Decoding enhancer complexity with machine learning and high-throughput discovery

Smith, Gabrielle D. and Ching, Wan Hern and Cornejo-Páramo, Paola and Wong, Emily S. (2023) Decoding enhancer complexity with machine learning and high-throughput discovery. Genome Biology, 24 (1). ISSN 1474-760X

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Link to published document: http://doi.org/10.1186/s13059-023-02955-4

Abstract

Enhancers are genomic DNA elements controlling spatiotemporal gene expression. Their flexible organization and functional redundancies make deciphering their sequence-function relationships challenging. This article provides an overview of the current understanding of enhancer organization and evolution, with an emphasis on factors that influence these relationships. Technological advancements, particularly in machine learning and synthetic biology, are discussed in light of how they provide new ways to understand this complexity. Exciting opportunities lie ahead as we continue to unravel the intricacies of enhancer function.

Item Type: Article
Subjects: R Medicine > R Medicine (General)
Depositing User: Repository Administrator
Date Deposited: 10 Jul 2023 04:20
Last Modified: 10 Jul 2023 05:18
URI: http://eprints.victorchang.edu.au/id/eprint/1419

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