也许有一种更短更快的方法,因为毫无疑问,这个函数将在非常大的数据上使用:
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
可以让您的循环在C中执行,而不是在Python中执行。这比使用普通循环甚至列表推导式要快得多。
现场测试
使用简单的字典d
代替由ambiguous_na_values
返回的字典
from itertools import product
import time
d = { "N": ["A", "G", "T", "C"], "R": ["C", "A", "T", "G"] }
seq = "RNRN"
lst_start = time.time()
[ "".join(i) for i in product(*[ d[j] for j in seq ]) ]
lst_end = time.time()
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:
List delay: 0.02 ms
Map delay: 0.01 ms
List delay: 0.04 ms
Map delay: 0.02 ms
List delay: 0.08 ms
Map delay: 0.06 ms
List delay: 0.43 ms
Map delay: 0.17 ms
List delay: 126.68 ms
Map delay: 77.15 ms
List delay: 1887.53 ms
Map delay: 1320.49 ms
显然,map
更优秀,但只有 2 或 3 倍的差距。可以肯定地说,它可以进一步优化。
import Bio.Data.IUPACData as bdi
,然后将d = Seq.IUPAC.IUPACData.ambiguous_dna_values
更改为d = bdi.ambiguous_dna_values
。 - Wayne