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This article is part of the supplement: Selected articles from the Eleventh Asia Pacific Bioinformatics Conference (APBC 2013): Bioinformatics

Open Access Proceedings

Reconstruction of phyletic trees by global alignment of multiple metabolic networks

Cheng-Yu Ma1, Shu-Hsi Lin2, Chi-Ching Lee13, Chuan Yi Tang14, Bonnie Berger56 and Chung-Shou Liao7*

Author affiliations

1 Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan

2 Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan

3 Bioinformatics Center, Chang Gung University, Taoyuan, Taiwan

4 Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

5 Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

6 Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

7 Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan

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Citation and License

BMC Bioinformatics 2013, 14(Suppl 2):S12  doi:10.1186/1471-2105-14-S2-S12

Published: 21 January 2013

Abstract

Background

In the last decade, a considerable amount of research has been devoted to investigating the phylogenetic properties of organisms from a systems-level perspective. Most studies have focused on the classification of organisms based on structural comparison and local alignment of metabolic pathways. In contrast, global alignment of multiple metabolic networks complements sequence-based phylogenetic analyses and provides more comprehensive information.

Results

We explored the phylogenetic relationships between microorganisms through global alignment of multiple metabolic networks. The proposed approach integrates sequence homology data with topological information of metabolic networks. In general, compared to recent studies, the resulting trees reflect the living style of organisms as well as classical taxa. Moreover, for phylogenetically closely related organisms, the classification results are consistent with specific metabolic characteristics, such as the light-harvesting systems, fermentation types, and sources of electrons in photosynthesis.

Conclusions

We demonstrate the usefulness of global alignment of multiple metabolic networks to infer phylogenetic relationships between species. In addition, our exhaustive analysis of microbial metabolic pathways reveals differences in metabolic features between phylogenetically closely related organisms. With the ongoing increase in the number of genomic sequences and metabolic annotations, the proposed approach will help identify phenotypic variations that may not be apparent based solely on sequence-based classification.