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gibbs sampler.py
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gibbs sampler.py
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import random
def random_kmers(DNA, k):
rand_list = []
for sequence in DNA:
l = len(sequence)
r = random.randint(0, (l-k))
rand_kmer = sequence[r:r+k]
rand_list.append(rand_kmer)
return (rand_list)
def most_common(motif_matrix):
from collections import Counter
return [Counter(col).most_common(1)[0][0] for col in zip(*motif_matrix)]
def consensus(motifs):
consensus = []
consensus.append(most_common(motifs))
consensus_string = ''.join(consensus[0])
return(consensus_string)
def hamming_distance(most_prob_kmer, kmer):
return sum(pattern_base1 != pattern_base2 for pattern_base1, pattern_base2 in zip(most_prob_kmer, kmer))
def calculate_score(motifs):
consensus_str = consensus(motifs)
score = 0
for motif in motifs:
score = score + hamming_distance(consensus_str, motif)
return score
def generate_profile(motifs, k):
import numpy
profile = []
probA, probC, probG, probT = [], [], [], []
for base in range(k):
countA, countC, countG, countT = 1, 1, 1, 1
for motif in motifs:
if motif[base] == "A":
countA += 1
elif motif[base] == "C":
countC += 1
elif motif[base] == "G":
countG += 1
elif motif[base] == "T":
countT += 1
totalcount = countA + countC + countG + countT
probA.append(countA/totalcount)
probC.append(countC/totalcount)
probG.append(countG/totalcount)
probT.append(countT/totalcount)
profile.append(probA)
profile.append(probC)
profile.append(probG)
profile.append(probT)
t_profile = numpy.transpose(profile)
return t_profile
def probability (profile, kmer):
product_probability = 1
for base in range(len(kmer)):
if kmer[base] == 'A':
p = profile[base][0]
elif kmer[base] == 'C':
p = profile[base][1]
elif kmer[base] == 'G':
p = profile[base][2]
elif kmer[base] == 'T':
p = profile[base][3]
product_probability = product_probability * p
return(product_probability)
def Random_i_kmer(Profile, seq, k):
import numpy as np
l = []
prof = Profile
for j in range(len(seq)-k+1):
kmer = seq[j:j+k]
prob = probability(prof, kmer)
l.append([j, prob])
p_list = []
i_list = []
for i in range(len(l)):
i_list.append(l[i][0])
p_list.append(l[i][1])
new_l = []
for i in range(len(p_list)):
new_l.append(p_list[i]/sum(p_list))
rand_i = np.random.choice(i_list, p=new_l)
return(rand_i)
def Gibbs_Sampling(DNA, k, N, t):
initial_motifs = random_kmers(DNA, k)
BestMotifs = initial_motifs
for n in range(1, N):
i = random.randint(0, t-1)
NewMotifs = BestMotifs
NewMotifs.pop(i)
NewProfile = generate_profile(NewMotifs, k)
seq = DNA[i]
print(seq)
rand_i = Random_i_kmer(NewProfile, seq, k)
Motif_i = str(DNA[i][rand_i:rand_i+k])
NewMotifs.insert(i,Motif_i)
if calculate_score(NewMotifs) < calculate_score(BestMotifs):
BestMotifs = NewMotifs
return(BestMotifs)
DNA=['CGCCCCTCTCGGGGGTGTTCAGTAACCGGCCA',
'GGGCGAGGTATGTGTAAGTGCCAAGGTGCCAG',
'TAGTACCGAGACCGAAAGAAGTATACAGGCGT',
'TAGATCAAGTTTCAGGTGCACGTCGGTGAACC',
'AATCCACCAGCTCCACGTGCAATGTTGGCCTA']
k = 8
N = 100
t = 5
BestMotifs = random_kmers(DNA, k)
for m in range (20):
Motifs = Gibbs_Sampling(DNA, k, N, t)
if calculate_score(Motifs) < calculate_score(BestMotifs):
BestMotifs = Motifs
print(BestMotifs)
print(calculate_score(BestMotifs))
# Output -> ['TCTCGGGG', 'CCAAGGTG', 'TACAGGCG', 'TTCAGGTG', 'TCCACGTG']