-
Notifications
You must be signed in to change notification settings - Fork 0
/
uORF_annotator.py
1063 lines (919 loc) · 59.2 KB
/
uORF_annotator.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
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)
cds_gtf = source_gtf.loc[source_gtf['type'] == 'CDS']
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(-)'])
first_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
#print(first_cds_df)
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['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"]}|'