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vcf2gvf.py
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vcf2gvf.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@author: madeline
This script converts VCF files that have been annotated into GVF
files. Required user input is a VCF file.
The attributes completed by this script are:
['ID', 'Name', 'gene', 'protein_name', 'protein_symbol', 'protein_id', 'ps_filter', 'ps_exc', 'mat_pep',
'mat_pep_desc','mat_pep_acc', 'ro', 'ao', 'dp', 'sample_size', 'Reference_seq',
'Variant_seq', 'nt_name', 'aa_name', 'hgvs_nt', 'hgvs_aa', 'hgvs_alias', 'vcf_gene', 'mutation_type',
'viral_lineage', 'alternate_frequency']
"""
import argparse
import pandas as pd
import numpy as np
import json
from functions import parse_INFO, find_sample_size, \
unnest_multi, get_unknown_labels, separate_attributes, rejoin_attributes, \
clade_defining_threshold, map_pos_to_gene_protein, add_alias_names, \
convert_amino_acid_codes, rewrite_nt_snps_as_hgvs, remove_nts_from_nt_name, \
add_hgvs_names
from functions import empty_attributes, gvf_columns, vcf_columns, pragmas
def vcftogvf(vcf, strain, GENE_PROTEIN_POSITIONS_DICT, sample_size):
vcf_df = pd.read_csv(vcf, sep='\t', names=vcf_columns)
# get variant-calling source
var_cols = get_unknown_labels(vcf_df)
# remove pragmas
vcf_df = vcf_df[~vcf_df['#CHROM'].str.contains("#")]
# restart index from 0
vcf_df = vcf_df.reset_index(drop=True)
# expand INFO column into multiple columns
vcf_df = parse_INFO(vcf_df, var_cols)
# create an empty df to make the new GVF in
new_gvf = pd.DataFrame(index=range(0, len(vcf_df)), columns=gvf_columns)
# fill in GVF columns from VCF
new_gvf['#seqid'] = vcf_df['#CHROM']
new_gvf['#source'] = '.'
new_gvf['#start'] = vcf_df['POS']
# 'end' is not used in creating the COVID-MVP heatmap,
# but is a required GVF column, so use 'POS' for 'end' as well
new_gvf['#end'] = vcf_df['POS']
new_gvf['#score'] = '.'
new_gvf['#strand'] = '+'
new_gvf['#phase'] = '.'
if "type" in vcf_df.columns: # "type" is not an attribute of INFO for wastewater
new_gvf['#type'] = vcf_df['type']
else:
new_gvf['#type'] = '.'
# fill '#attributes' column with empty key-value pairs to fill in later
new_gvf['#attributes'] = empty_attributes
# expand #attributes into columns to fill in separately
new_gvf = separate_attributes(new_gvf)
# fill in attributes from vcf_df columns by name if they exist
vcf_df_cols_to_add = ['vcf_gene', 'mutation_type',
'ps_filter', 'ps_exc', 'mat_pep','mat_pep_desc',
'mat_pep_acc', 'Reference_seq', 'Variant_seq',
"dp", "ro", "ao"]
for column in list(set(vcf_df.columns) & set(vcf_df_cols_to_add)):
# drop nans if they exist
vcf_df[column] = vcf_df[column].fillna('')
new_gvf[column] = vcf_df[column]
for column in ['nt_name', 'aa_name']:
vcf_df[column] = vcf_df[column].fillna('n/a')
new_gvf[column] = vcf_df[column]
# add other attributes
new_gvf['sample_size'] = sample_size
new_gvf['Name'] = vcf_df["Names"]
new_gvf['viral_lineage'] = strain
new_gvf['alternate_frequency'] = vcf_df["AF"]
# add gene and protein attributes from JSON
json_df = map_pos_to_gene_protein(
vcf_df['POS'].astype(int), GENE_PROTEIN_POSITIONS_DICT)
new_gvf["gene"] = json_df["gene"]
new_gvf["protein_name"] = json_df["protein_name"]
new_gvf["protein_symbol"] = json_df["protein_symbol"]
new_gvf["protein_id"] = json_df["protein_id"]
# add 'alias' column for ORF1a/b mutations
new_gvf = add_alias_names(new_gvf, GENE_PROTEIN_POSITIONS_DICT)
# add clade_defining attribute
new_gvf = clade_defining_threshold(args.clades_threshold,
new_gvf, sample_size)
# add HGVS names columns: 'hgvs_nt', 'hgvs_aa', 'hgvs_alias'
new_gvf = add_hgvs_names(new_gvf)
# save HGVS names for troubleshooting
#new_gvf[['nt_name', 'hgvs_nt', 'aa_name', 'hgvs_aa', 'alias', 'hgvs_alias']].to_csv('hgvs_troubleshooting.tsv', sep='\t')
# add 'ID' attribute: here, rows with the same entry in 'Name'
# get the same ID (should all be different)
new_gvf['ID'] = 'ID_' + new_gvf.groupby('Name', sort=False).ngroup().astype(str)
# merge attributes back into a single column
new_gvf = rejoin_attributes(new_gvf, empty_attributes)
return new_gvf
def parse_args():
parser = argparse.ArgumentParser(
description='Converts a annotated VCF file to a GVF '
'file with functional annotation')
parser.add_argument('--vcffile', type=str, default=None, required=True,
help='Path to a snpEFF-annotated VCF file')
parser.add_argument('--sample_desc', type=str, default=None, required=True,
choices=['Wastewater', 'Clinical'],
help="The sample group type")
parser.add_argument('--sample_group', type=str, default=None, required=True,
help='sample group name, ie. lineage, date range')
parser.add_argument('--size_stats', type=str, default=None,
help='Statistics file for for size extraction')
parser.add_argument('--clades_threshold', type=float,
default=0.75,
help='Alternate frequency cutoff for '
'clade-defining mutations')
parser.add_argument('--gene_positions', type=str,
default=None,
help='gene positions in JSON format')
parser.add_argument('--strain', type=str,
default=None,
help='Lineage; user mode is if strain="n/a"')
parser.add_argument("--wastewater", help="Activate wastewater data mode",
action="store_true")
parser.add_argument('--outgvf', type=str, required=True,
help='Filename for the output GVF file')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
# Reading the gene & protein coordinates of SARS-CoV-2 genome
with open(args.gene_positions) as fp:
GENE_PROTEIN_POSITIONS_DICT = json.load(fp)
# Assigning variables
vcf_file = args.vcffile
sample_desc = args.sample_desc
sample_group = args.sample_group
# If the strain and/or stats file are None, set them as 'n/a'
size_stats = args.size_stats
strain = args.strain
if size_stats == None:
size_stats='n/a'
if strain == None:
strain='n/a'
# print("Processing: " + vcf_file)
sample_size = find_sample_size(size_stats, strain, vcf_file, args.wastewater)
# create gvf from annotated vcf (ignoring pragmas for now)
gvf = vcftogvf(vcf_file, strain, GENE_PROTEIN_POSITIONS_DICT,
sample_size)
# add species to pragmas
species = GENE_PROTEIN_POSITIONS_DICT['Src']['species']
pragmas[0] = pragmas[0].str.replace("##species", "##species " + str(species))
# temporary pragma, subject to change
pragmas[0] = pragmas[0].str.replace("##sample-description", "##sample-description " + 'sample_desc=' + str(sample_desc) +';' + 'sample_group=' + str(sample_group) + ';')
# combine pragmas, header, GVF contents
final_gvf = pd.DataFrame(np.vstack([gvf.columns, gvf]))
final_gvf = pragmas.append(final_gvf)
# save GVF
filepath = args.outgvf # outdir + strain + ".annotated.gvf"
print("Saved as: ", filepath)
print("")
final_gvf.to_csv(filepath, sep='\t', index=False, header=False)
print("")
print("Processing complete.")