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Python在DNA序列中找到最长的ORF

[英]Python find longest ORF in DNA sequence

Can someone show me a straightforward solution for how to calculate the longest open reading frame (ORF) in a DNA sequence? 有人可以给我展示一个简单的解决方案,以解决如何计算DNA序列中最长的开放阅读框(ORF)吗? ATG is the start codon (ie, the beginning of an ORF) and TAG , TGA , and TAA are stop codons (ie, the end of an ORF). ATG是起始密码子(即ORF的开始),而TAGTGATAA是终止密码子(即ORF的结束)。

Here's some code that produces errors (and uses an external module called BioPython): 这是一些会产生错误的代码(并使用称为BioPython的外部模块):

import sys
from Bio import SeqIO

currentCid = ''
buffer = []

for record in SeqIO.parse(open(sys.argv[1]),"fasta"):
    cid = str(record.description).split('.')[0][1:]

    if currentCid == '':
        currentCid = cid
    else:
        if cid != currentCid:
            buffer.sort(key = lambda x : len(x[1]))
            print '>' + buffer[-1][0]
            print buffer[-1][1]
            currentCid = cid
            buffer = [(str(record.description),str(record.seq))]
        else:
            buffer.append((str(record.description),str(record.seq)))

buffer.sort(key = lambda x : len(x[1]))
print '>' + buffer[-1][0]
print buffer[-1][1]

Is it possible to write this procedure with the least amount of external dependencies (or at least get the above code to work)? 是否可以用最少的外部依赖关系编写此过程(或至少使上述代码起作用)?

Here's what my input looks like: 这是我的输入内容:

ACCGCCGCGAACATCGCCGAGATCCTGCCGCCGCAGCCGAGCCGGCTGGTCGAGTATGCGCAACGACGCG
CGTCCGGCAGCATCCCGGCGATCATGGCGCGCTGGGATGCACGCGTACTGCAGGACAACGAACCATTCAC
CGCAGTCTATGGCGGCGCGTCGTACATCAACAACGACCTGTTCCTCGCCCGCCTCGCCGACTGGGGCGTG
TCGGCCGGCAACTACAGCGGCGAGATCGGCGGCGCGACACCGCCGCTGCGCTGGCGCCCGCTGCGGCTGC
TGCGTTCGCTGCCGGTGTTCTGGCGCATGCTGCGTGTCGCGCGCGGGCACCTGCCGACGCTCGAGCGCGG
CTTGCAGCGCTTCGACCAGGAACTCGCGACGCTCGTCGAGCGACGCGCCGACGGCCAGCAACTGGCCGAC
TGGTTCACGCGCTTCTACGTGTTCGTCGTGCAGGGCAACCTGTGCATCGCGTCGTCGCTGGCCAGCAGCG
GCGGCGCACTGTGGGGCCGTCCGCCGACCGCATACGGCCAGCTCGACGACAGCCCGCACCGGCTGCCGTG
GGAAACCGATCCGGGCACCGCACGGCCCGCGCCCACCCACCTGCCGCTGCAGGCGTTTCCCGCCTGGCCG
CTGCCGGTCCGCGTGCTCCACGCGCTCGGCGCGCCCGGCATGCGCGGCTGGTATCTGCAGGTGCGCGAGT
GGTATCGCGACAACCTGATGCGCGTGTTCTTCCGCCTGCATCATGCGATGCCGGCCGCCGATCGCGACAC
GTGGTTCGCGCCCCATCCCGATCGCCGCGAACGCAACGGCAGCTTCTGGCAGGACGGCGGCGAAGGCACC
GACGAGGCAGCCGGCTTCATGATCTATCCGGGCCACACGCAAGGCGTGCTCGGCCACGACATCCTGCTGG
AAGACACGCTCGACCCGGGCCGGCACGCGCAGTACCAGGCCGCGCGCGCCGTGATCGCGCGCATGGGCGG
CCGGCTGTCGCACGGCGCGACGCTGCTGCGCGAGCTGCGCAAGCCGTCGGCCGTGCTGCCGCGCGTCGAT
GCGGCGTGGATCGGGCGCGAGGTGCGGCTCAGCGACGGCCAGCTGACGCTGGTCGAATGAACGCGATGCG
GTTGCCGCGCACCCGAGCACGGGCCCGGGCCTGAACTGCCGATCAGCGTACCGGCGTGCGGACGACTCCG
TCGACCTTCAGCGTGCGCCGGTCGTGCGCGGCTTCGTATTCGACCGTCTGCGCAGGCGTGACGGCGCCGT
ATGAATGGCCGTTCACGTAGACGGTGCCGTCCCGCAGCTCGACCCGGTCGCCGTTGACCGTCGCTGTGGC
CCGTTCACCCTGCAGCACCGCGCCCGAACAACCTGCAGTCGAAAAACTGCGGACCGACGTGCCCGGCATC
GCGGCGATCCCGCCCTGGTCCGCCGCATGCGCCGCGCTGCACGGCGGCGCATCCATGCTGCCGGCAGCGT
GGACCGCGCCGGCGCTGATGCCGCATCCGGCAAGCAGCGCAATCGTCATCGGCTTCAGATGGTTCATGGT
GAGCTCCGTTGTCCGCCGCCGCGGATCGATGACCGGCCGACGCCCGTGCTCGCATGGCAGGCCGGCCGGC
CGGATGCATCCAGTATGCGTCCGGTTCGCGGCATTCCGCCATCGTCGCCGATACCGCTCATCGCCGCCCG
GTTCGCTCCCGCAGCGGCCTCTGGAAGCACCTCCCGCGGGGCAACCCGTCCCCATGAAAATCCACCTTGA
TCAAGTTGCGACTCGCAACTATTATTGATTGCGATCCGCAACCTTTCCGGACCCGCCATGGACCTCATCG
ACGCTCCCGCCAAGCCCCGCGAAGCCACGATCCTCGAGCTGCGCGACTTCTCCCGCAAACTGGTTCGCGA
GCTCGGCTTCATGCGCGCGACGCTGGCCGACAGCGACTGGGCGCCTT

My output should be: 我的输出应为:

The longest substring that begins with ATG (ie, the start of an ORF) and ends with either TAG , TGA , or TAA as stop codons (ie, the end of an ORF). ATG (即ORF的开始)开始并以TAGTGATAA作为终止密码子(即ORF的结束)结束的最长子串。

You should look into regular expressions: 您应该查看正则表达式:

import re

max(re.findall(r'ATG(?:(?!TAA|TAG|TGA)...)*(?:TAA|TAG|TGA)',s), key = len)

There is a good tutorial here , that focuses on the use of regular expressions with DNA strings 这是一个很好的教程在这里 ,一个专注于使用正则表达式与DNA串

Since BioPython is a well-established and widely available module that's specifically designed for these sorts of questions, there's little reason to avoid it and re-invent the wheel. 由于BioPython是专门为此类问题而设计的,已建立且广泛使用的模块,因此没有理由避免使用它并重新发明轮子。 That said it is useful to use regexes to identify start codons: 也就是说,使用正则表达式来识别起始密码子非常有用:

from Bio import Seq
import regex as re
startP = re.compile('ATG')
nuc = input_seq.replace('\n','')
longest = (0,)
for m in startP.finditer(nuc, overlapped=True):
    if len(Seq.Seq(nuc)[m.start():].translate(to_stop=True)) > longest[0]:
        pro = Seq.Seq(nuc)[m.start():].translate(to_stop=True)
        longest = (len(pro), 
                   m.start(), 
                   str(pro),
                   nuc[m.start():m.start()+len(pro)*3+3])

Note that this uses the regex module, not the re module; 请注意,这使用的是regex模块,而不是re模块。 the former allows easier identification of overlapping matches. 前者可以轻松识别重叠的匹配项。 We can let BioPython count triplets and look for stop codons, rather than try to labor through regexes to do that. 我们可以让BioPython计算三胞胎并寻找终止密码子,而不是尝试通过正则表达式来做到这一点。

Here, longest yields the length of the protein encoded by the ORF, the start site (note, using 0-based numbering), the protein sequence encoded by the ORF, and the sequence of the ORF itself, including the stop codon. 在这里, longest产生由ORF编码的蛋白质,起始位点(请注意,使用从0开始的编号),由ORF编码的蛋白质序列以及ORF本身的序列(包括终止密码子)的长度。

(338,
 93,
 'MARWDARVLQDNEPFTAVYGGASYINNDLFLARLADWGVSAGNYSGEIGGATPPLRWRPLRLLRSLPVFWRMLRVARGHLPTLERGLQRFDQELATLVERRADGQQLADWFTRFYVFVVQGNLCIASSLASSGGALWGRPPTAYGQLDDSPHRLPWETDPGTARPAPTHLPLQAFPAWPLPVRVLHALGAPGMRGWYLQVREWYRDNLMRVFFRLHHAMPAADRDTWFAPHPDRRERNGSFWQDGGEGTDEAAGFMIYPGHTQGVLGHDILLEDTLDPGRHAQYQAARAVIARMGGRLSHGATLLRELRKPSAVLPRVDAAWIGREVRLSDGQLTLVE',
 'ATGGCGCGCTGGGATGCACGCGTACTGCAGGACAACGAACCATTCACCGCAGTCTATGGCGGCGCGTCGTACATCAACAACGACCTGTTCCTCGCCCGCCTCGCCGACTGGGGCGTGTCGGCCGGCAACTACAGCGGCGAGATCGGCGGCGCGACACCGCCGCTGCGCTGGCGCCCGCTGCGGCTGCTGCGTTCGCTGCCGGTGTTCTGGCGCATGCTGCGTGTCGCGCGCGGGCACCTGCCGACGCTCGAGCGCGGCTTGCAGCGCTTCGACCAGGAACTCGCGACGCTCGTCGAGCGACGCGCCGACGGCCAGCAACTGGCCGACTGGTTCACGCGCTTCTACGTGTTCGTCGTGCAGGGCAACCTGTGCATCGCGTCGTCGCTGGCCAGCAGCGGCGGCGCACTGTGGGGCCGTCCGCCGACCGCATACGGCCAGCTCGACGACAGCCCGCACCGGCTGCCGTGGGAAACCGATCCGGGCACCGCACGGCCCGCGCCCACCCACCTGCCGCTGCAGGCGTTTCCCGCCTGGCCGCTGCCGGTCCGCGTGCTCCACGCGCTCGGCGCGCCCGGCATGCGCGGCTGGTATCTGCAGGTGCGCGAGTGGTATCGCGACAACCTGATGCGCGTGTTCTTCCGCCTGCATCATGCGATGCCGGCCGCCGATCGCGACACGTGGTTCGCGCCCCATCCCGATCGCCGCGAACGCAACGGCAGCTTCTGGCAGGACGGCGGCGAAGGCACCGACGAGGCAGCCGGCTTCATGATCTATCCGGGCCACACGCAAGGCGTGCTCGGCCACGACATCCTGCTGGAAGACACGCTCGACCCGGGCCGGCACGCGCAGTACCAGGCCGCGCGCGCCGTGATCGCGCGCATGGGCGGCCGGCTGTCGCACGGCGCGACGCTGCTGCGCGAGCTGCGCAAGCCGTCGGCCGTGCTGCCGCGCGTCGATGCGGCGTGGATCGGGCGCGAGGTGCGGCTCAGCGACGGCCAGCTGACGCTGGTCGAATGA')

Check this out: 看一下这个:

https://www.kaggle.com/xiangma/orf-finder?scriptVersionId=6709465 https://www.kaggle.com/xiangma/orf-finder?scriptVersionId=6709465

As shown in the link above, there are two methods to do this: 如上面的链接所示,有两种方法可以执行此操作:

Please note I set ORF length limitation above 1000bp and you can adjust it with your needs. 请注意,我将ORF长度限制设置为1000bp以上,您可以根据需要进行调整。

First one: 第一:

from Bio import SeqIO
records = SeqIO.parse('dna2.fasta', 'fasta')
for record in records:
    for strand, seq in (1, record.seq), (-1, record.seq.reverse_complement()):
        for frame in range(3):
            length = 3 * ((len(seq)-frame) // 3)
            for pro in seq[frame:frame+length].translate(table = 1).split("*")[:-1]:
                if 'M' in pro:
                    orf = pro[pro.find('M'):]
                    pos = seq[frame:frame+length].translate(table=1).find(orf)*3 + frame +1
                    if len(orf)*3 +3 > 1300:
                        print("{}...{} - length {}, strand {}, frame {}, pos {}, name {}".format\
                           (orf[:3], orf[-3:], len(orf)*3+3, strand, frame, pos, record.id))

Second one, which uses the regex: 第二个,使用正则表达式:

from Bio import SeqIO
import re
records = SeqIO.parse('dna2.fasta', 'fasta')

for record in records:
    for strand, seq in (1, record.seq), (-1, record.seq.reverse_complement()):
        for frame in range(3):
            index = frame
            while index < len(record) - 6:
                match = re.match('(ATG(?:\S{3})*?T(?:AG|AA|GA))', str(seq[index:]))
                if match:
                    orf = match.group()
                    index += len(orf)
                    if len(orf) > 1300:
                        pos = str(record.seq).find(orf) + 1 
                        print("{}...{} - length {}, strand {}, frame {}, pos {}, name {}".format\
                           (orf[:6], orf[-3:], len(orf), strand, frame, pos, record.id))
                else: index += 3

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