The use of weighted graphs for large-scale genome analysis

Zhou, Fang; Toivonen, Hannu; King, Ross D.

Abstract

There is an acute need for better tools to extract knowledge from the growing flood of sequence data. For example, thousands of complete genomes have been sequenced, and their metabolic networks inferred. Such data should enable a better understanding of evolution. However, most existing network analysis methods are based on pair-wise comparisons, and these do not scale to thousands of genomes. Here we propose the use of weighted graphs as a data structure to enable large-scale phylogenetic analysis of networks. We have developed three types of weighted graph for enzymes: taxonomic (these summarize phylogenetic importance), isoenzymatic (these summarize enzymatic variety/redundancy), and sequence-similarity (these summarize sequence conservation); and we applied these types of weighted graph to survey prokaryotic metabolism. To demonstrate the utility of this approach we have compared and contrasted the large-scale evolution of metabolism in Archaea and Eubacteria. Our results provide evidence for limits to the contingency of evolution.

Citation

Zhou, F., Toivonen, H., & King, R. D. (2014). The use of weighted graphs for large-scale genome analysis. PLoS ONE, 9(3), Article e89618. https://doi.org/10.1371/journal.pone.0089618

• Journal Article Type: Article
• Acceptance Date: Jan 23, 2014
• Publication Date: Mar 11, 2014
• Deposit Date: Oct 19, 2017
• Publicly Available Date: Oct 19, 2017
• Journal: PLoS ONE
• Electronic ISSN: 1932-6203
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 3
• Article Number: e89618
• DOI: https://doi.org/10.1371/journal.pone.0089618
• Public URL: https://nottingham-repository.worktribe.com/output/725380
• Publisher URL: https://doi.org/10.1371/journal.pone.0089618
• Contract Date: Oct 19, 2017