def test_remove_outliers(self):
aln = [
'>ACT009', 'ACAT-',
'>ACT019', 'GACT-',
'>ACT_02', 'GACT-',
'>ACT_03', 'AACT-',
'>ACT_04', 'AACT-',
'>ACT_05', 'AACT-',
'>ACT011', 'CTGGC',
'>hello', 'AACTG',
]
# mean errors is 10/9 .
seqnames = []
for elem in aln:
if elem.startswith('>'):
seqnames.append(elem[1:])
seqs = []
for elem in aln:
if not elem.startswith('>'):
seqs.append(elem)
# just remove ACT011
res = remove_outliers(aln, 2)
self.assertEqual(res.sequence_count(), 7)
for seqname_left in res.ids():
self.assertTrue(seqname_left in seqnames)
self.assertTrue('ACT011' not in res.ids())
# now remove all that deviate have > 10/9 (2 or more) substitutions:
res = remove_outliers(aln, 0)
self.assertEqual(res.sequence_count(), 6)
for seqname_left in res.ids():
self.assertTrue(seqname_left in seqnames)
self.assertTrue('ACT011' not in res.ids())
self.assertTrue('ACT009' not in res.ids())
def test_remove_outliers(self):
aln = [
'>ACT009', 'ACAT-',
'>ACT019', 'GACT-',
'>ACT_02', 'GACT-',
'>ACT_03', 'AACT-',
'>ACT_04', 'AACT-',
'>ACT_05', 'AACT-',
'>ACT011', 'CTGGC',
'>hello', 'AACTG',
]
# mean errors is 10/9 .
seqnames = []
for elem in aln:
if elem.startswith('>'):
seqnames.append(elem[1:])
seqs = []
for elem in aln:
if not elem.startswith('>'):
seqs.append(elem)
# just remove ACT011
res = remove_outliers(aln, 2)
self.assertEqual(res.sequence_count(), 7)
for seqname_left in res.ids():
self.assertTrue(seqname_left in seqnames)
self.assertTrue('ACT011' not in res.ids())
# now remove all that deviate have > 10/9 (2 or more) substitutions:
res = remove_outliers(aln, 0)
self.assertEqual(res.sequence_count(), 6)
for seqname_left in res.ids():
self.assertTrue(seqname_left in seqnames)
self.assertTrue('ACT011' not in res.ids())
self.assertTrue('ACT009' not in res.ids())
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
# build the output filepath and open it any problems can be caught
# before starting the work
try:
mkdir(opts.output_dir)
except OSError:
pass
input_dir, input_filename = split(opts.input_fasta_file)
input_basename, ext = splitext(input_filename)
if getsize(opts.input_fasta_file) == 0:
raise ValueError("An empty fasta file was provided. "
"Did the alignment complete sucessfully? "
"Did PyNAST discard all sequences due to too-stringent minimum length "
"or minimum percent ID settings?")
output_fp = '%s/%s_pfiltered.fasta' % (opts.output_dir, input_basename)
try:
outfile = open(output_fp, 'w')
except IOError:
raise IOError("Can't open output_filepath for writing: %s"
% output_filepath)
if not opts.suppress_lane_mask_filter and not opts.entropy_threshold:
if opts.lane_mask_fp is not None:
lane_mask = open(opts.lane_mask_fp, 'U').read().strip()
else:
lane_mask = get_template_alignment_column_mask()
else:
lane_mask = None
# open the input and output files
infile = open(opts.input_fasta_file, 'U')
if opts.remove_outliers:
# apply the lanemask/gap removal, then remove outliers
seq_gen = apply_lane_mask_and_gap_filter(infile, lane_mask,
opts.allowed_gap_frac,
entropy_threshold=opts.entropy_threshold)
filtered_aln = remove_outliers(seq_gen, opts.threshold)
for seq in filtered_aln:
outfile.write(seq.to_fasta())
outfile.write('\n')
else:
# just apply the lanemask/gap removal
for result in apply_lane_mask_and_gap_filter(infile, lane_mask,
opts.allowed_gap_frac,
entropy_threshold=opts.entropy_threshold):
outfile.write(result)
infile.close()
outfile.close()
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
# build the output filepath and open it any problems can be caught
# before starting the work
try:
mkdir(opts.output_dir)
except OSError:
pass
input_dir, input_filename = split(opts.input_fasta_file)
input_basename, ext = splitext(input_filename)
if getsize(opts.input_fasta_file) == 0:
raise ValueError("An empty fasta file was provided. "
"Did the alignment complete sucessfully? "
"Did PyNAST discard all sequences due to too-stringent minimum length "
"or minimum percent ID settings?")
output_fp = '%s/%s_pfiltered.fasta' % (opts.output_dir, input_basename)
try:
outfile = open(output_fp, 'w')
except IOError:
raise IOError("Can't open output_filepath for writing: %s"
% output_filepath)
if opts.lane_mask_fp and not opts.suppress_lane_mask_filter and not\
opts.entropy_threshold:
# read the lane_mask, if one was provided
if opts.verbose:
print "Reading lane mask..."
lane_mask = open(opts.lane_mask_fp).read().strip()
else:
lane_mask = None
# open the input and output files
infile = open(opts.input_fasta_file, 'U')
if opts.remove_outliers:
# apply the lanemask/gap removal, then remove outliers
seq_gen = apply_lane_mask_and_gap_filter(infile, lane_mask,
opts.allowed_gap_frac, verbose=opts.verbose,
entropy_threshold=opts.entropy_threshold)
filtered_aln = remove_outliers(seq_gen, opts.threshold)
for seq in filtered_aln.Seqs:
outfile.write(seq.toFasta())
outfile.write('\n')
else:
# just apply the lanemask/gap removal
for result in apply_lane_mask_and_gap_filter(infile, lane_mask,
opts.allowed_gap_frac, verbose=opts.verbose,
entropy_threshold=opts.entropy_threshold):
outfile.write(result)
infile.close()
outfile.close()
def test_remove_outliers(self):
""" remove outliers returns only seqs similar to consensus"""
aln = [
'>ACT009',
'ACAT-',
'>ACT019',
'GACT-',
'>ACT_02',
'GACT-',
'>ACT_03',
'AACT-',
'>ACT_04',
'AACT-',
'>ACT_05',
'AACT-',
'>ACT011',
'CTGGC',
'>hello',
'AACTG',
]
# mean errors is 10/9 .
seqnames = []
for elem in aln:
if elem.startswith('>'):
seqnames.append(elem[1:])
seqs = []
for elem in aln:
if not elem.startswith('>'):
seqs.append(elem)
# just remove ACT011
res = remove_outliers(aln, 2)
self.assertEqual(len(res.getSeqNames()), 7)
for seqname_left in res.getSeqNames():
self.assertTrue(seqname_left in seqnames)
self.assertTrue('ACT011' not in res.getSeqNames())
# now remove all that deviate have > 10/9 (2 or more) substitutions:
res = remove_outliers(aln, 0)
self.assertEqual(len(res.getSeqNames()), 6)
for seqname_left in res.getSeqNames():
self.assertTrue(seqname_left in seqnames)
self.assertTrue('ACT011' not in res.getSeqNames())
self.assertTrue('ACT009' not in res.getSeqNames())
def test_remove_outliers(self):
""" remove outliers returns only seqs similar to consensus"""
aln = [\
'>ACT009','ACAT-',
'>ACT019','GACT-',
'>ACT_02','GACT-',
'>ACT_03','AACT-',
'>ACT_04','AACT-',
'>ACT_05','AACT-',
'>ACT011','CTGGC',
'>hello', 'AACTG',
]
# mean errors is 10/9 .
seqnames = []
for elem in aln:
if elem.startswith('>'):
seqnames.append (elem[1:])
seqs = []
for elem in aln:
if not elem.startswith('>'):
seqs.append(elem)
# just remove ACT011
res = remove_outliers(aln, 2)
self.assertEqual(len(res.getSeqNames()), 7)
for seqname_left in res.getSeqNames():
self.assertContains(seqnames, seqname_left)
self.assertNotContains(res.getSeqNames(), 'ACT011')
# now remove all that deviate have > 10/9 (2 or more) substitutions:
res = remove_outliers(aln, 0)
self.assertEqual(len(res.getSeqNames()), 6)
for seqname_left in res.getSeqNames():
self.assertContains(seqnames, seqname_left)
self.assertNotContains(res.getSeqNames(), 'ACT011')
self.assertNotContains(res.getSeqNames(), 'ACT009')
