def median(dnas, k):
kmers = set()
for dna in dnas:
substr_kmers = set(["".join(x) for x in window(dna, k)])
for kmer in substr_kmers:
kmers.update(set(generate_words_with_mismatches(kmer, k)))
opt_kmer = ''
opt_distance = len(dnas)*k + 1
for kmer in kmers:
if opt_distance > d(kmer, dnas, k):
opt_distance = d(kmer, dnas, k)
opt_kmer = kmer
return opt_kmer, opt_distance
def median(dnas, k):
kmers = set()
for dna in dnas:
substr_kmers = set(["".join(x) for x in window(dna, k)])
for kmer in substr_kmers:
kmers.update(set(generate_words_with_mismatches(kmer, k)))
opt_kmer = ''
opt_distance = len(dnas) * k + 1
for kmer in kmers:
if opt_distance > d(kmer, dnas, k):
opt_distance = d(kmer, dnas, k)
opt_kmer = kmer
return opt_kmer, opt_distance
def enumerate_motifs(dnas, k, d):
kmers = []
for text in dnas:
kmers.extend(["".join(x) for x in window(text, k)])
resulting_set = set()
for kmer in kmers:
for modified_kmer in generate_words_with_mismatches(kmer, d):
will_add = True
for text in dnas:
was_found = False
for fragment in ["".join(x) for x in window(text, k)]:
if hamming_distance(fragment, modified_kmer) <= d:
was_found = True
break
if not was_found:
will_add = False
break
if will_add:
resulting_set.add(modified_kmer)
return list(resulting_set)
max_count = 0
visited_kmers = set()
for kmer in kmers:
app = approx_pattern_count(text, kmer, d) + approx_pattern_count(
text, reverse_dna(kmer), d)
if app > 0 and kmer not in visited_kmers:
if kmer not in count_dict:
count_dict[kmer] = 0
count_dict[kmer] += app
if count_dict[kmer] > max_count:
max_count = count_dict[kmer]
result = [key for key in count_dict if count_dict[key] == max_count]
return result, max_count
if __name__ == '__main__':
with open('in.txt', 'r') as f:
text = f.readline().strip()
k, d = map(int, f.readline().split())
substr_kmers = set(["".join(x) for x in window(text, k)])
kmers = set()
for kmer in substr_kmers:
kmers.update(set(generate_words_with_mismatches(kmer, d)))
result, max_count = frequent_words_with_mismatch(text, kmers, d)
with open('out.txt', 'w') as f:
f.write(str(' '.join(result)))
max_count = 0
visited_kmers = set()
for kmer in kmers:
app = approx_pattern_count(text, kmer, d) + approx_pattern_count(text, reverse_dna(kmer), d)
if app > 0 and kmer not in visited_kmers:
if kmer not in count_dict:
count_dict[kmer] = 0
count_dict[kmer] += app
if count_dict[kmer] > max_count:
max_count = count_dict[kmer]
result = [key for key in count_dict if count_dict[key] == max_count]
return result, max_count
if __name__ == '__main__':
with open('in.txt', 'r') as f:
text = f.readline().strip()
k, d = map(int, f.readline().split())
substr_kmers = set(["".join(x) for x in window(text, k)])
kmers = set()
for kmer in substr_kmers:
kmers.update(set(generate_words_with_mismatches(kmer, d)))
result, max_count = frequent_words_with_mismatch(text, kmers, d)
with open('out.txt', 'w') as f:
f.write(str(' '.join(result)))
