Hugging Face's logo

Datasets:
songlab
/
deprecated-full-gnomad

Modalities:
Tabular
Text
Formats:
parquet
Size:
10M - 100M
Tags:
dna
variant-effect-prediction
biology
genomics
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files
xet
Community
1
Dataset Viewer (First 5GB)
Auto-converted to Parquet
chrom
stringclasses
2 values
pos
int64
10.2k
249M
ref
stringclasses
4 values
alt
stringclasses
4 values
AC
int64
1
152k
AN
int64
2
152k
AF
float64
0
1
consequence
stringlengths
9
552
GPN-MSA
float32
-13.59
11.1
1
10,181
A
T
56
520
0.107692
TF_binding_site_variant,downstream_gene_variant,regulatory_region_variant,upstream_gene_variant
-0.36925
1
10,193
A
T
1
25,386
0.000039
downstream_gene_variant,regulatory_region_variant,upstream_gene_variant
-0.429617
1
10,202
C
G
1
71,460
0.000014
downstream_gene_variant,regulatory_region_variant,upstream_gene_variant
-2.82533
1
10,367
C
T
33
133,014
0.000248
TF_binding_site_variant,downstream_gene_variant,regulatory_region_variant,upstream_gene_variant
-0.602358
1
10,376
A
C
1
49,706
0.00002
TF_binding_site_variant,downstream_gene_variant,regulatory_region_variant,upstream_gene_variant
0.382358
1
10,477
C
T
6
59,882
0.0001
downstream_gene_variant,upstream_gene_variant
-0.586057
1
10,482
G
A
2
44,458
0.000045
downstream_gene_variant,upstream_gene_variant
-0.85981
1
10,483
C
A
3
68,770
0.000044
downstream_gene_variant,upstream_gene_variant
-0.857589
1
10,483
C
G
1
68,772
0.000015
downstream_gene_variant,upstream_gene_variant
-0.35157
1
10,486
G
A
1
51,532
0.000019
downstream_gene_variant,upstream_gene_variant
-0.56156
1
10,486
G
C
2
51,532
0.000039
downstream_gene_variant,upstream_gene_variant
0.384257
1
10,489
C
T
3
75,980
0.000039
downstream_gene_variant,upstream_gene_variant
0.143523
1
10,490
G
A
19
56,492
0.000336
downstream_gene_variant,upstream_gene_variant
0.347739
1
10,490
G
T
3
56,504
0.000053
downstream_gene_variant,upstream_gene_variant
0.491265
1
10,491
C
G
1
85,100
0.000012
downstream_gene_variant,upstream_gene_variant
-0.096123
1
10,492
C
T
1,697
58,906
0.028809
downstream_gene_variant,upstream_gene_variant
-0.676649
1
10,493
C
G
59
76,212
0.000774
downstream_gene_variant,upstream_gene_variant
-0.164748
1
10,494
G
A
2
63,500
0.000031
downstream_gene_variant,upstream_gene_variant
0.305827
1
10,498
G
T
3
67,980
0.000044
downstream_gene_variant,upstream_gene_variant
0.474117
1
10,503
T
C
4
72,034
0.000056
downstream_gene_variant,upstream_gene_variant
-0.149619
1
10,504
G
C
46
72,160
0.000637
downstream_gene_variant,upstream_gene_variant
0.288704
1
10,506
C
G
1
77,026
0.000013
downstream_gene_variant,upstream_gene_variant
0.262592
1
10,511
G
C
2
75,410
0.000027
downstream_gene_variant,upstream_gene_variant
-1.173397
1
10,513
A
G
1
77,566
0.000013
downstream_gene_variant,upstream_gene_variant
0.011212
1
10,513
A
T
1
77,570
0.000013
downstream_gene_variant,upstream_gene_variant
-0.405044
1
10,514
G
C
1
76,640
0.000013
downstream_gene_variant,upstream_gene_variant
-0.225178
1
10,515
A
C
1
75,396
0.000013
downstream_gene_variant,upstream_gene_variant
0.480822
1
10,515
A
G
2
75,396
0.000027
downstream_gene_variant,upstream_gene_variant
0.389552
1
10,518
T
C
2
75,128
0.000027
downstream_gene_variant,upstream_gene_variant
-0.041974
1
10,519
G
A
1
62,980
0.000016
downstream_gene_variant,upstream_gene_variant
-1.177571
1
10,533
G
C
26
57,474
0.000452
downstream_gene_variant,upstream_gene_variant
0.030623
1
10,536
T
A
1
71,426
0.000014
downstream_gene_variant,upstream_gene_variant
0.791045
1
10,536
T
C
1
71,426
0.000014
downstream_gene_variant,upstream_gene_variant
1.160853
1
10,539
C
A
3
73,730
0.000041
downstream_gene_variant,upstream_gene_variant
-0.718854
1
10,543
G
A
3
72,936
0.000041
downstream_gene_variant,upstream_gene_variant
0.486537
1
10,550
G
A
1
71,906
0.000014
downstream_gene_variant,upstream_gene_variant
-0.987967
1
10,555
G
C
4
70,304
0.000057
downstream_gene_variant,upstream_gene_variant
-0.267843
1
10,558
G
C
1
68,376
0.000015
downstream_gene_variant,upstream_gene_variant
0.006382
1
10,563
C
A
16
67,114
0.000238
downstream_gene_variant,upstream_gene_variant
0.517038
1
10,563
C
T
3
67,120
0.000045
downstream_gene_variant,upstream_gene_variant
0.177246
1
10,565
C
T
1
66,612
0.000015
downstream_gene_variant,upstream_gene_variant
-1.272811
1
10,567
G
A
2
66,148
0.00003
downstream_gene_variant,upstream_gene_variant
-0.848526
1
10,571
C
T
1
63,484
0.000016
downstream_gene_variant,upstream_gene_variant
0.186047
1
10,575
C
G
2
60,576
0.000033
downstream_gene_variant,upstream_gene_variant
-0.753386
1
10,578
G
C
2
49,726
0.00004
downstream_gene_variant,upstream_gene_variant
0.638552
1
10,581
G
A
1
54,718
0.000018
downstream_gene_variant,upstream_gene_variant
-0.921889
1
10,582
T
C
5
53,586
0.000093
downstream_gene_variant,upstream_gene_variant
0.501172
1
10,583
G
A
325
42,096
0.00772
downstream_gene_variant,upstream_gene_variant
-1.280454
1
10,584
C
A
1
51,646
0.000019
downstream_gene_variant,upstream_gene_variant
-0.963778
1
10,590
G
C
2
49,220
0.000041
downstream_gene_variant,upstream_gene_variant
-0.033042
1
10,593
T
C
9
45,992
0.000196
downstream_gene_variant,upstream_gene_variant
0.688872
1
10,596
G
C
1
45,330
0.000022
downstream_gene_variant,upstream_gene_variant
0.310133
1
10,598
G
A
1
44,458
0.000022
downstream_gene_variant,upstream_gene_variant
-1.321086
1
10,601
G
A
1
41,000
0.000024
downstream_gene_variant,upstream_gene_variant
-1.342886
1
10,748
G
C
7
102,808
0.000068
downstream_gene_variant,upstream_gene_variant
-0.373331
1
10,762
C
G
3
107,182
0.000028
downstream_gene_variant,upstream_gene_variant
0.179545
1
10,765
G
A
1
105,572
0.000009
downstream_gene_variant,upstream_gene_variant
-1.172967
1
10,770
G
C
45
104,044
0.000433
downstream_gene_variant,upstream_gene_variant
-0.477708
1
10,777
G
C
3
87,284
0.000034
downstream_gene_variant,upstream_gene_variant
-0.352492
1
10,785
C
T
7
63,680
0.00011
downstream_gene_variant,upstream_gene_variant
-1.32269
1
10,796
G
A
1
36,246
0.000028
downstream_gene_variant,upstream_gene_variant
-1.084151
1
10,801
C
G
2
23,032
0.000087
downstream_gene_variant,upstream_gene_variant
0.369517
1
10,804
A
C
1
17,998
0.000056
downstream_gene_variant,upstream_gene_variant
1.375888
1
10,812
G
C
10
11,546
0.000866
downstream_gene_variant,upstream_gene_variant
-0.237664
1
10,817
G
A
1
9,734
0.000103
downstream_gene_variant,upstream_gene_variant
-0.522754
1
10,817
G
C
7
9,734
0.000719
downstream_gene_variant,upstream_gene_variant
-0.163188
1
10,818
G
A
6
47,878
0.000125
downstream_gene_variant,upstream_gene_variant
-0.699162
1
10,818
G
T
1
47,878
0.000021
downstream_gene_variant,upstream_gene_variant
-0.928654
1
10,825
G
A
1
47,736
0.000021
downstream_gene_variant,upstream_gene_variant
-1.117587
1
10,835
A
C
1
42,756
0.000023
downstream_gene_variant,upstream_gene_variant
0.377916
1
10,837
G
C
1
44,860
0.000022
downstream_gene_variant,upstream_gene_variant
-0.167094
1
10,847
G
C
2
43,232
0.000046
downstream_gene_variant,upstream_gene_variant
-0.061229
1
10,875
C
T
2
38,914
0.000051
downstream_gene_variant,upstream_gene_variant
-1.419405
1
10,888
G
T
1
36,510
0.000027
downstream_gene_variant,upstream_gene_variant
-0.378293
1
10,918
G
C
1
36,980
0.000027
downstream_gene_variant,upstream_gene_variant
-0.479087
1
10,928
C
A
2
29,822
0.000067
downstream_gene_variant,upstream_gene_variant
-1.045573
1
10,932
G
A
1
28,962
0.000035
downstream_gene_variant,upstream_gene_variant
-0.757578
1
10,946
A
G
1
24,878
0.00004
downstream_gene_variant,upstream_gene_variant
0.287965
1
10,959
A
G
1
19,964
0.00005
downstream_gene_variant,upstream_gene_variant
1.56442
1
10,964
G
C
19
18,646
0.001019
downstream_gene_variant,upstream_gene_variant
0.548674
1
10,972
G
A
12
17,570
0.000683
downstream_gene_variant,upstream_gene_variant
-0.979231
1
10,975
G
C
1
16,950
0.000059
downstream_gene_variant,upstream_gene_variant
-0.240667
1
10,977
G
T
1
16,102
0.000062
downstream_gene_variant,upstream_gene_variant
-1.392421
1
11,014
G
A
26
4,510
0.005765
downstream_gene_variant,upstream_gene_variant
-0.609987
1
11,021
G
C
1
3,614
0.000277
downstream_gene_variant,upstream_gene_variant
-0.148484
1
11,022
G
A
34
2,972
0.01144
downstream_gene_variant,upstream_gene_variant
-0.139792
1
11,023
G
A
1
3,280
0.000305
downstream_gene_variant,upstream_gene_variant
0.034881
1
11,030
G
A
2
1,914
0.001045
downstream_gene_variant,upstream_gene_variant
-1.76514
1
11,125
G
C
1
244
0.004098
downstream_gene_variant,upstream_gene_variant
-0.441182
1
11,134
A
G
2
226
0.00885
downstream_gene_variant,upstream_gene_variant
1.095121
1
12,998
C
G
1
111,102
0.000009
downstream_gene_variant,intron_variant&non_coding_transcript_variant,non_coding_transcript_exon_variant
-6.365894
1
13,007
G
C
1
115,132
0.000009
downstream_gene_variant,intron_variant&non_coding_transcript_variant,non_coding_transcript_exon_variant
-4.583621
1
13,014
T
C
1
116,866
0.000009
downstream_gene_variant,intron_variant&non_coding_transcript_variant,non_coding_transcript_exon_variant
-4.555737
1
13,023
T
C
1
119,678
0.000008
downstream_gene_variant,intron_variant&non_coding_transcript_variant,non_coding_transcript_exon_variant
-4.339008
1
13,031
A
G
2
121,810
0.000016
downstream_gene_variant,intron_variant&non_coding_transcript_variant,non_coding_transcript_exon_variant
-5.868319
1
13,037
G
A
2
122,950
0.000016
downstream_gene_variant,intron_variant&non_coding_transcript_variant,non_coding_transcript_exon_variant
-5.091455
1
13,047
C
A
2
124,394
0.000016
downstream_gene_variant,intron_variant&non_coding_transcript_variant,non_coding_transcript_exon_variant
-6.206705
1
13,052
G
C
4
125,182
0.000032
downstream_gene_variant,intron_variant&non_coding_transcript_variant,splice_region_variant&non_coding_transcript_exon_variant
-6.405015
1
13,053
G
C
44
125,134
0.000352
downstream_gene_variant,intron_variant&non_coding_transcript_variant,splice_donor_variant&non_coding_transcript_variant
-7.536184
1
13,073
C
A
1
126,460
0.000008
downstream_gene_variant,intron_variant&non_coding_transcript_variant
-1.72454
End of preview. Expand in Data Studio

gnomAD variants and GPN-MSA predictions

For more information check out our paper and repository.

Querying specific variants or genes

  • Install the latest tabix:
    In your current conda environment (might be slow):
    conda install -c bioconda -c conda-forge htslib=1.18
    or in a new conda environment:
    conda create -n tabix -c bioconda -c conda-forge htslib=1.18
conda activate tabix
  • Query a specific region (e.g. BRCA1), from the remote file: 
    tabix https://huggingface.co/datasets/songlab/gnomad/resolve/main/scores.tsv.bgz 17:43,044,295-43,125,364
    The output has the following columns:
    | chrom | pos | ref | alt | GPN-MSA score |
    and would start like this: 
    17      43044304        T       G       -5.10
17      43044309        A       G       -3.27

17 43044315 T A -6.84 17 43044320 T C -6.19 17 43044322 G T -5.29 17 43044326 T G -3.22 17 43044342 T C -4.10 17 43044346 C T -2.06 17 43044351 C T -0.33 17 43044352 G A 2.05

- If you want to do many queries you might want to first download the files locally
```bash
wget https://huggingface.co/datasets/songlab/gnomad/resolve/main/scores.tsv.bgz
wget https://huggingface.co/datasets/songlab/gnomad/resolve/main/scores.tsv.bgz.tbi

and then score:

tabix scores.tsv.bgz 17:43,044,295-43,125,364

Large-scale analysis

test.parquet contains coordinates, scores, plus allele frequency and consequences. Download:

wget https://huggingface.co/datasets/songlab/gnomad/resolve/main/test.parquet

Load into a Pandas dataframe:

df = pd.read_parquet("test.parquet")
Downloads last month
11
Size of the auto-converted Parquet files (First 5GB):
840 MB
Number of rows (First 5GB):
45,270,000