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Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data
Yang Li1, Anjun Ma1,2, Yizhong Wang3
1Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, United States.
Briefings in Bioinformatics
|July 31, 2024
Summary
This study introduces STREAM, a new method for inferring gene regulatory networks from single-cell data. STREAM accurately identifies transcription factor-enhancer-gene relationships, advancing our understanding of gene regulation.
Area of Science:
- Genomics
- Systems Biology
- Computational Biology
Background:
- Understanding gene regulatory programs requires deciphering complex relationships between transcription factors (TFs), enhancers, and genes.
- Inference of enhancer-driven gene regulatory networks (eGRNs) is critical for biological systems analysis.
Purpose of the Study:
- To introduce STREAM, a novel computational method for inferring eGRNs.
- To enhance the discovery of TF-enhancer-gene relationships using single-cell data.
Main Methods:
- STREAM utilizes a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization.
- The method integrates jointly profiled single-cell transcriptome and chromatin accessibility data.
Main Results:
- STREAM outperforms existing methods in TF recovery, TF-enhancer linkage prediction, and enhancer-gene relation discovery.
- The method successfully identified TF-enhancer-gene relations associated with pseudotime in biological datasets.
- STREAM revealed key TF-enhancer-gene interactions and TF cooperation in tumor cells.
Conclusions:
- STREAM provides a powerful new tool for inferring eGRNs from complex single-cell data.
- The method has significant applications in understanding gene regulation in diseases like Alzheimer's and lymphoma.
