python - DNA 字符串中随机基序搜索的意外输出

Question

我有以下 t=5 DNA 字符串:

DNA = '''CGCCCCTCTCGGGGGTGTTCAGTAAACGGCCA
GGGCGAGGTATGTGTAAGTGCCAAGGTGCCAG
TAGTACCGAGACCGAAAGAAGTATACAGGCGT
TAGATCAAGTTTCAGGTGCACGTCGGTGAACC
AATCCACCAGCTCCACGTGCAATGTTGGCCTA'''
k = 8
t = 5

我正在尝试使用拉普拉斯变换从字符串集合中找到长度为 k=8 的最佳主题，以从每个 t 字符串中随机采样长度为 k 的 block 。

我的辅助函数如下:

def window(s, k):
    for i in range(1 + len(s) - k):
        yield s[i:i+k]

def HammingDistance(seq1, seq2):
    if len(seq1) != len(seq2):
        raise ValueError('Undefined for sequences of unequal length.')
    return sum(ch1 != ch2 for ch1, ch2 in zip(seq1, seq2))

def score(motifs):
    score = 0
    for i in range(len(motifs[0])):
        motif = ''.join([motifs[j][i] for j in range(len(motifs))])
        score += min([HammingDistance(motif, homogeneous*len(motif)) for homogeneous in 'ACGT'])
    return score

def profile(motifs):
    prof = []
    for i in range(len(motifs[0])):
        col = ''.join([motifs[j][i] for j in range(len(motifs))])
        prof.append([float(col.count(nuc))/float(len(col)) for nuc in 'ACGT'])
    return prof

def profile_most_probable_kmer(dna, k, prof):
    dna = dna.splitlines()
    nuc_loc = {nucleotide:index for index,nucleotide in enumerate('ACGT')}
    motif_matrix = []
    max_prob = [-1, None]
    for i in range(len(dna)):
        motif_matrix.append(max_prob)
    for i in range(len(dna)):
        for chunk in window(dna[i],K):
            current_prob = 1
            for j, nuc in enumerate(chunk):
                current_prob*=prof[j][nuc_loc[nuc]]
            if current_prob>motif_matrix[i][0]:
                motif_matrix[i] = [current_prob,chunk]
    return list(list(zip(*motif_matrix))[1])

def profile_with_pseudocounts(motifs):
    prof = []
    for i in range(len(motifs[0])):
        col = ''.join([motifs[j][i] for j in range(len(motifs))])
        prof.append([float(col.count(nuc)+1)/float(len(col)+4) for nuc in 'ACGT'])
    return prof

from random import randint

def SampleMotifs(Dna,k,t):
    Dna = Dna.splitlines()
    BestMotifs = []
    for line in Dna:
        position = randint(0,len(line)-k)
        BestMotifs.append(line[position:position+k])
    return BestMotifs

def motifs_from_profile(profile, dna, k):
    return [profile_most_probable_kmer(seq,k,profile) for seq in dna]


def randomized_motif_search(dna,k,t):
    from random import randint
    dna = dna.splitlines()
    rand_ints = [randint(0,len(dna[0])-k)) for a in range(len(dna))]
    motifs = [dna[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, k)
        current_score = score(motifs)
        if current_score < best_score[0]:
            best_score = [current_score, motifs]
        else:
            return best_score[1]


def Laplace(dna,k,t):
    i = 0
    LastMotifs = randomized_motif_search(dna,k,t)
    while i < 1000:
        try:
            BestMotifs = randomized_motif_search(dna,k,t)
            if score(BestMotifs)<score(LastMotifs):
                LastMotifs = BestMotifs
        except:
            pass
        i+=1
    print(*LastMotifs)

我应该得到的输出是:

TCTCGGGG
CCAAGGTG
TACAGGCG
TTCAGGTG
TCCACGTG

我每次都会得到不同的输出，这是我期望使用具有随机元素的方法，但当我迭代 1000 次时它应该会收敛，并且只有在分数较低时才更新我的最佳主题。事实上，我必须在拉普拉斯中放入错误处理程序，因为我在调用 randomized_motif_search(dna,k,t) 时获得索引，这告诉我这可能是问题的根源。过去两天我一直在搜索代码以确保一切都正确，但事实上我得到了错误的答案或以下错误:

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-811-ee735449bb8e> in <module>
----> 1 Laplace(DNA,K,T)

<ipython-input-810-31301d79eb95> in Laplace(dna, k, t)
      3     LastMotifs = randomized_motif_search(dna,k,t)
      4     while i < 2000:
----> 5         BestMotifs = randomized_motif_search(dna,k,t)
      6         if score(BestMotifs)<score(LastMotifs):
      7             LastMotifs = BestMotifs

<ipython-input-809-43600d882734> in randomized_motif_search(dna, k, t)
      8     while True:
      9         current_profile = profile_with_pseudocounts(motifs)
---> 10         motifs = motifs_from_profile(current_profile, dna, k)
     11         current_score = score(motifs)
     12         if current_score < best_score[0]:

<ipython-input-408-7c866045d839> in motifs_from_profile(profile, dna, k)
      1 def motifs_from_profile(profile, dna, k):
----> 2     return [profile_most_probable_kmer(seq,k,profile) for seq in dna]

<ipython-input-408-7c866045d839> in <listcomp>(.0)
      1 def motifs_from_profile(profile, dna, k):
----> 2     return [profile_most_probable_kmer(seq,k,profile) for seq in dna]

<ipython-input-795-56f83ba5ee2b> in profile_most_probable_kmer(dna, k, prof)
     25             current_prob = 1
     26             for j, nuc in enumerate(chunk):
---> 27                 current_prob*=prof[j][nuc_loc[nuc]]
     28             if current_prob>motif_matrix[i][0]:
     29                 motif_matrix[i] = [current_prob,chunk]

IndexError: list index out of range

这不仅仅是有点令人烦恼。实际帮助将不胜感激。

编辑:问题是我对从 DNA 字符串中采样随机图案的随机整数进行索引，并且我的 motifs_from_profile 函数返回一个列表列表，而不仅仅是一个列表，代码有点依赖于取决于。我更新了以下函数:虽然这些修复解决了代码中引发错误的问题，并且现在每次运行 Laplace 函数时都会得到输出，但结果并不是什么我希望即使我在第一次迭代时输入正确的答案。我将尽最大努力调试评分函数中发生的情况并回顾我认为的文献。也许来自社区的一些更模糊的输入会有所帮助，但谁知道呢？

更新后的randomized_motif_search是:

def randomized_motif_search(dna,k,t):
    from random import randint
    from itertools import chain
    dna = dna.splitlines()
#     Randomly generate k-mers from each sequence in the dna list.
    rand_ints = [randint(0,len(dna[0])-(k)) for a in range(len(dna))]
    motifs = [dna[i][r:r+k] for i,r in enumerate(rand_ints)]
    best_score = [score(motifs), motifs]
    while True:
        current_profile = profile_with_pseudocounts(motifs)
        mfp = motifs_from_profile(current_profile,dna,k)
        motifs = []
        for i in range(len(mfp)):
            motifs.append(mfp[i][0])
        current_score = score(motifs)
        if current_score < best_score[0]:
            best_score = [current_score, motifs]
        else:
            return best_score[1]

和新的拉普拉斯:

def Laplace(dna,k,t):
    i = 0

    LastMotifs = randomized_motif_search(dna,k,t)
    while i < 1000:
        BestMotifs = randomized_motif_search(dna,k,t)
        if score(BestMotifs)<score(LastMotifs):
            LastMotifs = BestMotifs
        i+=1
    print(*LastMotifs)

编辑:亲爱的期刊，我已经搞乱了得分方法并弄清楚了如何返回主题得分，但仍然没有得出问题的正确答案。我正式被困在这里的是具有正确索引的更新的 score 函数代码:

def score(motifs):
    score = 0
    for i in range(len(motifs[0])):
        motif = ''.join([Motifs[j][i] for j in range(len(Motifs))])
        score+=min([HammingDistance(motif,homogenous*len(motif)) for homogenous in 'ACGT'])
    return score

Answer 1

我明白了!所有索引都源于对 splitlines() 的调用，我在许多辅助函数的开头进行了调用。我还没有完全调试我的 score() 函数，因此我的评分没有按照文献要求的方式进行。

所有辅助函数如下:

def window(s, k):
    for i in range(1 + len(s) - k):
        yield s[i:i+k]

def HammingDistance(seq1, seq2):
    if len(seq1) != len(seq2):
        raise ValueError('Undefined for sequences of unequal length.')
    return sum(ch1 != ch2 for ch1, ch2 in zip(seq1, seq2))

def score(motifs):
    score = 0
    for i in range(len(motifs[0])):
        motif = ''.join(motifs[j][i] for j in range(len(motifs)))
        score += min([HammingDistance(motif,homogenous*len(motif)) for homogenous in 'ACGT'])
    return score

def profile(motifs):
    prof = []
    for i in range(len(motifs[0])):
        col = ''.join([motifs[j][i] for j in range(len(motifs))])
        prof.append([float(col.count(nuc))/float(len(col)) for nuc in 'ACGT'])
    return prof

def profile_most_probable_kmer(dna, k, prof):
    nuc_loc = {nucleotide:index for index,nucleotide in enumerate('ACGT')}
    motif_matrix = []
    max_prob = [-1, None]
    for i in range(len(dna)):
        motif_matrix.append(max_prob)
    for i in range(len(dna)):
        for chunk in window(dna[i],k):
            current_prob = 1
            for j, nuc in enumerate(chunk):
                current_prob*=prof[j][nuc_loc[nuc]]
            if current_prob>motif_matrix[i][0]:
                motif_matrix[i] = [current_prob,chunk]
    return list(list(zip(*motif_matrix))[1])

def profile_with_pseudocounts(motifs):
    prof = []
    for i in range(len(motifs[0])):
        col = ''.join([motifs[j][i] for j in range(len(motifs))])
        prof.append([float(col.count(nuc)+1)/float(len(col)+4) for nuc in 'ACGT'])
    return prof

from random import randint

def SampleMotifs(Dna,k,t):
    Dna = Dna.splitlines()
    BestMotifs = []
    for line in Dna:
        position = randint(0,len(line)-k)
        BestMotifs.append(line[position:position+k])
    return BestMotifs

def randomized_motif_search(dna,k,t):
    from random import randint
    from itertools import chain
    dna = dna.splitlines()
#     Randomly generate k-mers from each sequence in the dna list.
    rand_ints = [randint(0,len(dna[0])-(k)) for a in range(len(dna))]
    motifs = [dna[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 = profile_most_probable_kmer(dna,k,current_profile)
#         motifs = []
#         for i in range(len(mfp)):
#             motifs.append(mfp[i][0])
        current_score = score(motifs)
        if current_score < best_score[0]:
            best_score = [current_score, motifs]
        else:
            return best_score[1]

def Laplace(dna,k,t):
    import random
    random.seed(0)
    i = 0

    LastMotifs = randomized_motif_search(dna,k,t)
    while i < 1000:
        BestMotifs = randomized_motif_search(dna,k,t)
        if score(BestMotifs)<score(LastMotifs):
            LastMotifs = BestMotifs
        i+=1
    print('\n'.join(LastMotifs))