def GenomicSprinzlParser(infile,fix_sequence=False):
"""Parser for the Genomic tRNA Database.
Assumes the file has been prepared by the following method:
1. Set all search fields to empty.
2. Check all the results fields.
3. Perform the search (this takes a while).
4. Save the results worksheet as tab-delimited text.
Note that the alignment length is supposed to be 99 bases, but not all the
sequences have been padded out with the correct number of hyphens.
"""
num_blanks = 4
first = True
for l in infile:
#skip blank lines
line = l.rstrip()
if not line:
continue
fields = line.split('\t')
if first: #label line
#for unknown reasons, some of the field headers have '.' instead
#of '0', e.g. '7.' instead of '70'.
line = line.replace('.', '0')
fields = line.split('\t')
labels = InverseDict(enumerate(fields[num_blanks:]))
first = False
offset = 0
else: #expect 3 record lines at a time
if offset == 0: #label line
info = dict(zip(GenomicFields, map(strip, fields)))
#add in the labels
info['Labels'] = labels
#convert the taxonomy from a string to a list
info['Taxonomy'] = map(strip, info['Taxonomy'].split(';'))
#convert the anticodon into RNA
info['Anticodon'] = Rna(info['Anticodon'])
#get rid of the empty fields
del info['']
elif offset == 1: #sequence line
raw_seq = ''.join(map(strip, fields))
#for some reason, there are underscores in some sequences
raw_seq = raw_seq.replace('_', '-')
if fix_sequence:
raw_seq = _fix_sequence(raw_seq)
seq = Rna(raw_seq, Info=info)
elif offset == 2: #structure line
seq.Pairing = _fix_structure(fields, seq)
yield seq
#figure out which type of line we're expecting next
offset += 1
if offset > 2:
offset = 0
def test_getSimilar(self):
"""Alignment getSimilar should get all sequences close to target seq"""
aln = self.many
x = Rna('gggggggggg')
y = Rna('----------')
#test min and max similarity ranges
result = aln.getSimilar(aln['a'],min_similarity=0.4,max_similarity=0.7)
for seq in 'cefg':
assert seq in result
assert result[seq] == aln[seq]
self.assertEqual(len(result), 4)
result = aln.getSimilar(aln['a'],min_similarity=0.95,max_similarity=1)
for seq in 'a':
assert seq in result
assert result[seq] == aln[seq]
self.assertEqual(len(result), 1)
result = aln.getSimilar(aln['a'], min_similarity=0.75, \
max_similarity=0.85)
for seq in 'bd':
assert seq in result
assert result[seq] == aln[seq]
self.assertEqual(len(result), 2)
result = aln.getSimilar(aln['a'],min_similarity=0,max_similarity=0.2)
self.assertEqual(len(result), 0)
#test some sequence transformations
transform = lambda s: s[1:4]
result = aln.getSimilar(aln['a'], min_similarity=0.5, \
transform=transform)
for seq in 'abdfg':
assert seq in result
assert result[seq] == aln[seq]
self.assertEqual(len(result), 5)
transform = lambda s: s[-3:]
result = aln.getSimilar(aln['a'], min_similarity=0.5, \
transform=transform)
for seq in 'abcde':
assert seq in result
assert result[seq] == aln[seq]
self.assertEqual(len(result), 5)
#test a different distance metric
metric = lambda x, y: x.count('g') + y.count('g')
result = aln.getSimilar(aln['a'], min_similarity=5, max_similarity=10, \
metric=metric)
for seq in 'ef':
assert seq in result
assert result[seq] == aln[seq]
self.assertEqual(len(result), 2)
#test the combination of a transform and a distance metric
aln = Alignment(dict(enumerate(map(Rna, ['aA-ac','A-aAC','aa-aa']))))
transform = lambda s: Rna(s.upper())
metric = RnaSequence.fracSameNonGaps
#first, do it without the transformation
result = aln.getSimilar(aln[0], min_similarity=0.5, metric=metric)
for seq in [0,2]:
assert seq in result
assert result[seq] == aln[seq]
self.assertEqual(len(result), 2)
result = aln.getSimilar(aln[0], min_similarity=0.8, metric=metric)
for seq in [0]:
assert seq in result
assert result[seq] == aln[seq]
self.assertEqual(len(result), 1)
#then, verify that the transform changes the results
result = aln.getSimilar(aln[0], min_similarity=0.5, metric=metric, \
transform=transform)
for seq in [0,1,2]:
assert seq in result
assert result[seq] == aln[seq]
self.assertEqual(len(result), 3)
result = aln.getSimilar(aln[0], min_similarity=0.8, metric=metric, \
transform=transform)
for seq in [0,1]:
assert seq in result
assert result[seq] == aln[seq]
self.assertEqual(len(result), 2)