Reconstruction of gene regulatory networks from single cell transcriptomic data
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 G...
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Siberian Branch of the Russian Academy of Sciences, Federal Research Center Institute of Cytology and Genetics, The Vavilov Society of Geneticists and Breeders
2025-01-01
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| Series: | Вавиловский журнал генетики и селекции |
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| Online Access: | https://vavilov.elpub.ru/jour/article/view/4418 |
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| author | M. A. Rybakov N. A. Omelyanchuk E. V. Zemlyanskaya |
| author_facet | M. A. Rybakov N. A. Omelyanchuk E. V. Zemlyanskaya |
| author_sort | M. A. Rybakov |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ef11b429760e4f6292f16d2b61466df1 |
| institution | Kabale University |
| issn | 2500-3259 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Siberian Branch of the Russian Academy of Sciences, Federal Research Center Institute of Cytology and Genetics, The Vavilov Society of Geneticists and Breeders |
| record_format | Article |
| series | Вавиловский журнал генетики и селекции |
| spelling | doaj-art-ef11b429760e4f6292f16d2b61466df12025-02-01T09:58:14ZengSiberian Branch of the Russian Academy of Sciences, Federal Research Center Institute of Cytology and Genetics, The Vavilov Society of Geneticists and BreedersВавиловский журнал генетики и селекции2500-32592025-01-0128897498110.18699/vjgb-24-1041531Reconstruction of gene regulatory networks from single cell transcriptomic dataM. A. Rybakov0N. A. Omelyanchuk1E. V. Zemlyanskaya2Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Novosibirsk State UniversityInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of SciencesInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Novosibirsk State UniversityGene 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.https://vavilov.elpub.ru/jour/article/view/4418gene regulatory networksingle-cell datarna sequencingscrna-seqscatac-seq |
| spellingShingle | M. A. Rybakov N. A. Omelyanchuk E. V. Zemlyanskaya Reconstruction of gene regulatory networks from single cell transcriptomic data Вавиловский журнал генетики и селекции gene regulatory network single-cell data rna sequencing scrna-seq scatac-seq |
| title | Reconstruction of gene regulatory networks from single cell transcriptomic data |
| title_full | Reconstruction of gene regulatory networks from single cell transcriptomic data |
| title_fullStr | Reconstruction of gene regulatory networks from single cell transcriptomic data |
| title_full_unstemmed | Reconstruction of gene regulatory networks from single cell transcriptomic data |
| title_short | Reconstruction of gene regulatory networks from single cell transcriptomic data |
| title_sort | reconstruction of gene regulatory networks from single cell transcriptomic data |
| topic | gene regulatory network single-cell data rna sequencing scrna-seq scatac-seq |
| url | https://vavilov.elpub.ru/jour/article/view/4418 |
| work_keys_str_mv | AT marybakov reconstructionofgeneregulatorynetworksfromsinglecelltranscriptomicdata AT naomelyanchuk reconstructionofgeneregulatorynetworksfromsinglecelltranscriptomicdata AT evzemlyanskaya reconstructionofgeneregulatorynetworksfromsinglecelltranscriptomicdata |