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HDplot_process_vcf.py
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HDplot_process_vcf.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
'''
Script based on McKinney et al. 2017's HDplot_python.py at:
http://datadryad.org/bitstream/handle/10255/dryad.123460/HDplot_python.py?sequence=1
Our modification calculates percentage of heterozygotes per locus
dividing the number of of heterozygote individuals over the number
of individuals present at the locus (unlike McKinney's which always
divides over the total number of individuals in the dataset, which
didn't skew their results because they had at most 10% missing data
per locus).
We also integrate the separate python script called vcf_to_depth.py at:
http://datadryad.org/bitstream/handle/10255/dryad.123461/vcf_to_depth.py?sequence=1
into the main script.
'''
__author__ = "Edgardo M. Ortiz"
__credits__ = "Juan D. Palacio-Mejía"
__version__ = "1.0"
__email__ = "e.ortiz.v@gmail.com"
__date__ = "2017-10-21"
import scipy.stats
import pandas as pd
import numpy as np
import statsmodels
import os
import gzip
import argparse
def vcf_to_allele_depth(vcf_file, out_file):
if vcf_file.endswith('.gz'):
opener = gzip.open
else:
opener = open
with opener(vcf_file) as INFILE:
with open(out_file, 'w') as OUTFILE:
vcf_format = "unk"
header_lines = 0
for line in INFILE:
if line.startswith('##'):
header_lines += 1
if line.startswith('##source'):
if "ipyrad" in line:
vcf_format="ipyrad"
elif "Stacks" in line:
vcf_format="stacks"
else:
vcf_format="unk"
elif line.startswith('#CHROM'):
print 'skipped {} header lines'.format(header_lines)
header = line.strip().strip('#').split('\t')
inds = header[9:]
print 'found {} individuals'.format(len(inds))
else:
if vcf_format == "stacks":
tabs = line.split('\t')
contig = tabs[0]
pos = tabs[1]
if tabs[2] == ".":
locus_ID = contig+"_"+str(int(pos)-1)
else:
locus_ID = tabs[2]
genotypes = tabs[9:]
depth_a_of_ind = dict()
depth_b_of_ind = dict()
for gen_idx, gen in enumerate(genotypes):
if gen.split(':')[0] in ['1/0', '0/1'] and gen.split(':')[2] != '.': # if het
depth_a_of_ind[inds[gen_idx]] = int(gen.split(':')[2].split(',')[0])
depth_b_of_ind[inds[gen_idx]] = int(gen.split(':')[2].split(',')[1])
sum_a = sum(depth_a_of_ind.values())
sum_b = sum(depth_b_of_ind.values())
num_hets = len(depth_b_of_ind.values())
# Our modification: get actual number of samples in the locus
num_samples = int(tabs[7].split(";")[0].replace("NS=",""))
if sum_a+sum_b > 0:
OUTFILE.write('{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n'.format(contig, pos, locus_ID, sum_a, sum_b, np.float(sum_a)/(sum_a+sum_b), num_hets, num_samples))
elif vcf_format == "ipyrad":
tabs = line.split("\t")
contig = tabs[0].replace("_","")
pos = tabs[1]
ref = tabs[3]
alt = tabs[4]
locus_ID = contig+"_"+pos
genotypes = tabs[9:]
depth_a_of_ind = dict()
depth_b_of_ind = dict()
catg={"C":0, "A":1, "T":2, "G":3}
if len(alt) == 1: # if SNP is biallelic
for gen_idx, gen in enumerate(genotypes):
if gen.split(":")[0] in ["1/0", "0/1"]: # if het
depth_a_of_ind[inds[gen_idx]] = int(gen.split(":")[2].split(",")[catg[ref]])
depth_b_of_ind[inds[gen_idx]] = int(gen.split(":")[2].split(",")[catg[alt]])
sum_a = sum(depth_a_of_ind.values())
sum_b = sum(depth_b_of_ind.values())
num_hets = len(depth_b_of_ind.values())
# Our modification: get actual number of samples in the locus
num_samples = int(tabs[7].split(";")[0].replace("NS=",""))
if sum_a+sum_b > 0:
OUTFILE.write('{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n'.format(contig, pos, locus_ID, sum_a, sum_b, np.float(sum_a)/(sum_a+sum_b), num_hets, num_samples))
else:
print "Error: source of VCF file undefined"
def main():
parser = argparse.ArgumentParser(description="Processes a VCF input file produced by Stacks or ipyrad for plotting heterozigosity and read depth deviations using the method of McKinney et al. 2017 (doi: 10.1111/1755-0998.12613)")
parser.add_argument("-i", "--input", action="store", dest="filename", required=True,
help="Name of VCF input file, must have read depth per allele in each individual (Stacks or ipyrad format)")
args = parser.parse_args()
filename = args.filename
depths_file = args.filename.split(".")[0]+".depths"
# LOAD VCF FILE AND EXTRACT SEQUENCE COUNTS FROM HETEROZYGOUS INDIVIDUALS
vcf_to_allele_depth(vcf_file=filename, out_file = depths_file)
# LOAD COUNT DATA INTO DATAFRAME
depths = pd.read_csv(depths_file, sep = '\t', header = None)
depths.columns = ['contig', 'pos', 'locus_ID', 'depth_a' , 'depth_b', 'ratio', 'num_hets', 'num_samples']
depths.head()
#SUM READ COUNTS PER LOCUS
depths['total_depth'] = depths['depth_a'] + depths['depth_b']
depths['depth_per_het'] = depths['total_depth']/[np.float(xx) for xx in depths['num_hets']]
depths.head()
# CALCULATE HETEROZYGOSITY
depths['hetPerc']=depths['num_hets']/depths['num_samples']
depths.head()
# CALCULATE EXPECTED STANDARD DEVIATION BASED ON BINOMIAL DISTRIBUTION
depths['std'] = scipy.stats.binom(n = depths['total_depth'], p = .5).std()
depths.head()
# CALCULATE Z-SCORE BASED ON STANDARD DEVIATION
depths['z'] = -(depths['total_depth']/2. - depths['depth_a'])/ depths['std']
depths.head()
# WRITE OUTPUT FILE CONTAINING DEPTH AND BIAS INFORMATION
depths.to_csv(depths_file+"Bias", sep="\t")
# REMOVE INTERMEDIATE FILE
os.remove(depths_file)
if __name__ == "__main__":
main()