def randomized_motif_search(dnas, k, t, N):
rand_ints = [random.randint(0,len(dnas[0])-k) for a in range(t)]
motifs = [dnas[i][r:r+k] for i, r in enumerate(rand_ints)]
best_score = [score(motifs), motifs]
for j in range(N):
i = random.randint(0, t-1)
profile = profile_with_pseudocounts(motifs[:i-1] + motifs[i:])
motifs[i] = profile_most_probable_kmer_swap(dnas[i], k, profile)
current_score = score(motifs)
if current_score < best_score[0]:
best_score = [current_score, motifs]
return best_score
def randomized_motif_search(dnas, k, t, N):
rand_ints = [random.randint(0, len(dnas[0]) - k) for a in range(t)]
motifs = [dnas[i][r:r + k] for i, r in enumerate(rand_ints)]
best_score = [score(motifs), motifs]
for j in range(N):
i = random.randint(0, t - 1)
profile = profile_with_pseudocounts(motifs[:i - 1] + motifs[i:])
motifs[i] = profile_most_probable_kmer_swap(dnas[i], k, profile)
current_score = score(motifs)
if current_score < best_score[0]:
best_score = [current_score, motifs]
return best_score
def greedy_motif_search_pseudocounts(dnas, k, t):
best_score = t*k
for i in range(len(dnas[0]) - k + 1):
motifs = [dnas[0][i:i + k]]
for j in range(1, t):
current_profile = profile_with_pseudocounts(motifs)
motifs.append(profile_most_probable_kmer_swap(dnas[j], k, current_profile))
current_score = score(motifs)
if current_score < best_score:
best_score = current_score
best_motifs = motifs
return best_motifs
def motifs_from_profile(profile, dna, k):
return [profile_most_probable_kmer_swap(seq, k, profile) for seq in dna]
def motifs_from_profile(profile, dna, k):
return [profile_most_probable_kmer_swap(seq, k, profile) for seq in dna]
