--- 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") ```