Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data

• Published in: NPJ systems biology and applications Vol. 9; no. 1; pp. 51 - 13
• Main Authors: Kim, Daniel, Tran, Andy, Kim, Hani Jieun, Lin, Yingxin, Yang, Jean Yee Hwa, Yang, Pengyi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.10.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/553; Associations; Binding sites; Bioinformatics; Biology; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Gene Regulatory Networks - genetics; Genes; Genomes; Life Sciences; Methods; Multiomics; Review; Review Article; Statistics; Systems Biology
• ISSN: 2056-7189; 2056-7189
• DOI: 10.1038/s41540-023-00312-6

Inferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims to unravel the complex relationships between genes and their regulators. Deciphering these networks plays a critical role in understanding the underlying regulatory crosstalk that drives many cellular processes and diseases. Recent advances in sequencing technology have led to the development of state-of-the-art GRN inference methods that exploit matched single-cell multi-omic data. By employing diverse mathematical and statistical methodologies, these methods aim to reconstruct more comprehensive and precise gene regulatory networks. In this review, we give a brief overview on the statistical and methodological foundations commonly used in GRN inference methods. We then compare and contrast the latest state-of-the-art GRN inference methods for single-cell matched multi-omics data, and discuss their assumptions, limitations and opportunities. Finally, we discuss the challenges and future directions that hold promise for further advancements in this rapidly developing field.