New framework enhances DNA sequence modeling and variant prediction, suggesting improved interoperability.
Deep learning models trained on DNA sequences can predict cell-type-specific regulatory activity, reveal cis-regulatory grammar, prioritize genetic variants and design synthetic DNA. However, building and interpreting these models correctly remains difficult, and models and software built by different groups are often not interoperable. Here we present gReLU, a comprehensive software framework that enables advanced sequence modeling pipelines, including data preprocessing, modeling, evaluation, interpretation, variant effect prediction and regulatory element design. gReLU advances deep-learning-based modeling and analysis of DNA sequences with comprehensive toolsets and versatile applications.
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Lal et al. (2025) studied this question.
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