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MONACO: accurate biological network alignment through optimal neighborhood matching between focal nodes

Journal Article · · Bioinformatics
 [1];  [2]
  1. Texas A & M Univ., College Station, TX (United States)
  2. Texas A & M Univ., College Station, TX (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
Motivation: Alignment of protein–protein interaction networks can be used for the unsupervised prediction of functional modules, such as protein complexes and signaling pathways, that are conserved across different species. To date, various algorithms have been proposed for biological network alignment, many of which attempt to incorporate topological similarity between the networks into the alignment process with the goal of constructing accurate and biologically meaningful alignments. Especially, random walk models have been shown to be effective for quantifying the global topological relatedness between nodes that belong to different networks by diffusing node-level similarity along the interaction edges. However, these schemes are not ideal for capturing the local topological similarity between nodes. Results: Here, we propose MONACO, a novel and versatile network alignment algorithm that finds highly accurate pairwise and multiple network alignments through the iterative optimal matching of ‘local’ neighborhoods around focal nodes. Extensive performance assessment based on real networks as well as synthetic networks, for which the ground truth is known, demonstrates that MONACO clearly and consistently outperforms all other state-of-the-art network alignment algorithms that we have tested, in terms of accuracy, coherence and topological quality of the aligned network regions. Furthermore, despite the sharply enhanced alignment accuracy, MONACO remains computationally efficient and it scales well with increasing size and number of networks.
Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
National Science Foundation (NSF); USDOE
Grant/Contract Number:
SC0012704
OSTI ID:
1822355
Report Number(s):
BNL--222133-2021-JAAM
Journal Information:
Bioinformatics, Journal Name: Bioinformatics Journal Issue: 10 Vol. 37; ISSN 1367-4803
Publisher:
International Society for Computational Biology - Oxford University PressCopyright Statement
Country of Publication:
United States
Language:
English

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