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--- |
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license: mit |
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tags: |
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- dna |
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- variant-effect-prediction |
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- biology |
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- genomics |
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--- |
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# gnomAD variants and GPN-MSA predictions |
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For more information check out our [paper](https://doi.org/10.1101/2023.10.10.561776) and [repository](https://github.com/songlab-cal/gpn). |
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## Querying specific variants or genes |
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- Install the latest [tabix](https://www.htslib.org/doc/tabix.html): |
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In your current conda environment (might be slow): |
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```bash |
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conda install -c bioconda -c conda-forge htslib=1.18 |
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``` |
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or in a new conda environment: |
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```bash |
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conda create -n tabix -c bioconda -c conda-forge htslib=1.18 |
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conda activate tabix |
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``` |
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- Query a specific region (e.g. BRCA1), from the remote file: |
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```bash |
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tabix https://huggingface.co/datasets/songlab/gnomad/resolve/main/scores.tsv.bgz 17:43,044,295-43,125,364 |
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``` |
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The output has the following columns: |
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| chrom | pos | ref | alt | GPN-MSA score | |
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and would start like this: |
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```tsv |
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17 43044304 T G -5.10 |
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17 43044309 A G -3.27 |
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17 43044315 T A -6.84 |
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17 43044320 T C -6.19 |
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17 43044322 G T -5.29 |
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17 43044326 T G -3.22 |
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17 43044342 T C -4.10 |
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17 43044346 C T -2.06 |
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17 43044351 C T -0.33 |
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17 43044352 G A 2.05 |
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``` |
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- If you want to do many queries you might want to first download the files locally |
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```bash |
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wget https://huggingface.co/datasets/songlab/gnomad/resolve/main/scores.tsv.bgz |
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wget https://huggingface.co/datasets/songlab/gnomad/resolve/main/scores.tsv.bgz.tbi |
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``` |
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and then score: |
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```bash |
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tabix scores.tsv.bgz 17:43,044,295-43,125,364 |
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``` |
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## Large-scale analysis |
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`test.parquet` contains coordinates, scores, plus allele frequency and consequences. |
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Download: |
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``` |
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wget https://huggingface.co/datasets/songlab/gnomad/resolve/main/test.parquet |
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``` |
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Load into a Pandas dataframe: |
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```python |
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df = pd.read_parquet("test.parquet") |
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``` |
