python中字符串搜索的优化

我必须编写一个python程序，给定一个50 MB的大DNA序列和一个约15个字符的较小DNA序列，返回一个15个字符的所有序列的列表，按它们与给定序列的距离以及它们在较大序列中的位置排序

我目前的方法是首先获得所有子序列：

def get_subsequences_of_size(size, data):
    sequences = {}
    i = 0
    while(i+size <= len(data)):
        sequence = data[i:i+size]
        if sequence not in sequences:
            sequences[sequence] = data.count(sequence)
        i += 1
    return sequences

---

我的问题是这条路太慢了。对于50MB的输入，完成处理需要30分钟以上。

以下方法如何：

在长序列和每个子序列上使用长度为15的滑动窗口：

• 将开始位置存储在长序列上
• 计算并存储相似度

这将输出子序列列表，这些子序列按它们与给定序列的接近程度排序

Sequence(s) which differ in 0 base(s) from the short sequence:
TGGCGACGGACTTCA at location(s) 300, 500

Sequence(s) which differ in 5 base(s) from the short sequence:
TGGCGATCGCCGTCG at location(s) 362

Sequence(s) which differ in 6 base(s) from the short sequence:
TGGCAACTACCTGAA at location(s) 86
TGGTGAGTATTTTCA at location(s) 401
TGGCGAGGGGGATGC at location(s) 191

Sequence(s) which differ in 7 base(s) from the short sequence:
ATGTGAAGGATGTGA at location(s) 283
AGGGGGATGCCTTCT at location(s) 196
TGACAACAACGTTTA at location(s) 53
CGCTGACGGATTATG at location(s) 154
TTATGACCGTTTTCC at location(s) 164
TGGTTGCTGGTTTCC at location(s) 430
TCGCGTCAGCCCGGA at location(s) 8
AGTCGCCTGAGTCCG at location(s) 30, 536
CGGCGATGTGGTTGC at location(s) 422

[... and so on...]

---

我还在50MB的FASTA文件上运行了脚本。在我的机器上，计算结果花了42秒，将结果写入文件又花了30秒（打印出来要花更长的时间！）

你能解释一下你的问题是什么吗？老兄，我编辑了这么多次，一定是忘了。很抱歉我编辑了这篇文章来添加它，但问题是它对我来说太慢了。50MB的输入需要30分钟才能运行。也许可以尝试重新表述你的科学问题，而不是寻找所有15个k-mers，尝试寻找完美的命中率、一个突变、两个突变等。或者看看这个库：试试这里

import re
from itertools import islice
from collections import defaultdict

short_seq = 'TGGCGACGGACTTCA'
long_seq = 'AGAACGTTTCGCGTCAGCCCGGAAGTGGTCAGTCGCCTGAGTCCGAACAAAAATGACAACAACGTTTATGACAGAACATT' +\
           'CCTTGCTGGCAACTACCTGAAAATCGGCTGGCCGTCAGTCAATATCATGTCCTCATCAGATTATAAATGCGTGGCGCTGA' +\
           'CGGATTATGACCGTTTTCCGGAAGATATTGATGGCGAGGGGGATGCCTTCTCTCTTGCCTCAAAACGTACCACCACATTT' +\
           'ATGTCCAGTGGTATGACGCTGGTGGAGAGTTCCCCCGGCAGGGATGTGAAGGATGTGAAATGGCGACGGACTTCACCGCA' +\
           'TGAGGCTCCACCAACCACGGGGATACTGTCGCTCTATAACCGTGGCGATCGCCGTCGCTGGTACTGGCCCTGTCCACACT' +\
           'GTGGTGAGTATTTTCAGCCCTGCGGCGATGTGGTTGCTGGTTTCCGTGATATTGCCGATCCCGTGCTGGCAAGTGAGGCG' +\
           'GCTTATATTCAGTGTCCTTCTGGCGACGGACTTCACGCGTCAGCCCGGAAGTGGTCAGTCGCCTGAGTCCGAACAAAAAT'


def window(seq, n=2):
    "Returns a sliding window (of width n) over data from the iterable"
    "   s -> (s0,s1,...s[n-1]), (s1,s2,...,sn), ...                   "
    # from https://docs.python.org/release/2.3.5/lib/itertools-example.html
    it = iter(seq)
    result = tuple(islice(it, n))
    if len(result) == n:
        yield ''.join(result)
    for elem in it:
        result = result[1:] + (elem,)
        yield ''.join(result)

def hamming_distance(s1, s2):
    if len(s1) != len(s2):
        raise ValueError("Undefined for sequences of unequal length")
    return sum(ch1 != ch2 for ch1, ch2 in zip(s1, s2))

k = len(short_seq)
locations = defaultdict(list)
similarities = defaultdict(set)

for start, subseq in enumerate(window(long_seq, k)):
    locations[subseq].append(start)
    similarity = hamming_distance(subseq, short_seq) # substitute with your own similarity function
    similarities[similarity].add(subseq)

with open(r'stack46268997.txt', 'w') as f:
    for similarity in sorted(similarities.keys()):
        f.write("Sequence(s) which differ in {} base(s) from the short sequence:\n".format(similarity))
        for subseq in similarities[similarity]:
            f.write("{} at location(s) {}\n".format(subseq, ', '.join(map(str, locations[subseq]))))
        f.write('\n')

Sequence(s) which differ in 0 base(s) from the short sequence:
TGGCGACGGACTTCA at location(s) 300, 500

Sequence(s) which differ in 5 base(s) from the short sequence:
TGGCGATCGCCGTCG at location(s) 362

Sequence(s) which differ in 6 base(s) from the short sequence:
TGGCAACTACCTGAA at location(s) 86
TGGTGAGTATTTTCA at location(s) 401
TGGCGAGGGGGATGC at location(s) 191

Sequence(s) which differ in 7 base(s) from the short sequence:
ATGTGAAGGATGTGA at location(s) 283
AGGGGGATGCCTTCT at location(s) 196
TGACAACAACGTTTA at location(s) 53
CGCTGACGGATTATG at location(s) 154
TTATGACCGTTTTCC at location(s) 164
TGGTTGCTGGTTTCC at location(s) 430
TCGCGTCAGCCCGGA at location(s) 8
AGTCGCCTGAGTCCG at location(s) 30, 536
CGGCGATGTGGTTGC at location(s) 422

[... and so on...]