gReLU: A comprehensive framework for DNA sequence modeling and design

Abstract

Deep learning models are increasingly being used to perform a variety of tasks on DNA sequences, such as predicting tissue- and cell type-specific sequence activity, deriving cis-regulatory rules, predicting non-coding variant effects, and designing synthetic regulatory sequences. However, these models require specialized knowledge to build, train and interpret correctly. In addition, the field is hampered by the lack of interoperability between models and software built by different groups. Here we present gReLU, a comprehensive software framework that enables users to easily execute advanced sequence modeling pipelines, including data preprocessing, model training, hyperparameter tuning, evaluation, interpretation, variant effect prediction, and design of novel regulatory elements. The software is accompanied by a model zoo containing state-of-the-art pre-trained models that can easily be downloaded, applied, and fine-tuned. This framework and resources will accelerate research across the field of DNA sequence modeling and enable the effective design of synthetic regulatory elements.