Deep learning in regulatory genomics: from identification to design

Hu, Xuehai; Fernie, A. R.; Yan, Jianbing

doi:10.1016/j.copbio.2022.102887

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Deep learning in regulatory genomics: from identification to design

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Fernie, A. R.
Central Metabolism, Department Gutjahr, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Citation

Hu, X., Fernie, A. R., & Yan, J. (2023). Deep learning in regulatory genomics: from identification to design. Current Opinion in Biotechnology, 79: 102887. doi:10.1016/j.copbio.2022.102887.

Cite as: https://hdl.handle.net/21.11116/0000-000C-3E44-3

Abstract

Genomics and deep learning are a natural match since both are data-driven fields. Regulatory genomics refers to functional noncoding DNA regulating gene expression. In recent years, deep learning applications on regulatory genomics have achieved remarkable advances so-much-so that it has revolutionized the rules of the game of the computational methods in this field. Here, we review two emerging trends: (i) the modeling of very long input sequence (up to 200 kb), which requires self-matched modularization of model architecture; (ii) on the balance of model predictability and model interpretability because the latter is more able to meet biological demands. Finally, we discuss how to employ these two routes to design synthetic regulatory DNA, as a promising strategy for optimizing crop agronomic properties.