def approx_pattern_count(text, pattern, d):
fragment_length = len(pattern)
count = 0
for i, fragment in enumerate(["".join(x) for x in window(text, fragment_length)]):
if hamming_distance(fragment, pattern) <= d:
count += 1
return count
def approx_pattern_count(text, pattern, d):
fragment_length = len(pattern)
count = 0
for i, fragment in enumerate(
["".join(x) for x in window(text, fragment_length)]):
if hamming_distance(fragment, pattern) <= d:
count += 1
return count
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)
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 repeat_subst(text):
for k in range(len(text), 1, -1):
for kmer in ["".join(x) for x in window(text, k)]:
if len(list(find_all(kmer, text))) > 1:
return kmer
return None
from _01_07_hamming_distance import hamming_distance
from _01_02_frequent_words import window
if __name__ == '__main__':
with open('in.txt', 'r') as f:
pattern = f.readline().strip()
text = f.readline().strip()
d = int(f.readline())
fragment_length = len(pattern)
indexes = list()
for i, fragment in enumerate(["".join(x) for x in window(text, fragment_length)]):
if hamming_distance(fragment, pattern) <= d:
indexes.append(i)
with open('out.txt', 'w') as f:
f.write(' '.join(map(str, indexes)))
def common_subst(text1, text2):
for k in range(1, min([len(text1), len(text2)])):
for kmer in ["".join(x) for x in window(text1, k)]:
if kmer not in text2:
return kmer
return None
from _01_07_hamming_distance import hamming_distance
from _01_02_frequent_words import window
if __name__ == '__main__':
with open('in.txt', 'r') as f:
pattern = f.readline().strip()
text = f.readline().strip()
d = int(f.readline())
fragment_length = len(pattern)
indexes = list()
for i, fragment in enumerate(
["".join(x) for x in window(text, fragment_length)]):
if hamming_distance(fragment, pattern) <= d:
indexes.append(i)
with open('out.txt', 'w') as f:
f.write(' '.join(map(str, indexes)))
count_dict = dict()
max_count = 0
for kmer in kmers:
app = approx_pattern_count(text, kmer, d)
if app > 0:
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)))
def get_kmers(s, k):
return list(set(["".join(x) for x in window(s, k)]))
def distance(kmer, string, k):
result = k+2
for fragment in ["".join(x) for x in window(string, k)]:
if hamming_distance(kmer, fragment) < result:
result = hamming_distance(kmer, fragment)
return result
import random
from _01_02_frequent_words import window
from _03_03_profile import profile_most_probable_kmer
from _03_04_greedy import profile_most_probable_kmer_swap, score, profile
from _03_05_pseudo import profile_with_pseudocounts
if __name__ == '__main__':
with open('in.txt', 'r') as f:
k = int(f.readline())
dna = f.readline().strip()
substr_kmers = set(["".join(x) for x in window(dna, k)])
result = sorted(substr_kmers)
with open('out.txt', 'w') as f:
f.write('\n'.join(sorted(substr_kmers)))
def repeat_subst(text):
for k in range(len(text), 1, -1):
for kmer in ["".join(x) for x in window(text, k)]:
if len(list(find_all(kmer, text))) > 1:
return kmer
return None
import random
from _01_02_frequent_words import window
from _03_03_profile import profile_most_probable_kmer
from _03_04_greedy import profile_most_probable_kmer_swap, score, profile
from _03_05_pseudo import profile_with_pseudocounts
if __name__ == '__main__':
with open('in.txt', 'r') as f:
k = int(f.readline())
dna = f.readline().strip()
substr_kmers = set(["".join(x) for x in window(dna, k)])
result = sorted(substr_kmers)
with open('out.txt', 'w') as f:
f.write('\n'.join(sorted(substr_kmers)))
def common_subst(text1, text2):
for k in range(min([len(text1), len(text2)]), 1, -1):
for kmer in ["".join(x) for x in window(text1, k)]:
if kmer in text2:
return kmer
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)))
def distance(kmer, string, k):
result = k + 2
for fragment in ["".join(x) for x in window(string, k)]:
if hamming_distance(kmer, fragment) < result:
result = hamming_distance(kmer, fragment)
return result
