Abstract
Gene regulatory networks (GRNs) - interpretable graph models of gene expression regulation - are a pivotal tool for understanding and investigating the mechanisms utilized by cells during development and in response to various internal and external stimuli. Historically, the first approach for the GRN reconstruction was based on the analysis of published data (including those summarized in databases). Currently, the primary GRN inference approach is the analysis of omics (mainly transcriptomic) data; a number of mathematical methods have been adapted for that. Obtaining omics data for individual cells has made it possible to conduct large-scale molecular genetic studies with an extremely high resolution. In particular, it has become possible to reconstruct GRNs for individual cell types and for various cell states. However, technical and biological features of single-cell omics data require specific approaches for GRN inference. This review describes the approaches and programs that are used to reconstruct GRNs from single-cell RNA sequencing (scRNA-seq) data. We consider the advantages of using scRNA-seq data compared to bulk RNA-seq, as well as challenges in GRN inference. We pay specific attention to state-of-the-art methods for GRN reconstruction from single-cell transcriptomes recruiting other omics data, primarily transcription factor binding sites and open chromatin profiles (scATAC-seq), in order to increase inference accuracy. The review also considers the applicability of GRNs reconstructed from single-cell omics data to recover and characterize various biological processes. Future perspectives in this area are discussed.