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 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. Regulatory circuits of gene expression can be represented as gene regulatory networks (GRNs) that are useful to understand cellular identity and disease. Here, the authors review the computational methods used to infer GRNs - in particular from single-cell multi-omics data - as well as the biological insights that they can provide, and methods for their downstream analysis and experimental assessment.

Automated summary

Gene regulatory networks (GRNs) are complex regulatory circuits involving chromatin, transcription factors, and genes. Studying GRNs helps understand the establishment, maintenance, and disruption of cellular identity in disease. GRNs can be inferred from experimental or literature data, and the latest computational methods utilize single-cell multi-omics data to achieve unprecedented resolution in GRN inference.