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BRANE Clust: Cluster-Assisted Gene Regulatory Network Inference Refinement.

Aurelie Pirayre, Camille Couprie, Laurent Duval

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |April 4, 2017
    PubMed
    Summary
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    BRANE Clust refines gene regulatory network inference by using cluster information to improve accuracy. This biologically-related network enhancement method reduces network indeterminacy and aids in discovering novel gene interactions.

    Area of Science:

    • Systems Biology
    • Bioinformatics
    • Computational Biology

    Background:

    • Identifying gene interactions is vital for understanding cellular regulatory processes.
    • Inferring gene regulatory networks (GRNs) is challenging due to numerous potential solutions from data.
    • Enforcing structural constraints like modularity can mitigate network indeterminacy.

    Purpose of the Study:

    • To refine gene regulatory network inference using cluster information.
    • To introduce BRANE Clust (Biologically-Related A priori Network Enhancement with Clustering) as a post-processing tool.
    • To enhance the accuracy and reduce indeterminacy in GRN inference.

    Main Methods:

    • BRANE Clust integrates cluster information as a post-processing step for existing GRN inference methods (e.g., CLR, GENIE3).

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  • Clustering is achieved by inverting a system of linear equations using a graph-Laplacian matrix that promotes modularity.
  • The approach was validated on DREAM4 and DREAM5 datasets and applied to an Escherichia coli network.

    • Main Results:

    • BRANE Clust demonstrated significant comparative improvements on DREAM datasets using objective measures.
    • The method facilitated the discovery of novel regulatory and co-expressed links in the Escherichia coli network, validated against the STRING database.
    • Comparative analyses discussed the pertinence of clustering computationally (SIMoNe, WGCNA, X-means) and biologically (RegulonDB).

    Conclusions:

    • BRANE Clust effectively refines GRN inference by leveraging modularity through clustering.
    • The approach enhances the discovery of biologically relevant gene interactions.
    • BRANE Clust offers a valuable tool for improving the accuracy and interpretability of gene regulatory networks.