def min_hamming_distance(pattern, dna):
k = len(pattern)
min_dist = len(dna)
for i in range(len(dna)-k+1):
if(hamming_distance(pattern, dna[i:i+k]) < min_dist):
min_dist = hamming_distance(pattern, dna[i:i+k])
return min_dist
def motif_enumeration(dna, k, d):
patterns = []
num_dna = len(dna)
for seq in dna:
len_seq = len(seq)
for i in range(len_seq-k+1):
pattern = seq[i:i+k]
neighbourhood = neighbours(pattern, d)
for pattern_dash in neighbourhood:
count=0
for seq_comp in dna:
len_seq_c = len(seq_comp)
for j in range(len_seq_c-k+1):
if hamming_distance(pattern_dash, seq_comp[j:j+k])<=d:
count=count+1
break
if(count == num_dna):
patterns.append(pattern_dash)
final_patterns=[]
for pattern in patterns:
if pattern not in final_patterns:
final_patterns.append(pattern)
return final_patterns
def approximate_pattern_matching(pattern, dna, d):
start_positions = []
p_len = len(pattern)
for i in range(len(dna) - p_len + 1):
pattern_dna = dna[i:i + p_len]
if (hamming_distance(pattern, pattern_dna) <= d):
start_positions.append(i)
return start_positions