Mapping gene regulatory networks from single-cell omics data
Mark W E J Fiers1, Liesbeth Minnoye1,2, Sara Aibar1,2
1VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium.
Briefings in Functional Genomics
|January 18, 2018
Summary
This review explores advanced single-cell methods for mapping gene regulatory networks (GRNs). These techniques help understand cellular diversity by analyzing gene expression and epigenetic data.
Area of Science:
- Genomics and Molecular Biology
- Computational Biology and Bioinformatics
Background:
- Single-cell technologies offer deep insights into cellular heterogeneity.
- Understanding gene regulatory networks (GRNs) is key to explaining this heterogeneity.
Purpose of the Study:
- To review emerging methods for mapping GRNs from single-cell transcriptomics data.
- To discuss the utility of single-cell epigenomics for deciphering gene regulatory programs.
- To highlight future directions in GRN inference using multi-omics and perturbation techniques.
Main Methods:
- Analysis of single-cell transcriptomics data, addressing noise and sparsity.
- Application of single-cell epigenomic techniques like ATAC-seq and DNA methylation profiling.
- Integration of multi-omics and perturbation data for enhanced GRN inference.
Main Results:
- Emerging methods effectively map GRNs from noisy, sparse single-cell transcriptomics data.
- Single-cell epigenomics provides crucial insights into gene regulatory mechanisms.
- Future techniques promise more comprehensive GRN inference.
Conclusions:
- Single-cell transcriptomics and epigenomics are powerful tools for mapping GRNs.
- Advancements in multi-omics and perturbation technologies will drive future GRN inference.
- These integrated approaches are essential for mechanistically understanding cellular heterogeneity.
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