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The use of weighted graphs for large-scale genome analysis

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

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Fang Zhou

Hannu Toivonen

Ross D. King


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.


Zhou, F., Toivonen, H., & King, R. D. (2014). The use of weighted graphs for large-scale genome analysis. PLoS ONE, 9(3), Article e89618.

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
Public URL
Publisher URL


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