The use of weighted graphs for large-scale genome analysis

Zhou, Fang and Toivonen, Hannu and King, Ross D. (2014) The use of weighted graphs for large-scale genome analysis. PLoS ONE, 9 (3). e89618/1-e89618/12. ISSN 1932-6203

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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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/725380
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
Identification Number: https://doi.org/10.1371/journal.pone.0089618
Depositing User: LIN, Zhiren
Date Deposited: 19 Oct 2017 07:52
Last Modified: 04 May 2020 16:45
URI: http://eprints.nottingham.ac.uk/id/eprint/47352

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