Datasets:
File size: 2,065 Bytes
2a23edf dc09857 2a23edf aec3818 d7b7128 aec3818 f6544f0 7d14a7e f6544f0 aec3818 f6544f0 6aa33c0 f6544f0 aec3818 2a23edf aec3818 |
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 |
---
license: mit
tags:
- dna
- variant-effect-prediction
- biology
- genomics
---
# gnomAD variants and GPN-MSA predictions
For more information check out our [paper](https://doi.org/10.1101/2023.10.10.561776) and [repository](https://github.com/songlab-cal/gpn).
## Querying specific variants or genes
- Install the latest [tabix](https://www.htslib.org/doc/tabix.html):
In your current conda environment (might be slow):
```bash
conda install -c bioconda -c conda-forge htslib=1.18
```
or in a new conda environment:
```bash
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:
```bash
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:
```tsv
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:
```bash
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:
```python
df = pd.read_parquet("test.parquet")
``` |