The use of weighted graphs for large-scale genome analysis
Zhou, Fang; Toivonen, Hannu; King, Ross D.
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), 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|
|Publisher||Public Library of Science|
|Peer Reviewed||Peer Reviewed|
|Copyright Statement||Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0|
The Use of Weighted Graphs for Large-Scale Genome Analysis.pdf
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0