_sorted)
lane_mask = ""
for base in uncertainty:
if base >= max_uncertainty and base != highest_certainty:
lane_mask += "0"
else:
lane_mask += "1"
return lane_mask
def __call__(self,
seq_path,
result_path=None,
log_path=None,
failure_path=None):
# load candidate sequences
seq_file = open(seq_path, 'U')
candidate_sequences = parse_fasta(seq_file)
# load template sequences
template_alignment = []
template_alignment_fp = self.Params['template_filepath']
for seq_id, seq in parse_fasta(open(template_alignment_fp)):
# replace '.' characters with '-' characters
template_alignment.append((seq_id, seq.replace('.', '-').upper()))
template_alignment = Alignment.from_fasta_records(template_alignment,
DNASequence,
validate=True)
# initialize_logger
logger = NastLogger(log_path)
# get function for pairwise alignment method
pairwise_alignment_f = pairwise_alignment_methods[
self.Params['pairwise_alignment_method']]
pynast_aligned, pynast_failed = pynast_seqs(
candidate_sequences,
template_alignment,
min_pct=self.Params['min_pct'],
min_len=self.Params['min_len'],
align_unaligned_seqs_f=pairwise_alignment_f,
logger=logger,
temp_dir=get_qiime_temp_dir())
logger.record(str(self))
for i, seq in enumerate(pynast_failed):
skb_seq = DNASequence(str(seq), id=seq.Name)
pynast_failed[i] = skb_seq
pynast_failed = SequenceCollection(pynast_failed)
for i, seq in enumerate(pynast_aligned):
skb_seq = DNASequence(str(seq), id=seq.Name)
pynast_aligned[i] = skb_seq
pynast_aligned = Alignment(pynast_aligned)
if failure_path is not None:
fail_file = open(failure_path, 'w')
fail_file.write(pynast_failed.to_fasta())
fail_file.close()
if result_path is not None:
result_file = open(result_path, 'w')
result_file.write(pynast_aligned.to_fasta())
result_file.close()
return None
else:
return pynast_aligned
def __call__(self, seq_path, result_path=None, log_path=None,
failure_path=None):
# load candidate sequences
seq_file = open(seq_path, 'U')
candidate_sequences = parse_fasta(seq_file)
# load template sequences
template_alignment = []
template_alignment_fp = self.Params['template_filepath']
for seq_id, seq in parse_fasta(open(template_alignment_fp)):
# replace '.' characters with '-' characters
template_alignment.append((seq_id, seq.replace('.', '-').upper()))
template_alignment = Alignment.from_fasta_records(
template_alignment, DNASequence, validate=True)
# initialize_logger
logger = NastLogger(log_path)
# get function for pairwise alignment method
pairwise_alignment_f = pairwise_alignment_methods[
self.Params['pairwise_alignment_method']]
pynast_aligned, pynast_failed = pynast_seqs(
candidate_sequences,
template_alignment,
min_pct=self.Params['min_pct'],
min_len=self.Params['min_len'],
align_unaligned_seqs_f=pairwise_alignment_f,
logger=logger,
temp_dir=get_qiime_temp_dir())
logger.record(str(self))
for i, seq in enumerate(pynast_failed):
skb_seq = DNASequence(str(seq), id=seq.Name)
pynast_failed[i] = skb_seq
pynast_failed = SequenceCollection(pynast_failed)
for i, seq in enumerate(pynast_aligned):
skb_seq = DNASequence(str(seq), id=seq.Name)
pynast_aligned[i] = skb_seq
pynast_aligned = Alignment(pynast_aligned)
if failure_path is not None:
fail_file = open(failure_path, 'w')
fail_file.write(pynast_failed.to_fasta())
fail_file.close()
if result_path is not None:
result_file = open(result_path, 'w')
result_file.write(pynast_aligned.to_fasta())
result_file.close()
return None
else:
return pynast_aligned
def apply_lane_mask_and_gap_filter(fastalines,
mask,
allowed_gap_frac=1. - 1e-6,
entropy_threshold=None):
"""Applies a mask and gap filter to fasta file, yielding filtered seqs."""
# load the alignment
aln = Alignment.from_fasta_records(parse_fasta(fastalines), DNA)
# build the entropy mask
if mask is None and entropy_threshold is None:
if allowed_gap_frac < 1:
aln = aln.omit_gap_positions(allowed_gap_frac)
elif mask is not None and entropy_threshold is not None:
raise ValueError('only mask or entropy threshold can be provided.')
elif mask is not None:
# a pre-computed mask (e.g., Lane mask) was provided, so apply that
# and then remove highly gapped positions (gap positions have to be
# removed after the mask-based filtering so that the positions in the
# mask correspond with the positions in the alignment at the time of
# filtering)
entropy_mask = mask_to_positions(mask)
aln = aln.subalignment(positions_to_keep=entropy_mask)
if allowed_gap_frac < 1:
aln = aln.omit_gap_positions(allowed_gap_frac)
elif entropy_threshold is not None:
# a mask is being computed on the fly to filter the entropy_threshold
# most entropic positions. if highly gapped positions are being omitted
# those are filtered first, so the entropy scores for those positions
# aren't included when determining the entropy threshold (since the
# positions that are mostly gaps will be counted as a lot of low
# entropy positions)
if not (0 <= entropy_threshold <= 1):
raise ValueError('entropy_threshold needs to be between 0 and 1'
' (inclusive)')
if allowed_gap_frac < 1:
aln = aln.omit_gap_positions(allowed_gap_frac)
entropy_mask = generate_lane_mask(aln, entropy_threshold)
entropy_mask = mask_to_positions(entropy_mask)
aln = aln.subalignment(positions_to_keep=entropy_mask)
else:
# it shouldn't be possible to get here
raise ValueError("Can't resolve parameters for "
"apply_lane_mask_and_gap_filter.")
if aln.sequence_length() == 0:
raise ValueError("Positional filtering resulted in removal of all "
"alignment positions.")
for seq in aln:
yield ">%s\n" % seq.id
yield "%s\n" % seq
def apply_lane_mask_and_gap_filter(fastalines, mask,
allowed_gap_frac=1.-1e-6,
entropy_threshold=None):
"""Applies a mask and gap filter to fasta file, yielding filtered seqs."""
# load the alignment
aln = Alignment.from_fasta_records(parse_fasta(fastalines), DNA)
# build the entropy mask
if mask is None and entropy_threshold is None:
if allowed_gap_frac < 1:
aln = aln.omit_gap_positions(allowed_gap_frac)
elif mask is not None and entropy_threshold is not None:
raise ValueError('only mask or entropy threshold can be provided.')
elif mask is not None:
# a pre-computed mask (e.g., Lane mask) was provided, so apply that
# and then remove highly gapped positions (gap positions have to be
# removed after the mask-based filtering so that the positions in the
# mask correspond with the positions in the alignment at the time of
# filtering)
entropy_mask = mask_to_positions(mask)
aln = aln.subalignment(positions_to_keep=entropy_mask)
if allowed_gap_frac < 1:
aln = aln.omit_gap_positions(allowed_gap_frac)
elif entropy_threshold is not None:
# a mask is being computed on the fly to filter the entropy_threshold
# most entropic positions. if highly gapped positions are being omitted
# those are filtered first, so the entropy scores for those positions
# aren't included when determining the entropy threshold (since the
# positions that are mostly gaps will be counted as a lot of low
# entropy positions)
if not (0 <= entropy_threshold <= 1):
raise ValueError('entropy_threshold needs to be between 0 and 1'
' (inclusive)')
if allowed_gap_frac < 1:
aln = aln.omit_gap_positions(allowed_gap_frac)
entropy_mask = generate_lane_mask(aln, entropy_threshold)
entropy_mask = mask_to_positions(entropy_mask)
aln = aln.subalignment(positions_to_keep=entropy_mask)
else:
# it shouldn't be possible to get here
raise ValueError("Can't resolve parameters for "
"apply_lane_mask_and_gap_filter.")
if aln.sequence_length() == 0:
raise ValueError("Positional filtering resulted in removal of all "
"alignment positions.")
for seq in aln:
yield ">%s\n" % seq.id
yield "%s\n" % seq
def test_call_infernal_test1_file_output(self):
"""InfernalAligner writes correct output files for infernal_test1 seqs
"""
# do not collect results; check output files instead
actual = self.infernal_test1_aligner(
self.infernal_test1_input_fp, result_path=self.result_fp,
log_path=self.log_fp)
self.assertTrue(actual is None,
"Result should be None when result path provided.")
expected_aln = self.infernal_test1_expected_aln
with open(self.result_fp) as result_f:
actual_aln = Alignment.from_fasta_records(parse_fasta(
result_f), DNA)
self.assertEqual(actual_aln, expected_aln)
def test_call_infernal_test1_file_output(self):
"""InfernalAligner writes correct output files for infernal_test1 seqs
"""
# do not collect results; check output files instead
actual = self.infernal_test1_aligner(self.infernal_test1_input_fp,
result_path=self.result_fp,
log_path=self.log_fp)
self.assertTrue(actual is None,
"Result should be None when result path provided.")
expected_aln = self.infernal_test1_expected_aln
with open(self.result_fp) as result_f:
actual_aln = Alignment.from_fasta_records(parse_fasta(result_f),
DNA)
self.assertEqual(actual_aln, expected_aln)
def test_call_pynast_test1_file_output(self):
"""PyNastAligner writes correct output files for pynast_test1 seqs
"""
# do not collect results; check output files instead
actual = self.pynast_test1_aligner(
self.pynast_test1_input_fp, result_path=self.result_fp,
log_path=self.log_fp, failure_path=self.failure_fp)
self.assertTrue(actual is None,
"Result should be None when result path provided.")
expected_aln = self.pynast_test1_expected_aln
with open(self.result_fp) as result_f:
actual_aln = Alignment.from_fasta_records(parse_fasta(
result_f), DNA)
self.assertEqual(actual_aln, expected_aln)
with open(self.failure_fp) as failure_f:
actual_fail = SequenceCollection.from_fasta_records(
parse_fasta(failure_f), DNA)
self.assertEqual(actual_fail.to_fasta(),
self.pynast_test1_expected_fail.to_fasta())
def test_call_pynast_test1_file_output(self):
"""PyNastAligner writes correct output files for pynast_test1 seqs
"""
# do not collect results; check output files instead
actual = self.pynast_test1_aligner(
self.pynast_test1_input_fp, result_path=self.result_fp,
log_path=self.log_fp, failure_path=self.failure_fp)
self.assertTrue(actual is None,
"Result should be None when result path provided.")
expected_aln = self.pynast_test1_expected_aln
with open(self.result_fp) as result_f:
actual_aln = Alignment.from_fasta_records(parse_fasta(
result_f), DNA)
self.assertEqual(actual_aln, expected_aln)
with open(self.failure_fp) as failure_f:
actual_fail = SequenceCollection.from_fasta_records(
parse_fasta(failure_f), DNA)
self.assertEqual(actual_fail.to_fasta(),
self.pynast_test1_expected_fail.to_fasta())
def setUp(self):
fd, self.infernal_test1_input_fp = mkstemp(
prefix='InfernalAlignerTests_', suffix='.fasta')
close(fd)
with open(self.infernal_test1_input_fp, 'w') as in_f:
in_f.write('\n'.join(infernal_test1_input_fasta))
fd, self.infernal_test1_template_fp = mkstemp(
prefix='InfernalAlignerTests_', suffix='template.sto')
close(fd)
with open(self.infernal_test1_template_fp, 'w') as in_f:
in_f.write(infernal_test1_template_stockholm)
# create temp file names (and touch them so we can reliably
# clean them up)
fd, self.result_fp = mkstemp(
prefix='InfernalAlignerTests_', suffix='.fasta')
close(fd)
open(self.result_fp, 'w').close()
fd, self.log_fp = mkstemp(
prefix='InfernalAlignerTests_', suffix='.log')
close(fd)
open(self.log_fp, 'w').close()
self._paths_to_clean_up = [
self.infernal_test1_input_fp,
self.result_fp,
self.log_fp,
self.infernal_test1_template_fp,
]
self.infernal_test1_aligner = InfernalAligner({
'template_filepath': self.infernal_test1_template_fp,
})
self.infernal_test1_expected_aln = Alignment.from_fasta_records(
parse_fasta(infernal_test1_expected_alignment),
DNA)
def setUp(self):
fd, self.infernal_test1_input_fp = mkstemp(
prefix='InfernalAlignerTests_', suffix='.fasta')
close(fd)
with open(self.infernal_test1_input_fp, 'w') as in_f:
in_f.write('\n'.join(infernal_test1_input_fasta))
fd, self.infernal_test1_template_fp = mkstemp(
prefix='InfernalAlignerTests_', suffix='template.sto')
close(fd)
with open(self.infernal_test1_template_fp, 'w') as in_f:
in_f.write(infernal_test1_template_stockholm)
# create temp file names (and touch them so we can reliably
# clean them up)
fd, self.result_fp = mkstemp(prefix='InfernalAlignerTests_',
suffix='.fasta')
close(fd)
open(self.result_fp, 'w').close()
fd, self.log_fp = mkstemp(prefix='InfernalAlignerTests_',
suffix='.log')
close(fd)
open(self.log_fp, 'w').close()
self._paths_to_clean_up = [
self.infernal_test1_input_fp,
self.result_fp,
self.log_fp,
self.infernal_test1_template_fp,
]
self.infernal_test1_aligner = InfernalAligner({
'template_filepath':
self.infernal_test1_template_fp,
})
self.infernal_test1_expected_aln = Alignment.from_fasta_records(
parse_fasta(infernal_test1_expected_alignment), DNA)
def setUp(self):
fd, self.pynast_test1_input_fp = mkstemp(prefix='PyNastAlignerTests_',
suffix='.fasta')
close(fd)
with open(self.pynast_test1_input_fp, 'w') as f:
f.write(pynast_test1_input_fasta)
fd, self.pynast_test1_template_fp = mkstemp(
prefix='PyNastAlignerTests_', suffix='template.fasta')
close(fd)
with open(self.pynast_test1_template_fp, 'w') as f:
f.write(pynast_test1_template_fasta)
fd, self.pynast_test_template_w_dots_fp = mkstemp(
prefix='PyNastAlignerTests_', suffix='template.fasta')
close(fd)
with open(self.pynast_test_template_w_dots_fp, 'w') as f:
f.write(pynast_test1_template_fasta.replace('-', '.'))
fd, self.pynast_test_template_w_u_fp = mkstemp(
prefix='PyNastAlignerTests_', suffix='template.fasta')
close(fd)
with open(self.pynast_test_template_w_u_fp, 'w') as f:
f.write(pynast_test1_template_fasta.replace('T', 'U'))
fd, self.pynast_test_template_w_lower_fp = mkstemp(
prefix='PyNastAlignerTests_', suffix='template.fasta')
close(fd)
with open(self.pynast_test_template_w_lower_fp, 'w') as f:
f.write(pynast_test1_template_fasta.lower())
# create temp file names (and touch them so we can reliably
# clean them up)
fd, self.result_fp = mkstemp(prefix='PyNastAlignerTests_',
suffix='.fasta')
close(fd)
open(self.result_fp, 'w').close()
fd, self.failure_fp = mkstemp(prefix='PyNastAlignerTests_',
suffix='.fasta')
close(fd)
open(self.failure_fp, 'w').close()
fd, self.log_fp = mkstemp(prefix='PyNastAlignerTests_', suffix='.log')
close(fd)
open(self.log_fp, 'w').close()
self._paths_to_clean_up = [
self.pynast_test1_input_fp, self.result_fp, self.failure_fp,
self.log_fp, self.pynast_test1_template_fp,
self.pynast_test_template_w_dots_fp,
self.pynast_test_template_w_u_fp,
self.pynast_test_template_w_lower_fp
]
self.pynast_test1_aligner = PyNastAligner({
'template_filepath': self.pynast_test1_template_fp,
'min_len': 15,
})
self.pynast_test1_expected_aln = Alignment.from_fasta_records(
parse_fasta(pynast_test1_expected_alignment), DNA)
self.pynast_test1_expected_fail = SequenceCollection.from_fasta_records(
parse_fasta(pynast_test1_expected_failure), DNA)
def setUp(self):
fd, self.pynast_test1_input_fp = mkstemp(
prefix='PyNastAlignerTests_', suffix='.fasta')
close(fd)
with open(self.pynast_test1_input_fp, 'w') as f:
f.write(pynast_test1_input_fasta)
fd, self.pynast_test1_template_fp = mkstemp(
prefix='PyNastAlignerTests_', suffix='template.fasta')
close(fd)
with open(self.pynast_test1_template_fp, 'w') as f:
f.write(pynast_test1_template_fasta)
fd, self.pynast_test_template_w_dots_fp = mkstemp(
prefix='PyNastAlignerTests_', suffix='template.fasta')
close(fd)
with open(self.pynast_test_template_w_dots_fp, 'w') as f:
f.write(pynast_test1_template_fasta.replace('-', '.'))
fd, self.pynast_test_template_w_u_fp = mkstemp(
prefix='PyNastAlignerTests_', suffix='template.fasta')
close(fd)
with open(self.pynast_test_template_w_u_fp, 'w') as f:
f.write(pynast_test1_template_fasta.replace('T', 'U'))
fd, self.pynast_test_template_w_lower_fp = mkstemp(
prefix='PyNastAlignerTests_', suffix='template.fasta')
close(fd)
with open(self.pynast_test_template_w_lower_fp, 'w') as f:
f.write(pynast_test1_template_fasta.lower())
# create temp file names (and touch them so we can reliably
# clean them up)
fd, self.result_fp = mkstemp(
prefix='PyNastAlignerTests_', suffix='.fasta')
close(fd)
open(self.result_fp, 'w').close()
fd, self.failure_fp = mkstemp(
prefix='PyNastAlignerTests_', suffix='.fasta')
close(fd)
open(self.failure_fp, 'w').close()
fd, self.log_fp = mkstemp(
prefix='PyNastAlignerTests_', suffix='.log')
close(fd)
open(self.log_fp, 'w').close()
self._paths_to_clean_up = [
self.pynast_test1_input_fp,
self.result_fp,
self.failure_fp,
self.log_fp,
self.pynast_test1_template_fp,
self.pynast_test_template_w_dots_fp,
self.pynast_test_template_w_u_fp,
self.pynast_test_template_w_lower_fp
]
self.pynast_test1_aligner = PyNastAligner({
'template_filepath': self.pynast_test1_template_fp,
'min_len': 15,
})
self.pynast_test1_expected_aln = Alignment.from_fasta_records(
parse_fasta(pynast_test1_expected_alignment),
DNA)
self.pynast_test1_expected_fail = SequenceCollection.from_fasta_records(
parse_fasta(pynast_test1_expected_failure), DNA)
def __call__(self, seq_path, result_path=None, log_path=None,
failure_path=None, cmbuild_params=None, cmalign_params=None):
log_params = []
# load candidate sequences
candidate_sequences = dict(parse_fasta(open(seq_path, 'U')))
# load template sequences
try:
info, template_alignment, struct = list(MinimalRfamParser(open(
self.Params['template_filepath'], 'U'),
seq_constructor=ChangedSequence))[0]
except RecordError:
raise ValueError(
"Template alignment must be in Stockholm format with corresponding secondary structure annotation when using InfernalAligner.")
# Need to make separate mapping for unaligned sequences
unaligned = SequenceCollection.from_fasta_records(
candidate_sequences.iteritems(), DNASequence)
mapped_seqs, new_to_old_ids = unaligned.int_map(prefix='unaligned_')
mapped_seq_tuples = [(k, str(v)) for k,v in mapped_seqs.iteritems()]
# Turn on --gapthresh option in cmbuild to force alignment to full
# model
if cmbuild_params is None:
cmbuild_params = {}
cmbuild_params.update({'--gapthresh': 1.0})
# record cmbuild parameters
log_params.append('cmbuild parameters:')
log_params.append(str(cmbuild_params))
# Turn on --sub option in Infernal, since we know the unaligned sequences
# are fragments.
# Also turn on --gapthresh to use same gapthresh as was used to build
# model
if cmalign_params is None:
cmalign_params = {}
cmalign_params.update({'--sub': True, '--gapthresh': 1.0})
# record cmalign parameters
log_params.append('cmalign parameters:')
log_params.append(str(cmalign_params))
# Align sequences to alignment including alignment gaps.
aligned, struct_string = cmalign_from_alignment(aln=template_alignment,
structure_string=struct,
seqs=mapped_seq_tuples,
include_aln=True,
params=cmalign_params,
cmbuild_params=cmbuild_params)
# Pull out original sequences from full alignment.
infernal_aligned = []
# Get a dict of the ids to sequences (note that this is a
# cogent alignment object, hence the call to NamedSeqs)
aligned_dict = aligned.NamedSeqs
for n, o in new_to_old_ids.iteritems():
aligned_seq = aligned_dict[n]
infernal_aligned.append((o, aligned_seq))
# Create an Alignment object from alignment dict
infernal_aligned = Alignment.from_fasta_records(infernal_aligned, DNASequence)
if log_path is not None:
log_file = open(log_path, 'w')
log_file.write('\n'.join(log_params))
log_file.close()
if result_path is not None:
result_file = open(result_path, 'w')
result_file.write(infernal_aligned.to_fasta())
result_file.close()
return None
else:
try:
return infernal_aligned
except ValueError:
return {}
def __call__(self, seq_path, result_path=None, log_path=None,
failure_path=None, cmbuild_params=None, cmalign_params=None):
log_params = []
# load candidate sequences
candidate_sequences = dict(parse_fasta(open(seq_path, 'U')))
# load template sequences
try:
info, template_alignment, struct = list(MinimalRfamParser(open(
self.Params['template_filepath'], 'U'),
seq_constructor=ChangedSequence))[0]
except RecordError:
raise ValueError(
"Template alignment must be in Stockholm format with corresponding secondary structure annotation when using InfernalAligner.")
# Need to make separate mapping for unaligned sequences
unaligned = SequenceCollection.from_fasta_records(
candidate_sequences.iteritems(), DNASequence)
mapped_seqs, new_to_old_ids = unaligned.int_map(prefix='unaligned_')
mapped_seq_tuples = [(k, str(v)) for k,v in mapped_seqs.iteritems()]
# Turn on --gapthresh option in cmbuild to force alignment to full
# model
if cmbuild_params is None:
cmbuild_params = {}
cmbuild_params.update({'--gapthresh': 1.0})
# record cmbuild parameters
log_params.append('cmbuild parameters:')
log_params.append(str(cmbuild_params))
# Turn on --sub option in Infernal, since we know the unaligned sequences
# are fragments.
# Also turn on --gapthresh to use same gapthresh as was used to build
# model
if cmalign_params is None:
cmalign_params = {}
cmalign_params.update({'--sub': True, '--gapthresh': 1.0})
# record cmalign parameters
log_params.append('cmalign parameters:')
log_params.append(str(cmalign_params))
# Align sequences to alignment including alignment gaps.
aligned, struct_string = cmalign_from_alignment(aln=template_alignment,
structure_string=struct,
seqs=mapped_seq_tuples,
include_aln=True,
params=cmalign_params,
cmbuild_params=cmbuild_params)
# Pull out original sequences from full alignment.
infernal_aligned = []
# Get a dict of the ids to sequences (note that this is a
# cogent alignment object, hence the call to NamedSeqs)
aligned_dict = aligned.NamedSeqs
for n, o in new_to_old_ids.iteritems():
aligned_seq = aligned_dict[n]
infernal_aligned.append((o, aligned_seq))
# Create an Alignment object from alignment dict
infernal_aligned = Alignment.from_fasta_records(infernal_aligned, DNASequence)
if log_path is not None:
log_file = open(log_path, 'w')
log_file.write('\n'.join(log_params))
log_file.close()
if result_path is not None:
result_file = open(result_path, 'w')
result_file.write(infernal_aligned.to_fasta())
result_file.close()
return None
else:
try:
return infernal_aligned
except ValueError:
return {}
