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Reconstruction of composite regulator-target splicing networks from high-throughput transcriptome data
BMC Genomics
|October 10, 2015
Summary
We developed a computational framework to model complex splicing regulation networks using transcriptomic data. This approach identifies key regulators and modules critical for Drosophila development, revealing novel insights into gene expression dynamics.
Area of Science:
- Computational Biology
- Systems Biology
- Genomics
Background:
- Splicing regulation is a complex process crucial for gene expression.
- Understanding these dynamic relationships requires advanced computational tools.
Purpose of the Study:
- To present a computational framework for modeling splicing regulatory networks.
- To identify key regulators and modules within these networks.
- To apply the framework to Drosophila development.
Main Methods:
- Utilized whole-genome transcriptomic data (gene expression and alternative splicing).
- Employed Graphical Model Selection to infer network structure.
- Applied community detection and social network analysis to identify modules and key actors.
Main Results:
- Constructed a comprehensive splicing regulatory network for Drosophila development.
- Identified modules linked to key developmental stages and processes.
- Highlighted known and novel splicing regulators, including those unassociated with development.
Conclusions:
- The framework effectively models complex splicing regulatory networks.
- The identified network provides insights into the genetic circuitry of development.
- The approach successfully captures true regulatory relationships validated by knockdown data.
