Borzoi - Predicting RNA-seq from DNA Sequence

Code repository for Borzoi models, which are convolutional neural networks trained to predict RNA-seq coverage at 32bp resolution given 524kb input sequences. The model is described in the following bioRxiv preprint:

https://www.biorxiv.org/content/10.1101/2023.08.30.555582v1.

Borzoi was trained on a large set of RNA-seq experiments from ENCODE and GTEx, as well as re-processed versions of the original Enformer training data (including ChIP-seq and DNase data from ENCODE, ATAC-seq data from CATlas, and CAGE data from FANTOM5). Click here for a list of trained-on experiments.

The repository contains example usage code (including jupyter notebooks for predicting and visualizing genetic variants) as well as links for downloading model weights, training data, QTL benchmark tasks, etc.

Contact drk (at) @calicolabs.com or jlinder (at) @calicolabs.com for questions about the model or data.

Installation

Borzoi depends on the baskerville repository, which can be installed by issuing the following commands:

git clone https://github.com/calico/baskerville.git
cd baskerville
pip install -e .

Next, install the borzoi repository by issuing the following commands:

git clone https://github.com/calico/borzoi.git
cd borzoi
pip install -e .

These repositories further depend on a number of python packages (which are automatically installed with borzoi). See setup.cfg for a complete list. The most important version dependencies are:

Python == 3.9
Tensorflow == 2.12.x (see https://www.tensorflow.org/install/pip)

Note: The example notebooks require jupyter, which can be installed with pip install notebook.
A new conda environment can be created with conda create -n borzoi_py39 python=3.9.