也许是一种更短更快的方法,因为这个函数很可能会用于非常大的数据:
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 而非Python 中执行。这应该比使用普通循环甚至列表推导式要快得多。
现场测试
使用d 的简单字典,而不是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"
# 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,但只有 2 或 3 倍。它肯定可以进一步优化。