Deep Learning Sequence Models for Transcriptional Regulation

作者：Ksenia Sokolova, Kathleen Chen, Yun Hao, Jian Zhou, Olga G. Troyanskaya · 发表于：Annual Review of Genomics and Human Genetics · 年份：2024 · DOI：10.1146/annurev-genom-021623-024727 · 被引用次数：32 · 研究领域：Cancer-related molecular mechanisms research、RNA modifications and cancer、RNA and protein synthesis mechanisms

Deciphering the regulatory code of gene expression and interpreting the transcriptional effects of genome variation are critical challenges in human genetics. Modern experimental technologies have resulted in an abundance of data, enabling the development of sequence-based deep learning models that link patterns embedded in DNA to the biochemical and regulatory properties contributing to transcriptional regulation, including modeling epigenetic marks, 3D genome organization, and gene expression, with tissue and cell-type specificity. Such methods can predict the functional consequences of any noncoding variant in the human genome, even rare or never-before-observed variants, and systematically characterize their consequences beyond what is tractable from experiments or quantitative genetics studies alone. Recently, the development and application of interpretability approaches have led to the identification of key sequence patterns contributing to the predicted tasks, providing insights into the underlying biological mechanisms learned and revealing opportunities for improvement in future models.