https://github.com/vccri/ssnvs
https://github.com/vccri/ssnvs
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/vccri/ssnvs
- Owner: VCCRI
- License: mit
- Created: 2023-10-29T08:44:26.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-21T07:04:15.000Z (over 1 year ago)
- Last Synced: 2025-02-02T16:52:50.606Z (4 months ago)
- Language: Python
- Size: 57.2 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Code for "Quantifying negative selection on synonymous variants" (HGG Advances, 2024)
## The "Data" pipeline
`data/` contains [Hail](https://hail.is/) and Snakemake code that requires execution in Google Cloud and saves files to a Google Storage bucket. Copies of the generated files are available in `files/`.
### How to run
1. Create a new cluster: `hailctl dataproc start --packages snakemake==6.6.1 --requester-pays-allow-buckets gnomad-public-requester-pays --project --bucket --region --num-workers --image-version=2.0.27-debian10`
- as of this writing, the command runs `gcloud dataproc clusters create` and installs the following packages: `--metadata=^|||^WHEEL=gs://hail-common/hailctl/dataproc/0.2.74/hail-0.2.74-py3-none-any.whl|||PKGS=aiohttp==3.7.4|aiohttp_session>=2.7,<2.8|asyncinit>=0.2.4,<0.3|bokeh>1.3,<2.0|boto3>=1.17,<2.0|botocore>=1.20,<2.0|decorator<5|Deprecated>=1.2.10,<1.3|dill>=0.3.1.1,<0.4|gcsfs==0.8.0|fsspec==0.9.0|humanize==1.0.0|hurry.filesize==0.9|janus>=0.6,<0.7|nest_asyncio|numpy<2|pandas>=1.1.0,<1.1.5|parsimonious<0.9|PyJWT|python-json-logger==0.1.11|requests==2.25.1|scipy>1.2,<1.7|tabulate==0.8.3|tqdm==4.42.1|google-cloud-storage==1.25.*`
- access to some annotation tables may also require adding `--requester-pays-allow-annotation-db`
2. Connect to the cluster: `gcloud beta compute ssh @-m --zone "" --project ""`
3. `git clone` this repository and navigate to `data/`
4. Run the pipeline: `snakemake --cores all --configfile config.yaml --config gcp_rootdir="/some_directory/" gcp_username=""`
The pipeline uses pre-calculated SpliceAI scores from *Jaganathan et al. Cell 2019*. Please, copy `exome_spliceai_scores.vcf.gz` from https://basespace.illumina.com/s/5u6ThOblecrh (supplementary data) to your preferred GS bucket and provide a path to it as an additional `--config` argument for Snakemake: `spliceai_scores_path="gs:///some_directory/exome_spliceai_scores.vcf.gz"`.
## The "Analysis" pipeline
`analysis/` contains scripts that reproduce the figures from the main paper and the supplement using files created in `data/`.
### How to run
`snakemake --cores all --config gcp_rootdir="/some_directory/" gcp_username=""`