zb-deepmind-terminator

Overview

This repository utilizes NVIDIA's Nucleotide Transformer to generate positional embeddings for a MPRA (Massively Parallel Reporter Assay) 3' sequence dataset. The goal is to explore the correlation between 3' sequences and gene expression.

Resources

• Nucleotide Transformer: GitHub Repository
• Dataset Source Paper: PLOS Genetics Article

Note: Conda environments and Dockerfiles are currently available for use :)

Setup Instructions

To set up a quick conda environment for this project, follow these steps:

1. Clone the Repository:

git clone https://github.com/zbates1/zb-deepmind-terminator.git ./zb-deepmind-terminator && cd ./zb-deepmind-terminator

2. Create the conda env

conda create -p ./envs/zb-terminator python=3.9

3. Activate Env

conda activate ./envs/zb-terminator

4. Install Scipy with Conda

conda install scipy

5. Clone the Nucleotide Transformer Repo

git clone https://github.com/instadeepai/nucleotide-transformer.git ./nucleotide-transformer

6. Use the native Nucleotide-Transformer setup.py file to install dependencies

python3 ./nucleotide-transformer/setup.py install

7. Then install the Nucleotide-Transformer Package=0.0.1

cd nucleotide-transformer && pip install . && cd ..

8. Install dependencies

pip install -r requirements.txt

9. Install CUDA-enabled JAX

pip install --upgrade "jax[cuda11_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

Dockerfile

Note: The Dockerfile is currently only supporting Jax implementation. The following commands are what I used to build the container

1. Build the image

docker build -t jax_test -f ./Dockerfile.jax .

2. Containerize

docker run -it jax_test:latest /bin/bash -c "conda init bash && conda activate /task/envs/zb-terminator && python3 run_inference.py"

Embeddings Generation

Now you can use my pipeline to generate embeddings for the Shalem dataset (provided) and do the rest of the analysis.

Note: If you would like to run your own dataset: 1. format correctly 2. pass --input_filename /path/to/your/ds/in/./data

Your dataset should be: a text or csv file and have two columns: ['sequences'] and ['gene expression (tpm)']