metadata

license: mit
tags:
  - dna
  - variant-effect-prediction
  - biology
  - genomics

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")