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BinAligner: a heuristic method to align biological networks.

Jialiang Yang, Jun Li, Stefan Grünewald

    BMC Bioinformatics
    |November 26, 2013
    PubMed
    Summary
    This summary is machine-generated.

    BinAligner aligns molecular networks across species, revealing conserved pathways and functional orthologs even with low sequence similarity. This computational approach aids in understanding evolutionary relationships and viral infection mechanisms.

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    Area of Science:

    • Computational biology
    • Bioinformatics
    • Systems biology

    Background:

    • High-throughput omics technologies enable molecular interaction characterization across species.
    • Comparing molecular networks aids in identifying orthologs, conserved modules, and evolutionary insights.
    • Network alignment is computationally challenging due to network complexity and high costs.

    Purpose of the Study:

    • Develop an efficient algorithm for cross-species network alignment.
    • Improve the detection of functional orthologs and conserved pathways.
    • Overcome limitations of sequence similarity-based comparisons.

    Main Methods:

    • Introduced BinAligner, a hybrid global-local algorithm for network alignment.
    • Utilized 1-neighborhood subnetwork and graphlet scoring schemes alongside vertex scoring.
    • Formulated alignment as an assignment problem solved by combinatorial optimization (Hungarian method).

    Main Results:

    • Applied BinAligner to protein-protein interaction networks of Kaposi's sarcoma-associated herpesvirus (KSHV) and varicella zoster virus (VZV).
    • Identified functional orthologous proteins with low sequence similarity, such as KSHV ORF56/VZV ORF55 and KSHV ORF75/VZV ORF44.
    • Discovered a conserved pathway of 7 orthologous protein pairs crucial for viral packing and infection.

    Conclusions:

    • BinAligner effectively aligns complex biological networks, identifying functional relationships missed by sequence similarity.
    • The algorithm provides valuable insights into viral evolution and conserved molecular mechanisms.
    • This approach enhances the understanding of cross-species molecular interactions and potential therapeutic targets.