for file in file_list:
base_file_name = os.path.splitext(file)[0]
annotation_input_file = base_file_name + '_HGVS.temp'
output_file = base_file_name + '.vcf'
# Clean LOVD data for VCF
lovd_file = pd.read_csv(file, sep=COLUMN_DELIMITER)
lovd_file = vcf.remove_malformed_fields(lovd_file)
# Output VCF variants for annotation
column_list = [
'dna_change', 'protein_change', 'var_pub_as', 'rna_change',
'db_id', 'variant_remarks', 'reference', 'frequency'
]
vcf_format = vcf.convert_to_vcf_format(lovd_file[column_list], rm,
'dna_change', 'LOVD')
vcf_column_order = [
'CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO'
]
with open(annotation_input_file, 'w') as f:
vcf_header = [
'##fileformat=VCFv4.0',
vcf.get_vcf_info_header(lovd_file[column_list], 'LOVD',
'Data from LOVD'),
'#' + '\t'.join(vcf_column_order)
]
f.write('\n'.join(vcf_header) + '\n')
vcf_format[vcf_column_order].to_csv(f,
sep=COLUMN_DELIMITER,
rm = VariantRemapper()
for file in file_list:
base_file_name = os.path.splitext(file)[0]
annotation_input_file = base_file_name + '_HGVS.temp'
output_file = base_file_name + '.vcf'
# Clean LOVD data for VCF
lovd_file = pd.read_csv(file, sep=COLUMN_DELIMITER)
lovd_file = vcf.remove_malformed_fields(lovd_file)
# Output VCF variants for annotation
column_list = ['dna_change', 'protein_change', 'var_pub_as', 'rna_change', 'db_id', 'variant_remarks', 'reference', 'frequency']
vcf_format = vcf.convert_to_vcf_format(lovd_file[column_list], rm, 'dna_change', 'LOVD')
vcf_column_order = ['CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO']
with open(annotation_input_file, 'w') as f:
vcf_header = ['##fileformat=VCFv4.0',
vcf.get_vcf_info_header(lovd_file[column_list], 'LOVD', 'Data from LOVD'),
'#' + '\t'.join(vcf_column_order)
]
f.write('\n'.join(vcf_header) + '\n')
vcf_format[vcf_column_order].to_csv(f, sep=COLUMN_DELIMITER, header=False, index=False)
# Annotate with VEP
annotate_vcf.annotate_vep(annotation_input_file, output_file)
# Combine VEP VCF output with original LOVD data