Update README.md

README.md

@@ -24,7 +24,7 @@ configs:
     path: "complex_traits_all/test.parquet"
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 # 🧬 TraitGym
- [Benchmarking DNA Sequence Models for Causal Regulatory Variant Prediction in Human Genetics](https://www.biorxiv.org/content/10.1101/2025.02.11.637758v1)
+[Benchmarking DNA Sequence Models for Causal Regulatory Variant Prediction in Human Genetics](https://www.biorxiv.org/content/10.1101/2025.02.11.637758v1)
 
 🏆 Leaderboard: https://huggingface.co/spaces/songlab/TraitGym-leaderboard
 
@@ -74,6 +74,50 @@ configs:
 - Tries to follow [recommended Snakemake structure](https://snakemake.readthedocs.io/en/stable/snakefiles/deployment.html)
 - GPN-Promoter code is in [the main GPN repo](https://github.com/songlab-cal/gpn)
 
+### Installation
+First, clone the repo and `cd` into it.  
+Second, install the dependencies:
+```bash
+conda env create -f workflow/envs/general.yaml
+conda activate TraitGym
+```
+Optionally, download precomputed datasets and predictions (6.7G):
+```bash
+mkdir -p results/dataset
+huggingface-cli download songlab/TraitGym --repo-type dataset --local-dir results/dataset/
+```
+
+### Running
+To compute a specific result, specify its path:
+```bash
+snakemake --cores all <path>
+```
+Example paths (these are already computed):
+```bash
+# zero-shot LLR
+results/dataset/complex_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA_absLLR.plus.score.csv
+# logistic regression/linear probing
+results/dataset/complex_traits_matched_9/AUPRC_by_chrom_weighted_average/all/GPN-MSA.LogisticRegression.chrom.csv
+```
+We recommend the following:
+```bash
+# Snakemake sometimes gets confused about which files it needs to rerun and this forces
+# not to rerun any existing file
+snakemake --cores all <path> --touch
+# to output an execution plan
+snakemake --cores all <path> --dry-run
+```
+To evaluate your own set of model features, place a dataframe of shape `n_variants,n_features` in `results/dataset/{dataset}/features/{features}.parquet`.  
+For zero-shot evaluation of column `{feature}` and sign `{sign}` (`plus` or `minus`), you would invoke:
+```bash
+snakemake --cores all results/dataset/{dataset}/{metric}/all/{features}.{sign}.{feature}.csv
+```
+To train and evaluate a logistic regression model, you would invoke:
+```bash
+snakemake --cores all results/dataset/{dataset}/{metric}/all/{feature_set}.LogisticRegression.chrom.csv
+```
+where `{feature_set}` should first be defined in `feature_sets` in `config/config.yaml` (this allows combining features defined in different files).
+
 ## Citation
 [Link to paper](https://www.biorxiv.org/content/10.1101/2025.02.11.637758v1)
 ```bibtex