Cover Image

Gene regulatory network inference in the era of single-cell multi-omics

The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred...

Full description

Saved in:
Bibliographic Details
Main Authors: Badia-i-Mompel, Pau (Author) , Wessels, Lorna (Author) , Müller-Dott, Sophia (Author) , Trimbour, Rémi (Author) , Ramirez Flores, Ricardo O. (Author) , Argelaguet, Ricard (Author) , Sáez Rodríguez, Julio (Author)
Format: Article (Journal)
Language:English
Published: November 2023
In: Nature reviews. Genetics
Year: 2023, Volume: 24, Issue: 11, Pages: 739-754
ISSN:1471-0064
DOI:10.1038/s41576-023-00618-5
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/s41576-023-00618-5
Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/s41576-023-00618-5
Get full text
Author Notes:Pau Badia-i-Mompel, Lorna Wessels, Sophia Müller-Dott, Rémi Trimbour, Ricardo O. Ramirez Flores, Ricard Argelaguet & Julio Saez-Rodriguez
Description
Summary:The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data — historically, bulk omics data — and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.
Item Description:Online veröffentlicht: 26. Juni 2023
Gesehen am 21.05.2024
Physical Description:Online Resource
ISSN:1471-0064
DOI:10.1038/s41576-023-00618-5

Similar Items