replacing SNPs with missing calls with a specific string

Question

I have a big file containing 27 columns and nearly 6 million rows. The following is a little example of my file

head data
0.65   0.722222   1.0      0.75     0
0.35   0.277778   0.0      0.25     0
0      0.666667   0.75     0.5      0.5625
0      0.333333   0.25     0.5      0.4375 

Rows are SNPs and columns are individuals. and I have "2 rows per each SNP" (one for reference allele and the other for alternative allele). In the example above I showed data for 2 SNPs (rows 1 and 2 correspond to SNP1 and rows 3 and 4 correspond to SNP2). I want to check if for each SNP, both ref and alt allele frequencies are 0, replace them with 9. this is my desired output:

head desired
0.65   0.722222   1.0      0.75     9
0.35   0.277778   0.0      0.25     9
9      0.666667   0.75     0.5      0.5625
9      0.333333   0.25     0.5      0.4375 

Answer 1

A possible approach in plain python:

#!/usr/bin/env python3

import sys

def both_zero(freq1, freq2):
    return freq1 == "0" and freq2 == "0"

with open(sys.argv[1], "r") as snp_file:
    for line1 in snp_file:
        # Consume one extra line to get the frequencies
        # for the alternative allele
        line2 = snp_file.readline()
        # "zip" the frequencies in pairs
        freq_pairs = zip(line1.strip().split(), line2.strip().split())
        recoded_pairs = [
            ("9", "9") if both_zero(freq1, freq2) else (freq1, freq2)
            for (freq1, freq2) in freq_pairs]
        # "unzip" the recoded frequencies into separate objects
        # (see https://stackoverflow.com/a/19343/1878788)
        (recoded1, recoded2) = zip(*recoded_pairs)
        print(*recoded1, sep="\t")
        print(*recoded2, sep="\t")

Calling it with your example snp frequencies file as command-line argument:

$ ./recode_absent.py snps.txt
0.65    0.722222        1.0     0.75    9
0.35    0.277778        0.0     0.25    9
9       0.666667        0.75    0.5     0.5625
9       0.333333        0.25    0.5     0.4375