def gibbs_sampler(dna, k, t, N):
# Randomly generate k-mers from each sequence in the dna list.
rand_ints = [randint(0, len(dna[0]) - k) for a in xrange(t)]
motifs = [dna_list[i][r:r + k] for i, r in enumerate(rand_ints)]
# Initialize the best score as a score higher than the highest possible score.
best_score = [score(motifs), motifs]
# Iterate motifs.
for i in xrange(N):
r = randint(0, t - 1)
current_profile = profile_with_pseudocounts(
[motif for index, motif in enumerate(motifs) if index != r])
# print 'a: ', motifs
motifs = [
profile_most_probable_kmer(dna[index], k, current_profile)
if index == r else motif for index, motif in enumerate(motifs)
]
# print 'b: ', motifs
current_score = score(motifs)
if current_score < best_score[0]:
best_score = [current_score, motifs]
return best_score
def motifs_from_profile(profile, dna, k):
return [profile_most_probable_kmer(seq, k, profile) for seq in dna]
if __name__ == '__main__':
with open('data/stepic_3e.txt') as input_data:
k,t = map(int, input_data.readline().split())
dna_list = [line.strip() for line in input_data.readlines()]
# Initialize the best score as a score higher than the highest possible score.
best_score = [t*k, None]
# Run the greedy motif search.
for i in xrange(len(dna_list[0])-k+1):
# Initialize the motifs as each k-mer from the first dna sequence.
motifs = [dna_list[0][i:i+k]]
current_profile = profile_with_pseudocounts(motifs)
# Find the most probable k-mer in the next string, using pseudocounts.
for j in xrange(1,t):
motifs.append(profile_most_probable_kmer(dna_list[j],k,current_profile))
current_profile = profile_with_pseudocounts(motifs)
# Check to see if we have a new best scoring list of motifs.
current_score = score(motifs)
if current_score < best_score[0]:
best_score = [current_score, motifs]
# Print and save the answer.
print '\n'.join(best_score[1])
with open('output/Assignment_03E.txt', 'w') as output_data:
output_data.write('\n'.join(best_score[1]))
def motifs_from_profile(profile, dna, k): return [profile_most_probable_kmer(seq,k,profile) for seq in dna]
