def randomized_motif_search(dna, k, t):
# 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.
while True:
current_profile = profile_with_pseudocounts(motifs)
motifs = motifs_from_profile(current_profile, dna_list, k)
current_score = score(motifs)
if current_score < best_score[0]:
best_score = [current_score, motifs]
else:
return best_score
def randomized_motif_search(dna,k,t): # 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. while True: current_profile = profile_with_pseudocounts(motifs) motifs = motifs_from_profile(current_profile, dna_list, k) current_score = score(motifs) if current_score < best_score[0]: best_score = [current_score, motifs] else: return best_score
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
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]))