Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-10-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-023-00312-6 |
| _version_ | 1797558824895250432 |
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| author | Daniel Kim Andy Tran Hani Jieun Kim Yingxin Lin Jean Yee Hwa Yang Pengyi Yang |
| author_facet | Daniel Kim Andy Tran Hani Jieun Kim Yingxin Lin Jean Yee Hwa Yang Pengyi Yang |
| author_sort | Daniel Kim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T17:36:51Z |
| format | Article |
| id | doaj.art-c501d927ae964ad8979177ec938ad4f5 |
| institution | Directory Open Access Journal |
| issn | 2056-7189 |
| language | English |
| last_indexed | 2024-03-10T17:36:51Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj.art-c501d927ae964ad8979177ec938ad4f52023-11-20T09:49:55ZengNature Portfolionpj Systems Biology and Applications2056-71892023-10-019111310.1038/s41540-023-00312-6Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic dataDaniel Kim0Andy Tran1Hani Jieun Kim2Yingxin Lin3Jean Yee Hwa Yang4Pengyi Yang5School of Mathematics and Statistics, University of SydneySchool of Mathematics and Statistics, University of SydneyComputational Systems Biology Unit, Children’s Medical Research Institute, University of SydneySchool of Mathematics and Statistics, University of SydneySchool of Mathematics and Statistics, University of SydneySchool of Mathematics and Statistics, University of SydneyAbstract 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.https://doi.org/10.1038/s41540-023-00312-6 |
| spellingShingle | Daniel Kim Andy Tran Hani Jieun Kim Yingxin Lin Jean Yee Hwa Yang Pengyi Yang Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data npj Systems Biology and Applications |
| title | Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data |
| title_full | Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data |
| title_fullStr | Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data |
| title_full_unstemmed | Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data |
| title_short | Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data |
| title_sort | gene regulatory network reconstruction harnessing the power of single cell multi omic data |
| url | https://doi.org/10.1038/s41540-023-00312-6 |
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