Deep learning for inferring transcription factor binding sites

Abstract

Deep learning is a powerful tool for predicting transcription factor binding sites from DNA sequence. Despite their high predictive accuracy, there are no guarantees that a high-performing deep learning model will learn causal sequence-function relationships. Thus, a move beyond performance comparisons on benchmark data sets is needed. Interpreting model predictions is a powerful approach to identify which features drive performance gains and ideally provide insight into the underlying biological mechanisms. Here, we highlight timely advances in deep learning for genomics, with a focus on inferring transcription factor binding sites. We describe recent applications, model architectures, and advances in ‘local’ and ‘global’ model interpretability methods and then conclude with a discussion on future research directions.