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
Beispiel #3
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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
Beispiel #4
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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]))