EPCOTv2 Installation

This document explains how to install and run EPCOTv2, the latest version hosted on Hugging Face Spaces, supporting inference on ATAC‑seq input to predict multiple genomic modalities in both human and mouse.

Dependencies

  • Python 3.9+

  • torch (PyTorch) with GPU support recommended

  • huggingface_hub, gradio (Space frontend)

  • Standard Python libraries: numpy, scipy, einops, etc.

Installation Steps

Clone the Space repository

git clone https://huggingface.co/spaces/luosanj/EPCOTv2
cd EPCOTv2

Create and activate virtual environment

python -m venv .venv
source .venv/bin/activate

Install dependencies

pip install -r requirements.txt

Otherwise manually install core packages:

pip install torch gradio huggingface_hub numpy scipy einops

Running Locally or in Colab

You can run the Gradio demo locally or on Google Colab:

  • Local execution:

    python app.py

  • Colab:

    Open the Space and click “Open in Colab” (if enabled) to run interactive demo on GPU.

Usage Steps

  1. Prepare an ATAC‑seq input file in .pickle format using provided scripts (e.g. atac_process.py).

  2. Launch the demo either locally or via Colab.

  3. Upload the .pickle file and specify genome (human/mouse) and modalities to predict.

  4. Download outputs such as prediction archives and formatted .bigWig / .bedpe for visualization.

Notes

  • EPCOTv2 supports both human (hg38) and mouse (mm10) genomes.

  • Internal scripts use LoRA fine-tuned models for efficient inference (see curriculum/loralib/utils.py).

  • The interface is provided through Hugging Face Spaces and is built using the Gradio framework.

References