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

Autor: Daniel Kim, Andy Tran, Hani Jieun Kim, Yingxin Lin, Jean Yee Hwa Yang, Pengyi Yang
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: npj Systems Biology and Applications, Vol 9, Iss 1, Pp 1-13 (2023)
Druh dokumentu: article
ISSN: 2056-7189
DOI: 10.1038/s41540-023-00312-6
Popis: Abstract 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.
Databáze: Directory of Open Access Journals
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