def randomized_motif_search(dna, k, t):
rand_ints = [randint(0, len(dna[0]) - k) for a in range(t)]
motifs = [dna_list[i][r:r + k] for i, r in enumerate(rand_ints)]
best_score = [score(motifs), 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 randomizedMotifSearch(dna, k, t):
bestMotifs = []
n = len(dna[0])
for i in xrange(t):
index = random.randint(0, n - k)
bestMotifs.append(dna[i][index:index + k])
motifs = bestMotifs
while True:
profile_matrix = formProfile(motifs)
motifs = selectMotifs(profile_matrix, dna, k)
if score(motifs) < score(bestMotifs):
bestMotifs = motifs
else:
return score(bestMotifs), bestMotifs
def gibbsSampler(dna, k, t, N):
motifs = []
n = len(dna[0])
for i in xrange(t):
index = random.randint(0, n - k)
motifs.append(dna[i][index:index + k])
best_motifs = motifs
for j in xrange(N):
r = random.randint(0, t - 1)
profile_matrix = formProfileGibbs(motifs, r)
motif = detectMostProbableKMer(dna[r], k, profile_matrix)
motifs = [motif if index == r else m for index, m
in enumerate(motifs)]
if score(motifs) < score(best_motifs):
best_motifs = motifs
return best_motifs
def gibbs_sampler(dna, k, t, N):
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)]
best_score = [score(motifs), motifs]
for i in range(N):
r = randint(0, t - 1)
current_profile = profile_with_pseudocounts(
[motif for index, motif in enumerate(motifs) if index != r])
motifs = [
profile_most_probable_kmer(dna[index], k, current_profile)
if index == r else motif for index, motif in enumerate(motifs)
]
current_score = score(motifs)
if current_score < best_score[0]:
best_score = [current_score, motifs]
return best_score
# k = 15
# t = 20
# N = 2000
#
# best_motifs = [k * t, None]
# for repeat in xrange(20):
# current_motifs = gibbsSampler(dna, k, t, N)
# if score(current_motifs) < best_motifs[0]:
# best_motifs = [score(current_motifs), current_motifs]
#
# output = best_motifs[1]
# example = ['ACGTCCACCGGCGTC', 'AAGCGCACCGGGGTG', 'ACCCTTACCGGGGTG', 'AAGTTCCTCGGGGTG', 'AAGTTTTATGGGGTG', 'AAGTTTACCGGGTGC', 'AAGTTTCGAGGGGTG', 'CTGTTTACCGGGGTA', 'AAGTTGCTCGGGGTG', 'AAACATACCGGGGTG', 'AAGTTTAGGAGGGTG', 'AAGGAAACCGGGGTG', 'AAGTTTACACAGGTG', 'TAGTTTACCGGGGAT', 'CCTTTTACCGGGGTG', 'AAGTGAGCCGGGGTG', 'AAGTCGTCCGGGGTG', 'AAGTTTACCGGACAG', 'AAGTTTACCAATGTG', 'AAGTTTACCGTCATG']
#
# for item in output:
# if item not in example:
# print 'ATATA'
dna = ['AGATAAGCGAAAGTCGCCCGTTTGGGAATAGAATTCGGGATAAAAAAGGCCGTAGCTCACTAGAGCCGTTATGCGGACAGGTGATGTGAGAGTCCTGGCACATTCCGCGACTATCGATTTCTGGGGACATCAAGTCGCTCAACGAGACTCGGGAAATCGTTCGCGGTCGCGCGTCGGTTACCGCATATGGTTGGCCTATGTCATGTCGGTTAAGTTCTGTACTTACGAACTCCGTTGAAACCGCTGTGCGTGTAAAACCAGCACTTGGAGCGCCACTTTCGAAGGGAGGTACGAGCAAGTCAAAACTAGCTGTCGGCAGATAAGCGAAAGTC', 'GATGGTGGAGTGCTCGCCCGTTTGGGAATAGAATTCGGGATAAAAAAGGCCGTAGCTCACTAGAGCCGTTATGCGGACAGGTGATGTGAGAGTCCTGGCACATTCCGCGACTATCGATTTCTGGGGACATCAAGTCGCTCAACGAGACTCGGGAAATCGTTCGCGGTCGCGCGTCGGTTACCGCATATGGTTGGCCTATGTCATGTCGGTTAAGTTCTGTACTTACGAACTCCGTTGAAACCGCTGTGCGTGTAAAACCAGCACTTGGAGCGCCACTTTCGAAGGGAGGTACGAGCAAGTCAAAACTAGCTGTCGGCAGATAAGCGAAAGTC', 'CGCGGGGGCTCTGAGTGTCTAAATGCCGAATACCGGTACAAGACACCAAGATTGCGGAAACTGACCTACTTGAACCCTGCCAGGTGCTAGCCAAGATAAGGCTAGGGTCTTAAGTCCAGAAATAGCCTTGCGCCAGCCAAACACGGAGCTGCTTCAGGTTGTGTGGTCGCTGTCTCGCAGTACGAGTGGCACGACAAGCCCCAGCCTCCCAGATTCGCAAGAGCTGATCCATGATGTAGGAAGACCTAGCTCTCAGATAGGGAGGCTTTCTCGTGGCGTGGGATCATCCGTTGTGTTCCCGTAACCAAGAGATACGGGAGCAATCGATTGGT', 'TTGAGTTGTGTGACAGGCGAACGGATATTAGAGTGCTCATGAATTCAATCGACCGGACGGCCGTTAAAATGTGGATAGAAATTTGTCTTTCCATGGATCCGCCAATGTACATCGGCACTAAGGTTAAATCTTCCCGTCACGATGGAGAATACAATCGGACTGCTTTACGCCCAGAACTCGGTAATCCCCGAGGATAGTCCCATCTAGATCGCTACTATGATACGGTCGCACTGACTCGTCAGCACCATGGGATTTAATAAACACAACTGTAGCAGAAGTGAAACCCTGCTAAAGTGCAGTCCTCTACGTTGAATAATTTTTTAAAGCAGCCC', 'CGTGACCTGGGTGACAGTCGACCCAGTGAGTAGGACTCAAAAGTGCCGCTCTTACATATATGCCCCTCAGGTCAGGATCTCAGCCCTAGGCTAGATGATTTCTCTGCTCGCGGTATGGGTCTAAAACTACACCATCTAGGGAACCGGCTGATAGGCGTAAAGTGGGTTTAACTGTTTCCGTGAGCTATCTTAAATAGTCAGTAGACTAGGTTTGTGTATGCCCCGTAACTGATCGTCAGAAGGGTGCCTAAAGTAACGTTTACAGGTCGTACGGGAGTGCTACCACGCTCTTCCGTGTTCGTTAGCCTGGTTCGGACTACCCAGAACCCACA', 'ATCGAGGATACGGGGCAGCTCTGCCGCAAAACAAAGTGATAGGGACGAAATGGCAGTCCCAACTAATAACATAAACACTTTCGTCATACTACGACTCTGCAAGACAGATTATTAGAATGTCCACGCGACTAATCGGCCCCTTCACGAAAATACCAGAAGCCGTAAAGGTCCACGCGACACCCAAACAGGGGAAGGAGGGTCTGTTTGAACGGAAGAACGAGCTTTTATAAAGGAGCCCCAAACATGAATTGTCATGTAGGCAGGTGTGATTTTTAGCATCCCTTACAAGAGTATAGCCTTAATCAGCTGTAAGCATAACGCCTGGGACTGCA', 'CAACAGTTAGATCCGGATAACTCAGTTCGACCACTGGAATACGCATTGCATCAAATGCCCAAGGAGAGCACGTCTGTTCTGCACGGAGTTATTGGTCATCTATTGCTGCCAAAGTTGCGATGGCGGCTAAGACTGGGTGCATACTGGTGACCGGAGAGCGTCGGACTGGTTATCTATTGCGGACGGGGGTGCGCAATCCTACACGTTTAATAGAGTTCAGGTCACGAGATACGGGAGTAAACCAACTTGGCTATTTTATGAATGTTTCAGACTGAGTATTTCGTCTGTCTTTGGTCTAAAGACTGGGGAGCTGAACGCCACAGAACGCCTAG', 'GTGTGCTATTGAACGAGCGTTTCTGGGAGCGCACTATAGGGCACAGTCCCGTCATATTACCGTGGATTCTTTTCCGTTTTCGTTACCTTATGATATGGTGAGGGCACCCAGAATTGTGCGTGAGAATATTTGCCCGGGGCACATACGCCGGCCGTTCATCCCTCAGATGGCTGGCCGTGTGATACGGGACGCCTCATACCACTAGGGCACTAGCGTTGTCCGGCCACTGCCCACTTTGAGAAGGAAACTTCAGCTGTCAAAAAATGAGTTGCCTCGCTTCACCAGACGCTATATCTACTTTGTATGATACGGTCATCGGTCATGCGCACTTG', 'TTACTCACTTCCGCCTCCATTCTAGTGAGTTCTAAGGACCTGGAGGGGATTCGGTCGTCGTACAGAAAACTAACTACTTTCACTACATTTGTGCAAAGCCACGCTCCCCAATCATTTTGTCCATTTCCCGATACGGGAGTGGCTTACACGCTCTGTGAGAGATTATGGCTAGGGTGAAGGTGATCGTAGCCTGTGCAGACACTCCCACTATCGGAATGTAACCCCGCAAGTCTCTTCTTCCGCGCCACGAGATGTCTAGCCGCCTCATGCGCGTGGATCCTGTACTGCGTCTCCCCCTACTCTGGGTCCGGCCAGGTTCCCTTTTAAGTCCC', 'TTCCCCTGGGGGGTAGGCGGTTGTGGTACGATATGGGGAATGGCTTGCCCAATCCACTTACGCGGAACGTTACCAGAAACCCACTAACCAAATCGTACTGTTCTATTAGGCGATAGTAGTTCTTCCGTCAAGGTTACATAGTCTTAGCCTCTAAATGTATACGCGCGGAGGTTGCTGTTCACCTAGCCCATCAATTGTTACGTGGTCCCATATGTATTATGCGGTTGCCGCGGCCTGCGTAGGCTTTTAGGCGTCATTGAAGCACCGAGAGATAGACGAGTGCTCATACCACTAACCGTTATACTCGGGCTCACCAAGGGGCGGATATACTT', 'AGGGTGAGCTAACTTACCGCCGGGGGACAGTGACCTCATTTAGGTCCACAAGTGTTATACTCGCCCACTGAGCACGCAACTCTATTCCTGTCCTGGCGCTCTCTCTCGCACCCGATGATTAGCCCTAAGACTGCACCGCGCCGCTAACTCTAAATTACACCCCTCCTCGTGGAATTAGAGGTGCGGATTTAATGGATCACAGGCTCTCGACGTGACCCCCTGGAACTGTTACGCGAGTTAGATACACTAGTGCTCTGTGCAGATAAGTTACCCCAAAGGCTTGCTCGGAGTTAGAATGACGCACTGGTGTCCCGATATAGGCGTTCGGGAAA', 'CTATCCGTTTAACTGGCGCACAGACTATCAATCAGCCACCAAATGTGACGGTCTGACTTTTCTCAGGCCGGTAAACCCGCGCTTTGTTCTGAGGCATAAGCTATGCTACGGTGCTTGATACGTTGGTGGGACGCACATCCGCGTGACGTGGGAGTGCTCCGTACGGATGACGCCCCTCCCGATTAAGCGCTGTAATTGAAGGGTTTGTGGTTCGCGCCCAACAACGGCGCGATATTAGGGGACCCGAAGCTTGATCTCCCACGCACGAAGGTGCAAAGAGCAGAAAAGACGCCCCAATAACGCATTAACCAGTAGTGTTGGATGCCCTCACG', 'CTCTGCACGGCGGATTTTGTGATTTTCCAGGCGCGTGGCTGTTTCTAGCCCCGGGAGTGCTCAGAAAATACTATATGATTGCGCAGCAACCGTTATATAAAGAGTCTAACAAGGGCCATTAACGGTGCAGTGATCTATAGGGGGTGGCGCCACCAAAAGAATCAAGAAGCTTTGTAGGTGTTCAGCCGGTGGTCTAGAATGCCCAGACCTTCTCACCATCGGTTCGATCTTCCACAAGCATTACTGGCGAGGGAGGATGTAGGAGTGCACGACGACTACCTAACTTCGTAGGTCTAGAAAAGATTCCCCTATTAGCGGTAGCAAGTGCAACG', 'GGGCATAAATATAATTTAGAGTGACGAGCCGGAGGGTTCTCTTTCACGTGCTTCCGAAAGTCGATGGGTTTGAGTCAATCACCCGCCTGTTAGTATTTAATGAAGTACCTCGGAGAGCTTGTCTGAGCTAAAACGTTGTTGAGCTATAACCTAGGGTGGAAAGGCACTAATACACGTCCCAGTTTGAAGGTTCTTGACTCCTCGTTGGTCGAGCCCATAAGGCCAACATCGCATTCGCGCGACTGAAGTAAAGTCGCTCAGTGATGAGGCTCGCCAAACTCCACGGGAGTGCTCTGACACAGTCACGTCACTAATCCCTTCCTGAGTGCCAT', 'TTACTCTTGTTAGACCCGAGGGAGTAGGTGCATAACCCACGTTACGAAGGGCTCGGACGACATGATGAAATACTGTACTGTGCGTTCATTTCCCCCATTCTATGTAGATACGCACGTGCTCAACCATCCAGTTGGTGCCTCCAGAGATCCGTGTGAACGCACCAGTGTATAGCGCCGCAGAGCGTTCGTTTGCGCTGCTCTTCCTTCGTCTATTCGAAGCTCCAAGAGGAAGGGTCCGTTCGCCCCCTTCTATGTGTCTCGCGGCGTGTTGAGTATTGAAATTCACGGCTCTCTACAGGCGCGTTTCGGACACAGCTATCTTGCCTAGCCTT', 'AAAACCGCGAAGTACTCCGGAAAAGCGATTCGCAAAGGCCCATTTCAGGACCCAGCGTCCAAAGGCGTTGACCCCAACGGACAGGACAGCTTTAGAGTGGTTCCCCGCCGTTAAGGTGCGTTCAGCGGCGTTAGTATTTAAAGAACGAGTTTCCATTTAATAAGTCATAGAGTGTCACTTGTGGTTCACTTTGGTTAGGTCAACCCATGCATCATTCAGTGGGATCTTTCTTCTAATTCATGCCTATGATATACTGAACTGGTCACTGAACTTCGCGACTCGGCCGACTTCCCAAGTATTTTTTTCATACGGGAGTGCGGTACCGAGCGGGT', 'AGACGTATTCGCGCAAGTGGCTTCCCGCGAAAATCGGCTCGTGGTACCCTCAGAGGACCTACCTCCGACTGTTACGGACATCCTGTCACCAGGGCAATTCTACAACGCTGTTGAAAGGTCTGTTATATGATTGTCCCGTTACGCCTAACCTCGAACCGGCCGGAAAGCGGATCTACGACCCGCTGTCTAGTATGTGGTGTGTAGACATACGCCGGGAAGGCATAAATGGAGAGTTACCCAATTTGGCACCTGTAGTAAGGTACGGGGATAATGATACTTGCCCTCACCGAATGGAATGTAAGCCGGGATCACGGAGTGCTCCGAGTGGCCCA', 'CCAAAACGATACTCGTATGACAACCCCCGTGTACCACTCGCCTATGGAAGGACTAGTTCTGCAGACAAGATATCGGATACGAATGTGCTCGTTACTACTAACGTTAAATTACTAAGCGACGATAGTGCGTTGCAAAGTGAGCGTTTGAATGTCACCATACTAAATAGACCTCAGTACCTGCATCGATGCCTACGCACTATTATGTCCAGCATTCGGTTTTAGGACAAATCATAATAACCTAGTCGGCAGAACAAGCCATGAACGAATGCTTGTGTTGACCGCTGTCGTGGCGATTCGCGTAGAATGTGTGCCCGACTTTCGGCGTTTCAGTG', 'GTATATCCAATATACGGGCGACATTCATCTACTAGCGTACATAACGTTTGTGCGAGCACAATGAAGCGCCATAGTCTAGATCTCGAATTTAATGAGCCCAACTCGCGTCTTACATGTTCTCTGGATCGAGCCACAACCCGATACGGAGATGCTCATTCGTCTCTAAGCTACTTCCGTCGACCAGGCCGGAGAGTTAGGTAAAAGCATACAATTGAACAACTAGGCAGAGTGGTCCGCGTCCACATGATATACAGCTGCGTGTACTGCTCCTAGTATATATCCGTGTTCCTGCCTGGTTTCACCAGATGCCTGGAAGAGCTCCGTCAGCGCGT', 'TCGAGTTGTTAAGTCTTCAAAGAGCTCGCGCAATTAATTTCCATTATGGCGTAGAGTCGACAGCTACCGCTCGAAAAATCGTGGGGGTTCGCTTGAAGGAAAGGTCAGGTTCTCACGACCTGTAAGCGAGTCTGCACGTTGTGGCGAGCGGTACACGGTATTTTTAACAAAGAATACAGGAGGTGTCCCACCAGTGTAGGGACCACTGGTGCAGATCAAAATAAGGATTAAGTTGCAGCGTACCGATACAACAGTGCTCAGATTCTCAATATTAGGTCGGCTAGCCGGACAGTCAATAGCACCGAGCACTCATCATATAGTTCGCACATAAG']
k = 15
t = 20
N = 2000
best_motifs = [k * t, None]
for repeat in xrange(100):
current_motifs = gibbsSampler(dna, k, t, N)
if score(current_motifs) < best_motifs[0]:
best_motifs = [score(current_motifs), current_motifs]
print best_motifs[1]
