def approx_pattern_count(text, pattern, d):
count = 0
pattern_len = len(pattern)
for i in range(len(text) - (pattern_len-1)):
substr = text[i:i+pattern_len]
if hamming_distance(pattern, substr) <= d:
count += 1
return count
def hamming_in_all(dna_strings, kmer, d):
for dna in dna_strings:
k = len(kmer)
found_match = False
for j in xrange(len(dna) - k + 1):
kmer2 = dna[j:j + k]
if hamming_distance(kmer, kmer2) <= d:
found_match = True
break
if not found_match:
return False
return True
def neighbours(pattern, d):
nucleotides = ['A', 'C', 'G', 'T']
if d == 0:
return {pattern}
if len(pattern) == 1:
return set(nucleotides)
neighbourhood = set()
suffix_pattern = pattern[1:]
suffix_neighbours = neighbours(suffix_pattern, d)
for t in suffix_neighbours:
if hamming_distance(suffix_pattern, t) < d:
for n in nucleotides:
neighbourhood.add(n + t)
else:
neighbourhood.add(pattern[0] + t)
return neighbourhood
