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uORF_annotator.py
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uORF_annotator.py
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from email.policy import default
import subprocess as sp
from pathlib import Path
from tempfile import NamedTemporaryFile
import pandas as pd
import numpy as np
import re
import os
from collections import defaultdict
from math import ceil
from Bio import SeqIO
from Bio.Seq import Seq, MutableSeq
import click
def process(input_vcf, bed, fasta, bed_4col_info_cols, gtf, gene_dict, h, atg_bed, non_atg_bed, utr_only) -> pd.core.frame.DataFrame:
"""find main values for further annotation"""
tmp_vcf1 = NamedTemporaryFile()
sp.run(f'cut -f-8 {input_vcf} > {tmp_vcf1.name}', shell=True)
# get intersection
tmp_out = NamedTemporaryFile()
sp.run(f'bedtools intersect -wo -a {tmp_vcf1.name} -b {bed} > {tmp_out.name}', shell=True)
source_bed_lines = {}
orf_count = 1
with open(bed, 'r') as in_bed_handle:
for line in in_bed_handle:
if line.startswith('#'):
continue
content = line.strip().split('\t')
trans_id = re.findall('[NMENST]+_*\d+', content[3])[0]
uo_name = f"{content[0]}:{content[1]}-{content[2]}({content[5]})/{trans_id}"
_, overlap_type, _, codon_type = content[3].split('|')
content[3] = f"UORF{orf_count}|{overlap_type}|{codon_type}"
orf_count += 1
source_bed_lines[uo_name] = content.copy()
# preprocessing
df = pd.read_table(tmp_out.name, header=None)
df = df.dropna()
df = df.loc[:, [0, 1, 3, 4, 7, 9, 10, 11, 13, 17, 18, 19]]
df = df.rename({0: '#CHROM', 1: 'POS', 3: 'REF', 4: 'ALT',
7: 'INFO', 9: 'orf_start', 10: 'orf_end',
11: 'bed_anno', 13:'strand', 17:'n_exons',
18: 'exons_sizes', 19: 'exons_starts'}, axis=1)
# string to list of int
df['exons_sizes'] = df['exons_sizes'].apply(lambda x: [int(i) for i in x.rstrip(',').split(',')])
df['exons_starts'] = df['exons_starts'].apply(lambda x: [int(i) for i in x.rstrip(',').split(',')])
# get columns names for bed-4-field annotations
bed_4col_values = df['bed_anno'].str.split('|', expand=True)
bed_4col_values.columns = bed_4col_info_cols
df = pd.concat([df, bed_4col_values], axis=1)
df['POS'] == df['POS'].astype(int)
df['orf_start'] == df['orf_start'].astype(int)
df['orf_end'] == df['orf_end'].astype(int)
# estimate distance
df['dist_from_orf_to_snp'] = df.apply(lambda x: x.loc['POS'] - x.loc['orf_start'] - 1 if \
x.loc['strand'] == '+' else \
x.loc['orf_end'] - x.loc['POS'],
axis = 1)
df['dist_from_orf_to_snp'] = df['dist_from_orf_to_snp'].astype(int)
# obtaining sequences from the corresponding reference genome
tmp_fasta = NamedTemporaryFile()
sp.run(f'rm -f {fasta}.fai && bedtools getfasta -s -fi {fasta} -bed {bed} > {tmp_fasta.name} 2>/dev/null',
shell=True)
uorf_dict = SeqIO.to_dict(SeqIO.parse(tmp_fasta.name, "fasta"))
tmp = NamedTemporaryFile()
tmp1 = NamedTemporaryFile()
tmp_exons_bed = NamedTemporaryFile()
tmp_exons_gtf = NamedTemporaryFile()
tmp_exons_sorted = NamedTemporaryFile()
tmp_interorf_single = NamedTemporaryFile()
tmp_io_exon_isec_tab = NamedTemporaryFile()
tmp_interorfs_bed = NamedTemporaryFile()
tmp_interorfs_bed_sorted = NamedTemporaryFile()
tmp_interorfs_full_seq = NamedTemporaryFile()
tmp_interorfs_fasta = NamedTemporaryFile()
closest_cds_data = NamedTemporaryFile()
df['zero'] = 0
df = df.astype({'orf_start':'int32', 'orf_end':'int32'})
df['name'] = df['#CHROM'] + ':' + df['orf_start'].astype(str) + '-' + df['orf_end'].astype(str) + '(' + df['strand'] + ')'
df['transcript'] = [re.findall('[NMENST]+_*\d+', x)[0] for x in df['bed_anno']]
df['gene_name'] = [gene_dict[x] for x in df['transcript']]
# print(df.head())
df['name_and_trans'] = [f'{x}/{y}' for x, y in zip(df['name'], df['transcript'])]
df.to_csv('check_temp_df.tsv', header=None, sep='\t', index=False)
df.loc[:, ['#CHROM', 'orf_start', 'orf_end', 'name_and_trans', 'zero', 'strand']]\
.to_csv(tmp.name, header=None, sep='\t', index=False)
sp.run(f'sort -k1,1 -k2,2n {tmp.name} > {tmp1.name}', shell=True)
source_gtf = pd.read_csv(gtf, sep='\t', comment='#', header=None)
source_gtf.columns = ['chr', 'src', 'type', 'start', 'end', 'score', 'strand', 'frame', 'info']
source_gtf['start'] = [int(x) - 1 for x in source_gtf['start']]
source_gtf['end'] = [int(x) for x in source_gtf['end']]
ok_rows = [bool(re.search('(NM_|ENST)\d+', x)) for x in source_gtf['info']]
source_gtf = source_gtf.loc[ok_rows]
source_gtf['transcript'] = [re.findall('transcript_id \"((NM_|ENST)\d+)', x)[0][0] for x in source_gtf['info']]
exons_gtf = source_gtf.loc[source_gtf['type'] == 'exon']
export_exons = exons_gtf.loc[:, ['chr', 'start', 'end', 'transcript', 'score', 'strand']]
export_exons.to_csv(tmp_exons_bed.name, sep='\t', header=False, index=False)
sp.run(f'sort -k1,1 -k4,4n {tmp_exons_bed.name} | uniq > {tmp_exons_sorted.name}', shell=True)
first_cds = {}
cds_gtf = source_gtf.loc[source_gtf['type'] == 'CDS']
for idx, row_data in cds_gtf.iterrows():
if row_data['transcript'] not in first_cds:
first_cds[row_data['transcript']] = row_data
continue
if (row_data['strand'] == '-') and (first_cds[row_data['transcript']]['start'] < row_data['start']):
first_cds[row_data['transcript']] = row_data
first_cds_df = pd.DataFrame(first_cds).T
first_cds_df.index = first_cds_df['transcript']
def get_true_cds_start(tr_id, first_cds_df=first_cds_df):
if tr_id not in first_cds_df.index:
return None
gtf_row = first_cds_df.loc[tr_id]
if gtf_row['strand'] == '+':
return gtf_row['start']
return gtf_row['end']
uorf_df = df.loc[:, ['#CHROM', 'transcript', 'strand', 'orf_start', 'orf_end', \
'exons_sizes', 'exons_starts', 'name', 'name_and_trans']]
uorf_df.columns = ['#CHROM', 'transcript', 'strand', 'orf_start', 'orf_end', \
'exons_sizes', 'exons_norm_starts', 'id', 'name_and_trans']
uorf_df = uorf_df.loc[uorf_df.astype(str).drop_duplicates().index]
uorf_exons = []
uorf_norm_exons = []
for idx, row_data in uorf_df.iterrows():
out_starts = []
out_norm_starts = []
orf_len = row_data.loc['orf_end'] - row_data.loc['orf_start']
if row_data.loc['strand'] == '+':
for eStart in row_data.loc['exons_norm_starts']:
out_starts.append(row_data.loc['orf_start'] + eStart)
out_norm_starts.append(eStart)
else:
for eStart, eSize in zip(row_data.loc['exons_norm_starts'], row_data['exons_sizes']):
out_starts.append(row_data.loc['orf_start'] + eStart + eSize)
out_norm_starts.append(orf_len - eStart - eSize)
out_starts = list(reversed(out_starts))
out_norm_starts = list(reversed(out_norm_starts))
uorf_exons.append(list(out_starts))
uorf_norm_exons.append(list(out_norm_starts))
uorf_df['exons_starts'] = uorf_exons
uorf_df['exons_norm_starts'] = uorf_norm_exons
uorf_esizes = []
for idx, row_data in uorf_df.iterrows():
if row_data['strand'] == '+':
uorf_esizes.append(list(row_data['exons_sizes']))
else:
uorf_esizes.append(list(reversed(row_data['exons_sizes'])))
uorf_df['exons_sizes'] = uorf_esizes
#uorf_df['exons_starts'] = [(o + x for x in e) for o, e in zip(uorf_df['orf_start'], uorf_df['exons_norm_starts'])]
uorf_df.index = uorf_df['id']
#print(uorf_df.loc['chr11:31806911-31810840(-)'])
uorf_df['cds_start'] = [get_true_cds_start(x) for x in uorf_df['transcript']]
#print(uorf_df)
interorf_data = []
for idx, row_data in uorf_df.iterrows():
if (row_data['strand'] == '+') and (row_data['orf_end'] < row_data['cds_start']):
interorf_data.append(row_data[['#CHROM', 'orf_end', 'cds_start', 'name_and_trans']].values)
elif row_data['cds_start'] < row_data['orf_start']:
interorf_data.append(row_data[['#CHROM', 'cds_start', 'orf_start', 'name_and_trans']].values)
interorf_single = pd.DataFrame(interorf_data)
interorf_single = interorf_single.dropna()
interorf_single.columns = ['chr', 'start', 'end', 'name_and_trans']
interorf_single['start'] = [int(x) for x in interorf_single['start']]
interorf_single['end'] = [int(x) for x in interorf_single['end']]
interorf_single.to_csv(tmp_interorf_single.name, sep='\t', header=False, index=False)
cmd = f"""bedtools intersect -wo -a {tmp_interorf_single.name} -b {tmp_exons_sorted.name} | tee test_isec.bed > {tmp_io_exon_isec_tab.name}"""
sp.run(cmd, shell=True)
with open(tmp_io_exon_isec_tab.name, 'r') as isec_handle, open(tmp_interorfs_bed.name, 'w') as isec_out:
for line in isec_handle:
content = line.strip().split('\t')
uorf_tr_id = content[3].split('/')[1]
if uorf_tr_id == content[7]:
rstart = max(int(content[1]), int(content[5]))
rend = min(int(content[2]), int(content[6]))
out_line = f'{content[0]}\t{rstart}\t{rend}\t{content[3]}\t{content[8]}\t{content[9]}\n'
isec_out.write(out_line)
command = f"""cat {tmp_interorfs_bed.name} | sort -k1,1 -k2,2n | uniq | tee test_io_exons.bed > {tmp_interorfs_bed_sorted.name}"""
sp.run(command, shell=True)
command = f"""rm -f {fasta}.fai && bedtools getfasta -bed {tmp_interorfs_bed_sorted.name} -fi {fasta} -s -name > {tmp_interorfs_fasta.name}""" #| sed 's/::.*//g' > {tmp_interorfs_fasta.name}"""
sp.run(command, shell=True)
### INTERORF DATA ###
interorfs_bed_list = [line.rstrip().split('\t') for line in open(tmp_interorfs_bed_sorted.name).read().split('\n') if line != '' ]
interorfs_bed_dict = defaultdict(lambda: defaultdict(list))
for bed_line in interorfs_bed_list:
key = bed_line[3]
interorfs_bed_dict[key]['chr'] = bed_line[0]
interorfs_bed_dict[key]['strand'] = bed_line[-1]
interorfs_bed_dict[key]['exons_sizes'].append(int(bed_line[2])-int(bed_line[1]))
if interorfs_bed_dict[key]['strand'] == '+':
interorfs_bed_dict[key]['exons_starts'].append(int(bed_line[1]))
elif interorfs_bed_dict[key]['strand'] == '-':
interorfs_bed_dict[key]['exons_starts'].append(int(bed_line[2]))
for k in interorfs_bed_dict.keys():
if interorfs_bed_dict[k]['strand'] == '+':
starts_sorted = sorted(interorfs_bed_dict[k]['exons_starts'])
interorfs_bed_dict[k]['exons_sizes'] = [x for _, x in sorted(zip(starts_sorted, interorfs_bed_dict[k]['exons_sizes']))]
contig = interorfs_bed_dict[k]['chr']
interorfs_bed_dict[k]['exons_norm_starts'] = [i-starts_sorted[0] for i in starts_sorted]
elif interorfs_bed_dict[k]['strand'] == '-':
size_dict = {k: v for k, v in zip(interorfs_bed_dict[k]['exons_starts'], interorfs_bed_dict[k]['exons_sizes'])}
starts_sorted = sorted(interorfs_bed_dict[k]['exons_starts'], reverse=True)
interorfs_bed_dict[k]['exons_sizes'] = [size_dict[x] for x in starts_sorted]
contig = interorfs_bed_dict[k]['chr']
interorfs_bed_dict[k]['exons_norm_starts'] = [abs(i-starts_sorted[0]) for i in starts_sorted]
interorfs_bed_dict[k]['exons_starts'] = starts_sorted
interorfs_bed_df = pd.DataFrame(interorfs_bed_dict).T
# interorfs_bed_df.columns = ['contig', 'start', 'end']
interorfs_bed_df['id'] = interorfs_bed_df.index
#print(' ======= interORF DATA ======= ')
#print(interorfs_bed_df)
#print(interorfs_bed_df.loc['chr11:31810829-31811221(-)/NM_001604'])
#####################
seen_parts = set()
records = []
############ BIG CHUNK OF NEW CODE ####################################
interorfs_full_seq = defaultdict(str)
with open(tmp_interorfs_fasta.name, "r") as split_fasta, open(tmp_interorfs_full_seq.name, 'w') as full_fasta:
for line in split_fasta:
if line.startswith('>'):
record_tmp_name = line[1:]
uorf_tmp_name = record_tmp_name.split('::')[0]
if record_tmp_name in seen_parts:
skip = True
else:
seen_parts.add(record_tmp_name)
skip = False
else:
if skip:
continue
if '(-)' in uorf_tmp_name:
interorfs_full_seq[uorf_tmp_name] = line.strip() + interorfs_full_seq[uorf_tmp_name]
else:
interorfs_full_seq[uorf_tmp_name] = interorfs_full_seq[uorf_tmp_name] + line.strip()
for interorf_seq in interorfs_full_seq:
full_fasta.write(f">{interorf_seq}\n{interorfs_full_seq[interorf_seq]}\n")
############# END OF CHUNK ############################################
cmd = f'grep -P "\tCDS\t" {gtf} | sort -k1,1 -k4,4n | uniq - > {tmp_exons_gtf.name}'
sp.run(cmd, shell=True)
# make per transcript exon dict (CDS-data)
exons_gtf_list = [line.rstrip().split('\t') for line in open(tmp_exons_gtf.name).read().split('\n') if line.startswith('chr')]
exons_gtf_dict = defaultdict(lambda: defaultdict(list))
idx = [False for _ in exons_gtf_list[0]]
idx[0], idx[3], idx[4], idx[6] = True, True, True, True
for gtf_line in exons_gtf_list:
key = re.search('transcript_id \"([^\.]+)', gtf_line[8]).group(1).strip('"')
values = [gtf_line_sub for i, gtf_line_sub in zip(idx, gtf_line) if i]
exons_gtf_dict[key]['chr'] = values[0]
exons_gtf_dict[key]['strand'] = values[-1]
exons_gtf_dict[key]['exons_sizes'].append(int(values[2])-int(values[1]) + 1)
if exons_gtf_dict[key]['strand'] == '+':
exons_gtf_dict[key]['exons_starts'].append(int(values[1]) - 1)
elif exons_gtf_dict[key]['strand'] == '-':
exons_gtf_dict[key]['exons_starts'].append(int(values[2]))
for k in exons_gtf_dict.keys():
if exons_gtf_dict[k]['strand'] == '+':
starts_sorted = sorted(exons_gtf_dict[k]['exons_starts'])
exons_gtf_dict[k]['exons_sizes'] = [x for _, x in sorted(zip(starts_sorted, exons_gtf_dict[k]['exons_sizes']))]
contig = exons_gtf_dict[k]['chr']
exons_gtf_dict[k]['id'] = f"{contig}:{starts_sorted[0]}(+)"
exons_gtf_dict[k]['exons_norm_starts'] = [i-starts_sorted[0] for i in starts_sorted]
elif exons_gtf_dict[k]['strand'] == '-':
size_dict = {k: v for k, v in zip(exons_gtf_dict[k]['exons_starts'], exons_gtf_dict[k]['exons_sizes'])}
starts_sorted = sorted(exons_gtf_dict[k]['exons_starts'], reverse=True)
exons_gtf_dict[k]['exons_starts'] = starts_sorted
exons_gtf_dict[k]['exons_sizes'] = [size_dict[x] for x in starts_sorted]
contig = exons_gtf_dict[k]['chr']
exons_gtf_dict[k]['id'] = f"{contig}:{starts_sorted[0]}(-)"
exons_gtf_dict[k]['exons_norm_starts'] = [abs(i-starts_sorted[0]) for i in starts_sorted]
### Get sequences of CDS
cds_fasta = NamedTemporaryFile()
get_cds_command = f"""rm -f {fasta}.fai && cat {gtf} | awk -F '\t' '$3~/CDS/' | awk -F '\t' '{{OFS="\t";print$1,$4-1,$5,$3,0,$7}}' | sort -k1,1 -k2,2n | uniq | bedtools getfasta -bed - -fi {fasta} -s > {cds_fasta.name}"""
sp.run(get_cds_command, shell=True)
cds_seqs = []
for record in SeqIO.parse(cds_fasta.name, "fasta"):
cds_seqs.append(record)
cds_dict = SeqIO.to_dict(cds_seqs)
def create_complete_cds(chrom, strand, eStarts, eSizes):
if chrom in ['chrM', 'chrMT', 'M', 'MT']:
return None
out_seq = ''
for i, j in zip(eStarts, eSizes):
if strand == '+':
out_seq += cds_dict[f'{chrom}:{i}-{i+j}({strand})'].seq
else:
out_seq += cds_dict[f'{chrom}:{i-j}-{i}({strand})'].seq
return out_seq
for k in exons_gtf_dict.keys():
echrom = exons_gtf_dict[k]['chr']
estrand = exons_gtf_dict[k]['strand']
estarts = exons_gtf_dict[k]['exons_starts']
esizes = exons_gtf_dict[k]['exons_sizes']
exons_gtf_dict[k]['seq'] = str(create_complete_cds(echrom, estrand, estarts, esizes))
closest_cds_dict = defaultdict(str)
with open(closest_cds_data.name, 'r') as uorf_cds_handle:
for line in uorf_cds_handle:
content = line.strip().split('\t')
if content[5] == "+":
exStart = content[7]
else:
exStart = content[8]
closest_cds_dict[content[3]] = f"{content[0]}:{exStart}({content[5]})"
exons_gtf_df = pd.DataFrame(exons_gtf_dict).T
#exons_gtf_df.index = exons_gtf_df['id']
#print(' ======= CDS DATA ======= ')
#print(exons_gtf_df)
################
for record in SeqIO.parse(tmp_interorfs_full_seq.name, "fasta"):
records.append(record)
interorfs_dict = SeqIO.to_dict(records)
# annotate variants
df[['symbol', 'consequence', 'main_cds_effect']] = df.apply(lambda x: annotate_variant(x, \
uorf_dict=uorf_dict, \
interorfs_dict=interorfs_dict, \
interorfs_bed_df=interorfs_bed_df, \
uorf_df=uorf_df, \
cds_df=exons_gtf_df, \
closest_cds_dict=closest_cds_dict, \
atg_bed=atg_bed, \
non_atg_bed=non_atg_bed, \
source_bed_lines=source_bed_lines, \
utr_only=utr_only),
axis=1)
# check overlapping with gtf annotation of CDS
df['overlapped_CDS'] = ''
df['additional_info'] = ''
if gtf is not None:
df = check_overlapping(df, gtf, h, fasta, bed_4col_info_cols)
return df
def check_overlapping(df, gtf, h, fasta, bed_4col_info_cols) -> pd.core.frame.DataFrame:
"""mark whether variants intersect with the GTF-annotation"""
# add rest obligate VCF-fields
df = df.drop('overlapped_CDS', axis=1)
check_cds_df = df.copy()
check_cds_df['ID'] = '.'
check_cds_df['QUAL'] = '.'
check_cds_df['FILTER'] = '.'
check_cds_df['FORMAT'] = '.'
check_cds_df = check_cds_df.loc[:, ['#CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO']]
# convert gtf to bed
# read gtf as tsv, skip all lines starting with '#'
df_gtf = pd.read_table(gtf, comment='#', header=None)
# subset only CDS annotation
df_gtf = df_gtf[df_gtf[2] == 'CDS']
# extract gene_id without quotes
df_gtf[8] = df_gtf[8].str.extract('gene_id [\'|\"]?([^;\"\']+)')
# create dict {'<gene_id>': [<chr>, <start>, <end>, info>]}
d = defaultdict(list)
for _, i in df_gtf.iterrows():
if i[8] in d:
if d[i[8]][1] > i[3]-1:
d[i[8]][1] = i[3]-1
if d[i[8]][2] < i[4]:
d[i[8]][2] = i[4]
continue
else:
d[i[8]] = [i[0], i[3]-1, i[4], i[6]]
# make dataframe from dict
d = pd.DataFrame(d).T
d[3] = d.index
# write temporary bed file
tmp_bed1 = NamedTemporaryFile()
pd.DataFrame(d).to_csv(tmp_bed1.name, sep='\t', index=None, header=None)
# write VCF-header and VCF-body in temparary output file
tmp_vcf2 = NamedTemporaryFile()
tmp_tsv = NamedTemporaryFile()
with open(tmp_vcf2.name, 'a') as w:
w.write(h)
check_cds_df.to_csv(w, sep='\t', index=None)
# get intersection of current VCF and input GTF annotation and obtain information about ORF interactions
tmp_bed2 = NamedTemporaryFile()
sp.run(f'sort -k1,1 -k2,2n {tmp_bed1.name} | bedtools merge -i - | sort -k1,1 -k2,2n > {tmp_bed2.name}', shell=True)
sp.run(f'bedtools intersect -wo -a {tmp_vcf2.name} -b {tmp_bed2.name} | cut -f1,2,4,5 | sort -k1,1 -k2,2n > {tmp_tsv.name}', shell=True)
check_cds_df = pd.read_table(tmp_tsv.name, header=None)
check_cds_df.columns = ['#CHROM', 'POS', 'REF', 'ALT']
check_cds_df['in_known_ORF'] = 'YES'
df = pd.merge(df, check_cds_df, on=['#CHROM', 'POS', 'REF', 'ALT'], how='left')
df = df.fillna({'in_known_ORF':'NO'})
del check_cds_df
# get intersection of current VCF and input GTF annotation and obtain information about CDS interactions
tmp_bed3 = NamedTemporaryFile()
tmp_tsv2 = NamedTemporaryFile()
sp.run(f"""grep -v '#' {gtf} | awk '$3~/CDS/ {{OFS="\t";print$1,$4-3,$5+2,$3}}' | sort -k1,1 -k2,2n | bedtools merge -i - > {tmp_bed3.name}""", shell=True)
sp.run(f'bedtools intersect -wo -a {tmp_vcf2.name} -b {tmp_bed3.name} | cut -f1,2,4,5 | sort -k1,1 -k2,2n | uniq > {tmp_tsv2.name}', shell=True)
check_cds_df2 = pd.read_table(tmp_tsv2.name, header=None)
check_cds_df2.columns = ['#CHROM', 'POS', 'REF', 'ALT']
df.to_csv('df.tsv', sep='\t', index=None)
check_cds_df2['in_known_CDS'] = 'YES'
check_cds_df2.to_csv('check_cds_df.tsv', sep='\t', index=None)
df = pd.merge(df, check_cds_df2, on=['#CHROM', 'POS', 'REF', 'ALT'], how='left')
df = df.fillna({'in_known_CDS':'NO'})
df.loc[df['in_known_ORF'] == 'NO', 'in_known_CDS'] = 'NO'
return df
def annotate_variant(x, uorf_dict, interorfs_dict, interorfs_bed_df, uorf_df, cds_df, closest_cds_dict, atg_bed, non_atg_bed, source_bed_lines, utr_only) -> pd.core.series.Series:
"""get a symbolic description of the mutations and consequences"""
main_cds_effect = ''
# checking for the effect of large deletions
if x['ALT'] == '<DEL>':
symb = '<DEL_L>'
conseq = 'large_deletion'
return pd.Series([symb, conseq, main_cds_effect], index=['symbol', 'consequence', 'main_cds_effect'])
elif (len(x['REF']) > 1) & ((x['POS']<=x['orf_start']) | (x['POS']>x['orf_end'])):
symb = '<DELB>'
conseq = 'deletion_boundary'
return pd.Series([symb, conseq, main_cds_effect], index=['symbol', 'consequence', 'main_cds_effect'])
# excluding of multinucleotide polymorphism
mnp_condition = (len(x['REF']) > 1) & (len(x['ALT']) > 1)
if mnp_condition:
symb = '<MNP>'
conseq = 'multinucleotide_polymorphism'
return pd.Series([symb, conseq, main_cds_effect], index=['symbol', 'consequence', 'main_cds_effect'])
# get fasta sequence of current ORF
orf = uorf_dict[f"{x['#CHROM']}:{int(x.loc['orf_start'])}-{int(x.loc['orf_end'])}({x.loc['strand']})"]
cds = Seq(orf.seq)
diff = abs(len(x['REF']) - len(x['ALT']))
signed_diff = len(x['REF']) - len(x['ALT'])
del_end_intron = False
if x['n_exons'] == 1:
# get position of nucl in codon
x['codon_pos'] = x['dist_from_orf_to_snp'] % 3
# get ORF-based coordinates
begin = int(x['dist_from_orf_to_snp'] - x['codon_pos'])
end = int(x['dist_from_orf_to_snp'] + (3 - x['codon_pos']))
# get sequence of reference codon from ORF
ref_codon = orf.seq[int(begin):int(end)]
aa_pos = ceil(len(orf.seq)/3)
# there is one or more introns
else:
exons_ends = [i+j for i,j in zip(x['exons_sizes'],
x['exons_starts'])]
introns_starts = [int(i) for i in exons_ends][:-1] + [None]
introns_ends = [int(i) for i in x['exons_starts'][1:]] + [None]
block_ranges = pd.DataFrame({'ex_start': x['exons_starts'],
'ex_end': exons_ends,
'int_start': introns_starts,
'int_end': introns_ends})
if x['strand'] == '-':
# swap start and end
block_ranges_tr = pd.DataFrame({'ex_start': block_ranges['ex_end'],
'ex_end': block_ranges['ex_start'],
'int_start': block_ranges['int_end'],
'int_end': block_ranges['int_start']})
# flip dataframe
block_ranges_tr = block_ranges_tr.iloc[::-1]
block_ranges_tr = block_ranges_tr.reset_index()
# subtract all coordinates from ORF length
block_ranges_tr = block_ranges_tr.apply(lambda x: len(orf.seq) - x)
# place NA from top to bottom as in initial data
block_ranges_tr.loc[:, ['int_start', 'int_end']] = block_ranges_tr.loc[:, ['int_start', 'int_end']].shift(-1)
# redifine data
block_ranges = block_ranges_tr.copy()
# add splice ranges
block_ranges['splice_upstream_start'] = block_ranges['ex_start'] - 2
block_ranges['splice_downstream_end'] = block_ranges['ex_end'] + 2
# exclusive first and last exons
block_ranges.loc[0, 'splice_upstream_start'] = None
block_ranges.loc[len(block_ranges)-1, 'splice_downstream_end'] = None
# check splice sites
splice_condition = len(block_ranges[block_ranges.eval(f'((splice_upstream_start <= {x["dist_from_orf_to_snp"]}) & '
f'({x["dist_from_orf_to_snp"]} < ex_start)) | '
f'((ex_end <= {x["dist_from_orf_to_snp"]}) & '
f'({x["dist_from_orf_to_snp"]} < splice_downstream_end))')]) != 0
if splice_condition:
symb = '<SP>'
conseq = 'splice_variant'
return pd.Series([symb, conseq, main_cds_effect], index=['symbol', 'consequence', 'main_cds_effect'])
# is SNP in intron?
intron_condition = len(block_ranges[block_ranges.eval(f'(int_start <= {x["dist_from_orf_to_snp"]}) & '
f'({x["dist_from_orf_to_snp"]} < int_end)')]) != 0
if intron_condition:
symb = '<INT>'
conseq = 'intronic_variant'
return pd.Series([symb, conseq, main_cds_effect], index=['symbol', 'consequence', 'main_cds_effect'])
else:
# get intronic nucleotide counts before exon with SNP
block_ranges['int_sum'] = (block_ranges['int_end'].shift(1) - block_ranges['int_start'].shift(1)).cumsum()
intron_nucl_count = block_ranges[block_ranges.eval(f'(ex_start <= {x["dist_from_orf_to_snp"]}) & '
f'({x["dist_from_orf_to_snp"]} < ex_end)')]['int_sum'].tolist()
intron_nucl_count = 0 if (np.isnan(intron_nucl_count[0])) | (len(intron_nucl_count) == 0) else intron_nucl_count[0]
if len(x['REF']) > len(x['ALT']):
if x['strand'] == "+":
del_end_pos = x["dist_from_orf_to_snp"] + diff
else:
del_end_pos = x["dist_from_orf_to_snp"] - diff
del_end_intron = len(block_ranges[block_ranges.eval(f'(int_start <= {del_end_pos}) & '
f'({del_end_pos} < int_end)')]) != 0
# reformat snp distance from snp[orf_coord] to snp[cds_coord]
x['dist_from_orf_to_snp'] = x['dist_from_orf_to_snp'] - intron_nucl_count
# find codon position and coordinates of begin and end
x['codon_pos'] = x['dist_from_orf_to_snp'] % 3
begin = int(x['dist_from_orf_to_snp'] - x['codon_pos'])
end = int(x['dist_from_orf_to_snp'] + (3 - x['codon_pos']))
# get CDS sequence from ORF
cds = Seq('')
cds_list = [orf.seq[i['ex_start']:i['ex_end']] for _, i in block_ranges.loc[:, ['ex_start', 'ex_end']].iterrows()]
for exon in cds_list:
cds+=exon
# get sequence of reference codon from cds
ref_codon = cds[int(begin):int(end)]
aa_pos = ceil(len(cds)/3)
# get sequence of alternative codon
ref_aa = str(ref_codon.translate())
alt_codon = MutableSeq(ref_codon)
# main condition
# classify mutations into consequence groups
if diff == 0:
# insert mutation in reference codon and get new aminoacid or stop-codon
if x['strand'] == '-':
alt_codon[int(x['codon_pos'])] = str(Seq(x['ALT']).reverse_complement())
else:
alt_codon[int(x['codon_pos'])] = x['ALT']
alt_codon = Seq(alt_codon)
alt_aa = str(alt_codon.translate())
# SNP variant
if ref_aa == alt_aa:
symb = f'{ref_aa}{aa_pos}{alt_aa}'
conseq = 'synonymous_variant'
elif (ref_aa != alt_aa) & (alt_aa != '*') & (ref_aa != '*'):
symb = f'{ref_aa}{aa_pos}{alt_aa}'
conseq = 'missense_variant'
elif (ref_aa == '*') & (alt_aa != '*'):
symb = f'*{aa_pos}{alt_aa}'
conseq = 'stop_lost'
alt_seq = cds[:-3] + str(alt_codon)
elif (ref_aa != alt_aa) & (alt_aa == '*'):
symb = f'{ref_aa}{aa_pos}*'
conseq = 'stop_gained'
x['dist_from_orf_to_snp'] = int(x['dist_from_orf_to_snp'])
if x['strand'] == "+":
alt_seq = str(cds[:x['dist_from_orf_to_snp']] + Seq(x['ALT']) + cds[x['dist_from_orf_to_snp'] + 1:])
elif x['strand'] == '-':
alt_seq = str(cds[:x['dist_from_orf_to_snp']] + Seq(x['ALT']).reverse_complement() + cds[x['dist_from_orf_to_snp'] + 1:])
alt_seq = Seq(alt_seq)
elif (ref_codon in [Seq('ATG'), Seq('CTG')]) & (alt_codon not in [Seq('ATG'), Seq('CTG')]):
symb = f'{ref_aa}{aa_pos}{alt_aa}'
conseq = 'start_lost'
# deletion/insertion/frameshift
elif (diff % 3 == 0) & (len(x['REF']) > len(x['ALT'])):
symb = '<DEL>'
conseq = 'inframe_deletion'
elif (diff % 3 == 0) & (len(x['REF']) < len(x['ALT'])):
symb = '<INS>'
conseq = 'inframe_insertion'
elif diff % 3 != 0:
if del_end_intron:
symb = '<EMD>'
conseq = 'deletion_at_exon_margin'
else:
symb = '<FS>'
conseq = 'frameshift'
x['dist_from_orf_to_snp'] = int(x['dist_from_orf_to_snp'])
if len(x['ALT']) > len(x['REF']):
if x['strand'] == "+":
alt_seq = str(cds[:x['dist_from_orf_to_snp']] + Seq(x['ALT']) + cds[x['dist_from_orf_to_snp'] + 1:])
elif x['strand'] == '-':
alt_seq = str(cds[:x['dist_from_orf_to_snp']] + Seq(x['ALT']).reverse_complement() + cds[x['dist_from_orf_to_snp'] + 1:])
alt_seq = Seq(alt_seq)
else:
if x['strand'] == "+":
alt_seq = str(cds[:x['dist_from_orf_to_snp']] + Seq(x['ALT']) + cds[x['dist_from_orf_to_snp'] + len(x['ALT']):])
elif x['strand'] == '-':
alt_seq = str(cds[:x['dist_from_orf_to_snp'] - diff] + Seq(x['ALT']).reverse_complement() + cds[x['dist_from_orf_to_snp'] + 1:])
if (conseq == 'stop_lost') | (conseq == 'frameshift') | (conseq == 'stop_gained'):
def get_intron_ln(exon_starts, exon_sizes):
if len(exon_sizes) == 1:
return []
return [exon_starts[i + 1] - exon_starts[i] - exon_sizes[i] for i in range(len(exon_sizes) - 1)]
uorf_name = f"{x['#CHROM']}:{int(x.loc['orf_start'])}-{int(x.loc['orf_end'])}({x.loc['strand']})"
uorf_len = int(x.loc['orf_end']) - int(x.loc['orf_start'])
uorf_df_row = uorf_df.drop_duplicates(subset='id', keep='last').loc[uorf_name].copy()
ORFExonSizes = uorf_df_row['exons_sizes'].copy()
ORFExonStarts = uorf_df_row['exons_starts'].copy()
ORFExonNormStarts = uorf_df_row['exons_norm_starts'].copy()
if x['strand'] == '-':
#ORFExonNormStarts = list(reversed([uorf_len - x - y for x, y in zip(ORFExonSizes, ORFExonNormStarts)]))
#ORFExonSizes = list(reversed(ORFExonSizes))
true_uorf_start = x.loc['orf_end']
true_uorf_end = x.loc['orf_start']
else:
true_uorf_start = x.loc['orf_start']
true_uorf_end = x.loc['orf_end']
ORFIntronSizes = get_intron_ln(exon_starts=ORFExonNormStarts, exon_sizes=ORFExonSizes)
transcript_id = uorf_df.loc[uorf_name]['transcript']
try:
closest_cds_row = cds_df.loc[transcript_id].copy()
CDSExonSizes = closest_cds_row.loc['exons_sizes'].copy()
CDSExonStarts = closest_cds_row.loc['exons_starts'].copy()
CDSExonNormStarts = closest_cds_row.loc['exons_norm_starts'].copy()
CDSIntronSizes = get_intron_ln(exon_starts=CDSExonNormStarts, exon_sizes=CDSExonSizes)
CDSSeq = closest_cds_row.loc['seq']
except KeyError:
print(f'WARN Did not find transcript CDS for uORF {uorf_name}!')
return pd.Series([symb, conseq, 'unassigned'], index=['symbol', 'consequence', 'main_cds_effect'])
io_name = x.loc["name_and_trans"]
gene_name = x.loc["gene_name"]
source_line = source_bed_lines[io_name].copy()
true_orf_id, overlap_type, codon_type = source_line[3].split('|')
true_orf_id = f'{true_orf_id}-{gene_name}'
source_line[3] = f'{true_orf_id}|{overlap_type}|{codon_type}'
try:
_, _, IOExonSizes, IOExonStarts, IOExonNormStarts, _ = interorfs_bed_df.loc[io_name].copy()
interorf = interorfs_dict[io_name].seq
except KeyError:
IOExonSizes, IOExonStarts, IOExonNormStarts = [[], [], []]
interorf = ''
if x.loc['strand'] == '+':
IOLen = CDSExonStarts[0] - true_uorf_end
else:
IOLen = true_uorf_end - CDSExonStarts[0]
if IOLen <= 0:
if x.loc['overlapping_type'] == 'non-overlapping':
print('WARN Saw the end of non-overlapping uORF inside CDS')
remaining_exons = CDSExonSizes.copy()
remaining_introns = CDSIntronSizes.copy()
for eID, eStart in enumerate(CDSExonStarts):
eSize = CDSExonSizes[eID]
if x.loc['strand'] == '+':
eEnd = eStart + eSize
pos_cond = (eEnd < true_uorf_end)
else:
eEnd = eStart - eSize
pos_cond = (eEnd >= true_uorf_end)
if pos_cond:
remaining_exons = remaining_exons[1:]
remaining_introns = remaining_introns[1:]
CDSSeq = CDSSeq[eSize:]
else:
extension = abs(eEnd - true_uorf_end)
remove_piece = (remaining_exons[0] - extension)
ORFExonSizes[-1] += extension
CDSExonSizes = remaining_exons[1:]
CDSIntronSizes = remaining_introns
CDSSeq = CDSSeq[remove_piece:]
#if len(remaining_introns) > 1:
# CDSIntronSizes = remaining_introns[1:]
#else:
# CDSIntronSizes = []
break
AllExons = ORFExonSizes + CDSExonSizes
AllIntrons = ORFIntronSizes + CDSIntronSizes
if (len(IOExonSizes) == 0) and (IOLen > 0):
AllExons = ORFExonSizes + CDSExonSizes
AllIntrons = ORFIntronSizes + [IOLen] + CDSIntronSizes
elif IOLen > 0:
# Getting true end of the inter-ORF region
if x.loc['strand'] == '+':
true_io_end = IOExonStarts[-1] + IOExonSizes[-1]
else:
true_io_end = IOExonStarts[-1] - IOExonSizes[-1]
# Getting IO-specific introns - left-most and right-most
if len(IOExonSizes) > 1:
IOInternalIntrons = get_intron_ln(exon_starts=IOExonNormStarts, exon_sizes=IOExonSizes)
else:
IOInternalIntrons = []
left_intron_size = abs(IOExonStarts[0] - true_uorf_end)
right_intron_size = abs(true_io_end - CDSExonStarts[0])
#if io_name == 'chr11:31806911-31810840(-)/NM_000280':
# print(uorf_name)
# print(uorf_df.loc[uorf_name])
# print(list(ORFExonStarts))
# print(ORFExonNormStarts)
# print(ORFExonSizes)
# print(IOExonStarts)
# print(IOExonNormStarts)
# print(IOExonSizes)
# print(CDSExonStarts)
# print(CDSExonNormStarts)
# print(IOInternalIntrons)
# print('## #INTRONS')
# print(ORFIntronSizes)
# print(left_intron_size)
# print(IOInternalIntrons)
# print(right_intron_size)
# print(CDSIntronSizes)
if left_intron_size == 0:
ORFExonSizes[-1] += IOExonSizes[0]
IOExonSizes = [x for idx, x in enumerate(IOExonSizes) if idx != 0]
IOIntrons = IOInternalIntrons
else:
IOIntrons = [left_intron_size] + IOInternalIntrons
if (right_intron_size == 0) and (len(IOExonSizes) > 0):
CDSExonSizes[0] += IOExonSizes[-1]
IOExonSizes = [x for idx, x in enumerate(IOExonSizes) if idx != (len(IOExonSizes) - 1)]
else:
IOIntrons.append(right_intron_size)
AllExons = ORFExonSizes + IOExonSizes + CDSExonSizes
AllIntrons = ORFIntronSizes + IOIntrons + CDSIntronSizes
checkseq = alt_seq + str(interorf)
ext_checkseq = checkseq + CDSSeq
if len(checkseq)%3 > 0:
ss = Seq(checkseq[0:-(len(checkseq)%3)])
else:
ss = Seq(checkseq)
if len(ext_checkseq)%3 > 0:
ess = Seq(ext_checkseq[0:-(len(ext_checkseq)%3)])
else:
ess = Seq(ext_checkseq)
sstr = ss.translate()
esstr = ess.translate()
if '*' in sstr:
dist_var_cds = int(f'{x["strand"]}1') * (CDSExonStarts[0] - x['POS'])
if dist_var_cds < 0 and conseq == 'stop_gained':
main_cds_effect = 'overlap_removal'
mce_short = 'activ'
else:
main_cds_effect = 'main_CDS_unaffected'
mce_short = 'unaff'
else:
#if len(checkseq)%3==0:
#print(checkseq)
#print(ss)
#print(sstr)
#print(ext_checkseq)
#print(ess)
#print(esstr)
#print(-(len(ext_checkseq)%3))
if (len(ext_checkseq)%3 == 0) and not ('*' in esstr[:-1]):
main_cds_effect = 'N-terminal_extension'
mce_short = 'ext'
else:
main_cds_effect = 'out-of-frame_overlap'
mce_short = 'overl'
if '*' not in esstr:
ExportExons = AllExons
ExportExons[-1] += 3
ExportIntrons = AllIntrons
else:
where_stop = [i for i, x in enumerate(esstr) if x == '*'][0]
stop_codon_start = (where_stop * 3) + signed_diff
ExportExons = []
ExportLength = 0
ExportIntrons = []
try:
for i, k in enumerate(AllExons):
if i >= 1:
ExportIntrons.append(AllIntrons[i - 1])
if stop_codon_start < (ExportLength + k):
ExportExons.append(stop_codon_start - ExportLength + 2)
break
ExportExons.append(k)
ExportLength += k
except:
print(f'WARN Number of introns does not match the number of exons for {uorf_name}!!')
return pd.Series([symb, conseq, 'unassigned'], index=['symbol', 'consequence', 'main_cds_effect'])
#print(f"Of a total of {len(AllExons)} remained {len(ExportExons)} due to {conseq} in {uorf_name} on strand {x.loc['strand']} with CDS effect {main_cds_effect}")
AllStarts = np.cumsum([0] + [x + y for x, y in zip(ExportExons[:-1], ExportIntrons)])
MasterLength = AllStarts[-1] + ExportExons[-1]
export_bed_line = [x.loc['#CHROM']]
if x.loc['strand'] == '+':
export_bed_line.append(x.loc['orf_start'])
export_bed_line.append(x.loc['orf_start'] + MasterLength)
ExportStarts = AllStarts
else:
export_bed_line.append(x.loc['orf_end'] - MasterLength)
export_bed_line.append(x.loc['orf_end'])
ExportStarts = list(reversed([MasterLength - x - y for x, y in zip(ExportExons, AllStarts)]))
ExportExons = list(reversed(ExportExons))
conseqs_short = {'frameshift': 'fs', 'stop_lost': 'stop_lost', 'stop_gained': 'nonsn'}
conseq_short = conseqs_short[conseq]
export_name = f"{true_orf_id}|{x.loc['#CHROM']}-{x.loc['POS']}-{x.loc['REF']}>{x.loc['ALT']}|{conseq_short}|{mce_short}"
export_bed_line.append(export_name)
export_bed_line.append(0)
export_bed_line.append(x.loc['strand'])
export_bed_line.append(export_bed_line[1])
export_bed_line.append(export_bed_line[1])
# Color annotation
if (main_cds_effect == 'N-terminal_extension') or (main_cds_effect == 'overlap_removal'):
export_bed_line.append('255,125,0')
elif main_cds_effect == 'out-of-frame_overlap':
export_bed_line.append('255,0,0')
elif main_cds_effect == 'main_CDS_unaffected':
export_bed_line.append('125,125,125')
else:
export_bed_line.append('0,0,0')
export_bed_line.append(len(ExportStarts))
export_bed_line.append(','.join([str(x) for x in ExportExons]) + ',')
export_bed_line.append(','.join([str(x) for x in ExportStarts]) + ',')
export_bed_str = '\t'.join([str(x) for x in export_bed_line])
#if io_name == 'chr11:31806911-31810840(-)/NM_000280':
# print(export_bed_str)
source_line = "\t".join(source_line)
if x.loc['strand'] == '+':
isUTR = x.loc['POS'] < CDSExonStarts[0]
else:
isUTR = x.loc['POS'] >= CDSExonStarts[0]
if isUTR and utr_only:
if x.loc['codon_type'] == 'ATG':
atg_bed.write(f'{source_line}\n')
atg_bed.write(f'{export_bed_str}\n')
else:
non_atg_bed.write(f'{source_line}\n')
non_atg_bed.write(f'{export_bed_str}\n')
# return two columns: symbolic designation of variants and consequence group
return pd.Series([symb, conseq, main_cds_effect], index=['symbol', 'consequence', 'main_cds_effect'])
if __name__ == '__main__':
@click.command()
@click.option('--input_vcf', '-i', required=True)
@click.option('--bed', '-b', required=True)
@click.option('--fasta', '-f', required=True)
@click.option('--gtf', '-g', required=True)
@click.option('--gnomad_constraint', '-gc')
@click.option('--output', '-out', required=True)
# @click.option('--output_bed', '-ob', required=True)
# @click.option('--output_vcf', '-ov', required=True)
@click.option('--utr_only', '-utr', is_flag=True, default=False)
def main(input_vcf, bed, fasta, gtf, output, utr_only, gnomad_constraint) -> None:
# HARDCODED: annotations from 4 column of bed file
bed_4col_info = '|utid|overlapping_type|dominance_type|codon_type'
bed_4col_info_cols = bed_4col_info.split('|')[1:]
gene_transcript = defaultdict(str)
with open(gtf, 'r') as gtf_handle:
for line in gtf_handle:
if 'gene_id' not in line and 'transcript_id' not in line:
continue
gene_name = re.findall('gene_id \"([^\"]+)', line)[0]
transcript_id = re.findall('transcript_id \"([^\"^\.]+)', line)[0]
gene_transcript[transcript_id] = gene_name
tmp_atg_bed = NamedTemporaryFile()
tmp_non_atg_bed = NamedTemporaryFile()
atg_bed = f'{output}.atg.bed'
non_atg_bed = f'{output}.non-atg.bed'
if os.path.isfile(atg_bed):
print('WARN Output ATG BED file exists, overwriting...')
if os.path.isfile(non_atg_bed):
print('WARN Output non-ATG BED file exists, overwriting...')
atg_bed_handle = open(tmp_atg_bed.name, 'w')
with open(atg_bed, 'w') as out_atg_handle:
out_atg_handle.write('#track name="affected ARG uORFs" description=" affected uORFs " itemRgb="On"\n')
non_atg_bed_handle = open(tmp_non_atg_bed.name, 'w')
with open(non_atg_bed, 'w') as out_non_atg_handle:
out_non_atg_handle.write('#track name="affected non-ATG uORFs" description=" affected uORFs " itemRgb="On"\n')
# get VCF-header lines
tmp_h = NamedTemporaryFile()
sp.run(f"head -5000 {input_vcf} | grep -P '^##' > {tmp_h.name}", shell=True)
with open(tmp_h.name) as f:
h = f.read()
# processing
df = process(input_vcf, bed, fasta, bed_4col_info_cols, gtf, gene_transcript, \
h, atg_bed_handle, non_atg_bed_handle, utr_only)
df.drop_duplicates(subset=['#CHROM', 'POS', 'REF', 'ALT',
'orf_start', 'orf_end'], inplace=True)
if utr_only:
df = df.loc[df['in_known_CDS'] == 'NO']
export_columns = ["#CHROM", "POS", "REF", "ALT", "orf_start", "orf_end", "strand", "gene_name", \
"transcript", "codon_type", "overlapping_type", "dist_from_orf_to_snp", \
"consequence", "main_cds_effect", "in_known_ORF", "INFO"]
if gnomad_constraint is not None:
constraint_df = pd.read_csv(gnomad_constraint, sep='\t')
pli_vals = defaultdict(lambda: None, {k: v for k, v in zip(constraint_df['gene'], constraint_df['pLI'])})
loeuf_vals = defaultdict(lambda: None, {k: v for k, v in zip(constraint_df['gene'], constraint_df['oe_lof_upper'])})
df['pLI'] = [pli_vals[x] for x in df['gene_name']]
df['LOEUF'] = [loeuf_vals[x] for x in df['gene_name']]
export_columns = export_columns[:-1] + ['pLI', 'LOEUF', 'INFO']
df_atg = df.loc[df['codon_type'] == 'ATG']
df_atg = df_atg.loc[:, export_columns]
df_non_atg = df.loc[df['codon_type'] == 'non-ATG']
df_non_atg = df_non_atg.loc[:, export_columns]
# write optional output tsv file
df_atg.to_csv(f'{output}.atg.tsv', sep='\t', index=None)
df_non_atg.to_csv(f'{output}.non-atg.tsv', sep='\t', index=None)
# get counts of variation types
print(df['consequence'].value_counts())
# write new CSQ like INFO line
df['INFO_new'] = \
[f'uBERT_uORFs={int(x["orf_start"])}|'
f'{int(x["orf_end"])}|'
f'{x["strand"]}|'
f'{x["symbol"]}|'