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Predicting gene expression from DNA sequence using deep learning models
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AI
DOI：10.1038/s41576-025-00841-2.png)
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Abstract
En 中文
Transcription of genes is regulated by DNA elements such as promoters and enhancers, the activity of which are in turn controlled by many transcription factors. Owing to the highly complex combinatorial logic involved, it has been difficult to construct computational models that predict gene activity from DNA sequence. Recent advances in deep learning techniques applied to data from epigenome mapping and high-throughput reporter assays have made substantial progress towards addressing this complexity. Such models can capture the regulatory grammar with remarkable accuracy and show great promise in predicting the effects of non-coding variants, uncovering detailed molecular mechanisms of gene regulation and designing synthetic regulatory elements for biotechnology. Here, we discuss the principles of these approaches, the types of training data sets that are available and the strengths and limitations of different approaches. Barbadilla-Martínez et al. review recent progress in deep-learning-based sequence-to-expression models, which predict gene expression levels solely from DNA sequence. These models are providing new insights into the complex combinatorial logic underlying cis-regulatory control of gene expression.