Discovering large conserved functional components in global network alignment by graph matching

Background Aligning protein-protein interaction (PPI) networks is very important to discover the functionally conserved sub-structures between different species. In recent years, the global PPI network alignment problem has been extensively studied aiming at finding the one-to-one alignment with the...

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Bibliographic Details
Published in:BMC genomics Vol. 19; no. Suppl 7; pp. 670 - 58
Main Authors: Zhu, Yuanyuan, Li, Yuezhi, Liu, Juan, Qin, Lu, Yu, Jeffrey Xu
Format: Journal Article
Language:English
Published: London BioMed Central 24.09.2018
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Algorithms
Alignment
Analysis
Animal Genetics and Genomics
Animals
Bioinformatics
Biological effects
Biomedical and Life Sciences
Computational biology
Computational Biology - methods
Computer Graphics
Gene Ontology
Gene Regulatory Networks
Genomics
Graph matching
Graph theory
Graphs
Humans
Integer programming
Life Sciences
Linear programming
Methods
Microarrays
Microbial Genetics and Genomics
Models, Theoretical
Phylogenetics
Plant Genetics and Genomics
Protein interaction
Protein Interaction Mapping
Protein-protein interaction network
Protein-protein interactions
Proteins
Proteins - genetics
Proteins - metabolism
Proteomics
Seeds
Semantics
Species
Wildlife conservation
Protein-protein interaction network
Graph theory
Graph matching
ISSN:1471-2164, 1471-2164
Online Access:Get full text
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Summary:Background Aligning protein-protein interaction (PPI) networks is very important to discover the functionally conserved sub-structures between different species. In recent years, the global PPI network alignment problem has been extensively studied aiming at finding the one-to-one alignment with the maximum matching score. However, finding large conserved components remains challenging due to its NP-hardness. Results We propose a new graph matching method GMAlign for global PPI network alignment. It first selects some pairs of important proteins as seeds, followed by a gradual expansion to obtain an initial matching, and then it refines the current result to obtain an optimal alignment result iteratively based on the vertex cover. We compare GMAlign with the state-of-the-art methods on the PPI network pairs obtained from the largest BioGRID dataset and validate its performance. The results show that our algorithm can produce larger size of alignment, and can find bigger and denser common connected subgraphs as well for the first time. Meanwhile, GMAlign can achieve high quality biological results, as measured by functional consistency and semantic similarity of the Gene Ontology terms. Moreover, we also show that GMAlign can achieve better results which are structurally and biologically meaningful in the detection of large conserved biological pathways between species. Conclusions GMAlign is a novel global network alignment tool to discover large conserved functional components between PPI networks. It also has many potential biological applications such as conserved pathway and protein complex discovery across species. The GMAlign software and datasets are avaialbile at https://github.com/yzlwhu/GMAlign .
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ISSN:1471-2164
1471-2164
DOI:10.1186/s12864-018-5027-9