如何扩展不明确的dna序列

问题描述

假设您具有这样的DNA序列:

Let's say you have a DNA sequence like this :

AATCRVTAA

其中 R 和 V 是DNA核苷酸的歧义值，其中 R 代表 A 或 G 和 V 代表 A ， C 或 G .

where R and V are ambiguous values of DNA nucleotides, where R represents either A or G and V represents A, C or G.

是否存在Biopython方法来生成可以由上述歧义序列表示的序列的所有不同组合?

Is there a Biopython method to generate all the different combinations of sequences that could be represented by the above ambiguous sequence ?

例如，在这里，输出将是:

Here for instance, the output would be :

AATCAATAA
AATCACTAA
AATCAGTAA
AATCGATAA
AATCGCTAA
AATCGGTAA

推荐答案

也许是一种更短，更快的方法，因为很可能该函数将用于非常大的数据:

Perhaps a little shorter and faster way, since by all odds this function is going to be used on very large data:

from Bio import Seq
from itertools import product

def extend_ambiguous_dna(seq):
   """return list of all possible sequences given an ambiguous DNA input"""
   d = Seq.IUPAC.IUPACData.ambiguous_dna_values
   return [ list(map("".join, product(*map(d.get, seq)))) ]

使用 map 允许您的循环在C中而不是在Python中执行.这应该比使用普通循环甚至列表理解要快得多.

Using map allows your loops to be executed in C rather than in Python. This should prove much faster than using plain loops or even list comprehensions.

使用简单的dict作为 d ，而不是 ambiguous_na_values

With a simple dict as d instead of the one returned by ambiguous_na_values

from itertools import product
import time

d = { "N": ["A", "G", "T", "C"], "R": ["C", "A", "T", "G"] }
seq = "RNRN"

# using list comprehensions
lst_start = time.time()
[ "".join(i) for i in product(*[ d[j] for j in seq ]) ]
lst_end = time.time()

# using map
map_start = time.time()
[ list(map("".join, product(*map(d.get, seq)))) ]
map_end = time.time()

lst_delay = (lst_end - lst_start) * 1000
map_delay = (map_end - map_start) * 1000

print("List delay: {} ms".format(round(lst_delay, 2)))
print("Map delay: {} ms".format(round(map_delay, 2)))

输出:

# len(seq) = 2:
List delay: 0.02 ms
Map delay: 0.01 ms

# len(seq) = 3:
List delay: 0.04 ms
Map delay: 0.02 ms

# len(seq) = 4
List delay: 0.08 ms
Map delay: 0.06 ms

# len(seq) = 5
List delay: 0.43 ms
Map delay: 0.17 ms

# len(seq) = 10
List delay: 126.68 ms
Map delay: 77.15 ms

# len(seq) = 12
List delay: 1887.53 ms
Map delay: 1320.49 ms

显然 map 更好，但只是2或3倍.可以肯定的是，它可以进一步优化.

Clearly map is better, but just by a factor of 2 or 3. It's certain it could be further optimised.

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