def randomized_motif_search(dna, k, t):
motifs = []
for seq in dna:
rand_pos = random.randint(0, len(seq) - k)
motifs.append(seq[rand_pos:rand_pos + k])
best = motifs
score_best = score(best)
while True:
profile = profile_matrix(motifs, k)
motifs = [profile_most_probable(seq, k, profile) for seq in dna]
score_motifs = score(motifs)
if score_motifs < score_best:
best = motifs
score_best = score_motifs
else:
return best, score_best
def greedy_motif_search_with_pseudocounts(dna, k, t):
best = [t[:k] for t in dna]
score_best = score(best)
first = dna[0]
for i in range(len(first) - k + 1):
motif = first[i : i + k]
motifs = [motif]
for j in range(1, t):
current = dna[j]
profile = profile_matrix(motifs, k)
motifs_j = profile_most_probable(current, k, profile)
motifs.append(motifs_j)
score_motifs = score(motifs)
if score_motifs < score_best:
best = motifs
score_best = score_motifs
return best
def gibbs_sampler(dna, k, t, N):
motifs = []
for seq in dna:
rand_pos = random.randint(0, len(seq) - k)
motifs.append(seq[rand_pos:rand_pos + k])
best = motifs
score_best = score(best)
for j in range(N):
i = random.randint(0, t - 1)
motif_subset = motifs[:]
motif_subset.pop(i)
profile = profile_matrix(motif_subset, k)
motifs[i] = profile_most_probable(dna[i], k, profile)
score_motifs = score(motifs)
if score_motifs < score_best:
best = motifs
score_best = score_motifs
return best, score_best
else:
return best, score_best
def repeated_randomized_motif_search(dna, k, t, *, times=1000):
best, score_best = randomized_motif_search(dna, k, t)
for _ in range(times - 1):
new_best, new_score = randomized_motif_search(dna, k, t)
if new_score < score_best:
best = new_best
score_best = new_score
return best
result = repeated_randomized_motif_search(
"CGCCCCTCTCGGGGGTGTTCAGTAAACGGCCA GGGCGAGGTATGTGTAAGTGCCAAGGTGCCAG TAGTACCGAGACCGAAAGAAGTATACAGGCGT TAGATCAAGTTTCAGGTGCACGTCGGTGAACC AATCCACCAGCTCCACGTGCAATGTTGGCCTA"
.split(),
8,
5,
)
assert (
score(result) <=
score("TCTCGGGG CCAAGGTG TACAGGCG TTCAGGTG TCCACGTG".split()) + 1), result
if __name__ == "__main__":
with open("data/rosalind_ba2f.txt", "r") as dataset:
k, t = [int(d) for d in dataset.readline().rstrip().split()]
text = [l.rstrip() for l in dataset.readlines()]
print(*repeated_randomized_motif_search(text, k, t), sep="\n")