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# EPCOT

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7485616.svg)](https://doi.org/10.5281/zenodo.7485616)

EPCOT (comprehensively predicting <ins>EP</ins>igenome, <ins>C</ins>hromatin <ins>O</ins>rganization and <ins>T</ins>ranscription) is a comprehensive model to jointly predict epigenomic features, gene expression, high-resolution chromatin contact maps, and enhancer activities from DNA sequence and cell-type specific chromatin accessibility data.

We have developed resources to assist users in **predicting other genomic modalities from ATAC-seq**. These include a Google Colab notebook
<a target="_blank" href="https://colab.research.google.com/github/liu-bioinfo-lab/EPCOT/blob/main/gradio.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
and a webpage [https://liu-bioinfo-lab.github.io/EPCOT_APP.github.io/](https://liu-bioinfo-lab.github.io/EPCOT_APP.github.io/).

<img
  src="Data/model.png"
  title=""
  style="display: inline-block; margin: 0 auto; max-width: 300px">
  
 ## Dependencies
* einops (0.3.2)
* kipoiseq (0.5.2)
* numpy (1.19.5)
* torch (1.10.1)
* scipy (1.7.3)
* scikit-learn (1.0.2)

You can use ```conda``` and ```pip``` to install the required packages
```
conda create -n epcot python==3.9
conda activate epcot
pip install -r requirements.txt
```

## Usage

### Prepare inputs to EPCOT
Please go to the directory [Input/](https://github.com/liu-bioinfo-lab/EPCOT/tree/main/Input) for how to generate the inputs to EPCOT (one-hot repsentations of DNA sequences and normalized DNase-seq).  All the human data used in EPCOT are in reference genome hg38 and the data processing codes are also for hg38 version.

### Download the pre-training model and downstream models
You can download EPCOT models trained on DNA sequence and DNase-seq or ATAC-seq from [Google Drive](https://drive.google.com/drive/folders/1gsveyTgYwlXK5Ntnx5nLKSzIW3JvxLse?usp=share_link) or [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7485616.svg)](https://doi.org/10.5281/zenodo.7485616)

For the trained downstream models and how to train downstream models from scratch, you can go to each correspoding directory [GEP/](https://github.com/liu-bioinfo-lab/EPCOT/tree/main/GEP), [COP/](https://github.com/liu-bioinfo-lab/EPCOT/tree/main/COP), and [EAP/](https://github.com/liu-bioinfo-lab/EPCOT/tree/main/EAP).

### Tutorial
We prepare a Google Colab Notebook [EPCOT_usage.ipynb](https://github.com/liu-bioinfo-lab/EPCOT/tree/main/EPCOT_usage.ipynb)
<a target="_blank" href="https://github.com/liu-bioinfo-lab/EPCOT/tree/main/EPCOT_usage.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
to introduce how to use EPCOT to predict multiple modalities.

### Other materials
We prepare a [GitHub page](https://zzh24zzh.github.io/epcot.github.io/) to share our TF sequence binding patterns along with Tomtom motif comparison results, and we also summarize the results in an EXCEL file [motif_comparison_summary.xls](https://github.com/liu-bioinfo-lab/EPCOT/blob/main/Data/motif_comparison_summary.xls).

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