Enhancing the accuracy of HMM-based conserved pathway prediction using global correspondence scores

BMC Bioinformatics

Proceedings

Xiaoning Qian¹  Sayed Mohammad Ebrahim Sahraeian²  Byung-Jun Yoon² 

[1] Department of Computer Science and Engineering, University of South Florida, 33620, Tampa, FL, USA;Department of Electrical and Computer Engineering, Texas A&M University, 77843, College Station, TX, USA;

关键词: Protein Pair;    Alignment Result;    Node Similarity;    Synthetic Network;    Network Alignment;   

DOI  :  10.1186/1471-2105-12-S10-S6

来源: Springer

【 摘 要 】

BackgroundComparative network analysis aims to identify common subnetworks in biological networks. It can facilitate the prediction of conserved functional modules across different species and provide deep insights into their underlying regulatory mechanisms. Recently, it has been shown that hidden Markov models (HMMs) can provide a flexible and computationally efficient framework for modeling and comparing biological networks.ResultsIn this work, we show that using global correspondence scores between molecules can improve the accuracy of the HMM-based network alignment results. The global correspondence scores are computed by performing a semi-Markov random walk on the networks to be compared. The resulting score naturally integrates the sequence similarity between molecules and the topological similarity between their molecular interactions, thereby providing a more effective measure for estimating the functional similarity between molecules. By incorporating the global correspondence scores, instead of relying on sequence similarity or functional annotation scores used by previous approaches, our HMM-based network alignment method can identify conserved subnetworks that are functionally more coherent.ConclusionsPerformance analysis based on synthetic and microbial networks demonstrates that the proposed network alignment strategy significantly improves the robustness and specificity of the predicted alignment results, in terms of conserved functional similarity measured based on KEGG ortholog (KO) groups. These results clearly show that the HMM-based network alignment framework using global correspondence scores can effectively find conserved biological pathways and has the potential to be used for automatic functional annotation of biomolecules.

【 授权许可】

CC BY   
© Qian et al; licensee BioMed Central Ltd. 2011