def greedy_motif_search(dna, k, t, pseudo=0):
"""
:param dna: DNA strings
:param k: kmer size
:param t: number of kmers in the DNA
:param pseudo: pseudocount
:return: best motif
"""
n = len(dna[0])
best_motifs = []
best_score = 0
for i in range(n - k + 1):
motifs = [dna[0][i:i + k]]
for j in range(1, t):
profile = get_profile(get_count(motifs[0:j]))
if pseudo:
profile = add_scalar_to_matrix(pseudo, profile)
motifs.append(profile_most_probable_pattern(dna[j], k, profile))
current_score = score(motifs, consensus(get_count(motifs)))
if not best_motifs or current_score < best_score:
best_motifs = motifs
best_score = current_score
return best_motifs
def repeated_gibbs_sampler(dna, k, t, n):
best_score = float('inf')
best_motifs = []
for i in range(100):
motifs = gibbs_sampler(dna, k, t, n)
current_score = score(motifs, consensus(get_count(motifs)))
if current_score < best_score:
best_score = current_score
best_motifs = motifs
return best_motifs
def profile_to_motifs(profile, dna):
"""
:param profile: profile matrix
:param dna: DNA strings
:return: best motifs
"""
k = len(list(profile.values())[0])
t = len(dna)
n = len(dna[0])
best_motifs = []
best_score = 0
for i in range(n - k + 1):
motifs = [dna[0][i:i + k]]
for j in range(1, t):
motifs.append(profile_most_probable_pattern(dna[j], k, profile))
if not best_motifs or score(motifs, consensus(get_count(motifs))) < best_score:
best_motifs = motifs
best_score = score(best_motifs, consensus(get_count(best_motifs)))
return best_motifs
def test_score(self):
motifs = [
'AACGTA',
'CCCGTT',
'CACCTT',
'GGATTA',
'TTCCGG',
]
expected = 14
count = get_count(motifs)
cons = consensus(count)
sc = score(motifs, cons)
self.assertEqual(expected, sc)
def gibbs_sampler(dna, k, t, n):
best_score = float('inf')
motifs = random_motifs([string for string in dna], k, t)
best_motifs = motifs
for j in range(n):
skip_index = randint(0, t - 1)
dna_slice = dna[:skip_index] + dna[skip_index + 1:]
profile = profile_with_pseudocounts(get_count(dna_slice))
motifs[skip_index] = profile_generated_string(motifs[skip_index],
profile, k)
current_score = score(motifs, consensus(get_count(motifs)))
if current_score < best_score:
best_motifs = motifs
best_score = current_score
return best_motifs
def test_greedy_motif_search_with_pseudocounts_long(self):
dna = [
'GCGCCCCGCCCGGACAGCCATGCGCTAACCCTGGCTTCGATGGCGCCGGCTCAGTTAGGGCCGGAAGTCCCCAATGTGGCAGACCTTTCGCCCCTGGCGGACGAATGACCCCAGTGGCCGGGACTTCAGGCCCTATCGGAGGGCTCCGGCGCGGTGGTCGGATTTGTCTGTGGAGGTTACACCCCAATCGCAAGGATGCATTATGACCAGCGAGCTGAGCCTGGTCGCCACTGGAAAGGGGAGCAACATC',
'CCGATCGGCATCACTATCGGTCCTGCGGCCGCCCATAGCGCTATATCCGGCTGGTGAAATCAATTGACAACCTTCGACTTTGAGGTGGCCTACGGCGAGGACAAGCCAGGCAAGCCAGCTGCCTCAACGCGCGCCAGTACGGGTCCATCGACCCGCGGCCCACGGGTCAAACGACCCTAGTGTTCGCTACGACGTGGTCGTACCTTCGGCAGCAGATCAGCAATAGCACCCCGACTCGAGGAGGATCCCG',
'ACCGTCGATGTGCCCGGTCGCGCCGCGTCCACCTCGGTCATCGACCCCACGATGAGGACGCCATCGGCCGCGACCAAGCCCCGTGAAACTCTGACGGCGTGCTGGCCGGGCTGCGGCACCTGATCACCTTAGGGCACTTGGGCCACCACAACGGGCCGCCGGTCTCGACAGTGGCCACCACCACACAGGTGACTTCCGGCGGGACGTAAGTCCCTAACGCGTCGTTCCGCACGCGGTTAGCTTTGCTGCC',
'GGGTCAGGTATATTTATCGCACACTTGGGCACATGACACACAAGCGCCAGAATCCCGGACCGAACCGAGCACCGTGGGTGGGCAGCCTCCATACAGCGATGACCTGATCGATCATCGGCCAGGGCGCCGGGCTTCCAACCGTGGCCGTCTCAGTACCCAGCCTCATTGACCCTTCGACGCATCCACTGCGCGTAAGTCGGCTCAACCCTTTCAAACCGCTGGATTACCGACCGCAGAAAGGGGGCAGGAC',
'GTAGGTCAAACCGGGTGTACATACCCGCTCAATCGCCCAGCACTTCGGGCAGATCACCGGGTTTCCCCGGTATCACCAATACTGCCACCAAACACAGCAGGCGGGAAGGGGCGAAAGTCCCTTATCCGACAATAAAACTTCGCTTGTTCGACGCCCGGTTCACCCGATATGCACGGCGCCCAGCCATTCGTGACCGACGTCCCCAGCCCCAAGGCCGAACGACCCTAGGAGCCACGAGCAATTCACAGCG',
'CCGCTGGCGACGCTGTTCGCCGGCAGCGTGCGTGACGACTTCGAGCTGCCCGACTACACCTGGTGACCACCGCCGACGGGCACCTCTCCGCCAGGTAGGCACGGTTTGTCGCCGGCAATGTGACCTTTGGGCGCGGTCTTGAGGACCTTCGGCCCCACCCACGAGGCCGCCGCCGGCCGATCGTATGACGTGCAATGTACGCCATAGGGTGCGTGTTACGGCGATTACCTGAAGGCGGCGGTGGTCCGGA',
'GGCCAACTGCACCGCGCTCTTGATGACATCGGTGGTCACCATGGTGTCCGGCATGATCAACCTCCGCTGTTCGATATCACCCCGATCTTTCTGAACGGCGGTTGGCAGACAACAGGGTCAATGGTCCCCAAGTGGATCACCGACGGGCGCGGACAAATGGCCCGCGCTTCGGGGACTTCTGTCCCTAGCCCTGGCCACGATGGGCTGGTCGGATCAAAGGCATCCGTTTCCATCGATTAGGAGGCATCAA',
'GTACATGTCCAGAGCGAGCCTCAGCTTCTGCGCAGCGACGGAAACTGCCACACTCAAAGCCTACTGGGCGCACGTGTGGCAACGAGTCGATCCACACGAAATGCCGCCGTTGGGCCGCGGACTAGCCGAATTTTCCGGGTGGTGACACAGCCCACATTTGGCATGGGACTTTCGGCCCTGTCCGCGTCCGTGTCGGCCAGACAAGCTTTGGGCATTGGCCACAATCGGGCCACAATCGAAAGCCGAGCAG',
'GGCAGCTGTCGGCAACTGTAAGCCATTTCTGGGACTTTGCTGTGAAAAGCTGGGCGATGGTTGTGGACCTGGACGAGCCACCCGTGCGATAGGTGAGATTCATTCTCGCCCTGACGGGTTGCGTCTGTCATCGGTCGATAAGGACTAACGGCCCTCAGGTGGGGACCAACGCCCCTGGGAGATAGCGGTCCCCGCCAGTAACGTACCGCTGAACCGACGGGATGTATCCGCCCCAGCGAAGGAGACGGCG',
'TCAGCACCATGACCGCCTGGCCACCAATCGCCCGTAACAAGCGGGACGTCCGCGACGACGCGTGCGCTAGCGCCGTGGCGGTGACAACGACCAGATATGGTCCGAGCACGCGGGCGAACCTCGTGTTCTGGCCTCGGCCAGTTGTGTAGAGCTCATCGCTGTCATCGAGCGATATCCGACCACTGATCCAAGTCGGGGGCTCTGGGGACCGAAGTCCCCGGGCTCGGAGCTATCGGACCTCACGATCACC',
]
k = 15
t = len(dna)
expected_score = 35
result = greedy_motif_search_with_pseudocounts(dna, k, t)
print(result)
self.assertEqual(expected_score,
score(result, consensus(get_count(result))))
def test_greedy_motif_search_0(self):
dna = [
'GGCGTTCAGGCA',
'AAGAATCAGTCA',
'CAAGGAGTTCGC',
'CACGTCAATCAC',
'CAATAATATTCG',
]
k = 3
t = len(dna)
expected = [
'CAG',
'CAG',
'CAA',
'CAA',
'CAA',
]
result = greedy_motif_search(dna, k, t)
self.assertEqual(expected, result)
cons = consensus(get_count(expected))
self.assertEqual(2, score(result, cons))
def test_greedy_motif_search_with_pseudocounts_0(self):
dna = [
'GGCGTTCAGGCA',
'AAGAATCAGTCA',
'CAAGGAGTTCGC',
'CACGTCAATCAC',
'CAATAATATTCG',
]
k = 3
t = len(dna)
expected = [
"TTC",
"ATC",
"TTC",
"ATC",
"TTC",
]
expected_score = 2
result = greedy_motif_search_with_pseudocounts(dna, k, t)
self.assertEqual(expected, result)
self.assertEqual(expected_score,
score(result, consensus(get_count(result))))