Decoding enhancer complexity with machine learning and high-throughput discovery

Gabrielle D Smith; Wan Hern Ching; Paola Cornejo-Páramo; Emily S Wong

doi:10.1186/s13059-023-02955-4

Decoding enhancer complexity with machine learning and high-throughput discovery

Genome Biol. 2023 May 12;24(1):116. doi: 10.1186/s13059-023-02955-4.

Authors

Gabrielle D Smith^{1

2}, Wan Hern Ching¹, Paola Cornejo-Páramo^{1

2}, Emily S Wong^{3

4}

Affiliations

¹ Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, NSW, Australia.
² School of Biotechnology and Biomolecular Sciences, UNSW Sydney, Kensington, NSW, Australia.
³ Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, NSW, Australia. e.wong@victorchang.edu.au.
⁴ School of Biotechnology and Biomolecular Sciences, UNSW Sydney, Kensington, NSW, Australia. e.wong@victorchang.edu.au.

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.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

DNA* / metabolism
Enhancer Elements, Genetic*
Machine Learning

Substances

DNA