Back to Search Start Over

Reconstruction of phyletic trees by global alignment of multiple metabolic networks

Authors :
Ma Cheng-Yu
Lin Shu-Hsi
Lee Chi-Ching
Tang Chuan Yi
Berger Bonnie
Liao Chung-Shou
Source :
BMC Bioinformatics, Vol 14, Iss Suppl 2, p S12 (2013)
Publication Year :
2013
Publisher :
BMC, 2013.

Abstract

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.

Details

Language :
English
ISSN :
14712105
Volume :
14
Issue :
Suppl 2
Database :
Directory of Open Access Journals
Journal :
BMC Bioinformatics
Publication Type :
Academic Journal
Accession number :
edsdoj.3708cc654d8d9300215ee2f9d0d6
Document Type :
article
Full Text :
https://doi.org/10.1186/1471-2105-14-S2-S12