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DualAligner: a dual alignment-based strategy to align protein interaction networks.

Boon-Siew Seah1, Sourav S Bhowmick1, C Forbes Dewey1

  • 1Division of Software and Information Systems, School of Computer Engineering, Nanyang Technological University, Singapore-MIT Alliance, Nanyang Technological University, Singapore 639798 and Biological Engineering Department, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA Division of Software and Information Systems, School of Computer Engineering, Nanyang Technological University, Singapore-MIT Alliance, Nanyang Technological University, Singapore 639798 and Biological Engineering Department, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

Bioinformatics (Oxford, England)
|May 30, 2014
PubMed
Summary

This study introduces DualAligner, a novel network alignment method that combines protein-to-protein and region-to-region alignments. This dual approach improves accuracy by leveraging high-confidence data and functional regions for robust biological predictions.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Protein-protein interaction (PPI) network alignment is crucial for comparative analysis across species and conditions.
  • High-throughput PPI data contain false positives/negatives, hindering accurate full network alignment.
  • Existing methods either ignore low-confidence mappings or align all proteins, limiting accuracy.

Purpose of the Study:

  • To develop a novel network alignment strategy that balances specificity and comprehensiveness.
  • To enable accurate biological predictions by aligning networks at multiple granularities.
  • To address limitations of existing protein-protein interaction network alignment methods.

Main Methods:

  • Proposed a dual network alignment strategy combining protein-to-protein and region-to-region alignment.
  • Integrated Gene Ontology annotation and PPI network data to guide dual alignment.
  • Implemented the DualAligner method for practical application.

Main Results:

  • DualAligner achieved higher accuracy compared to state-of-the-art methods on global networks from IntAct.
  • Demonstrated the effectiveness of dual alignment by integrating protein-level and region-level comparisons.
  • Analyzed parameter effects on alignment quality and presented a utility case study.

Conclusions:

  • The dual network alignment approach offers a more robust and accurate method for analyzing PPI networks.
  • DualAligner provides a flexible framework for biological predictions at varying levels of specificity.
  • This method enhances understanding of biological networks despite inherent data noise.