The use of weighted graphs for large-scale genome analysis
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 ana...
| Main Authors: | , , |
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| Format: | Article |
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Public Library of Science
2014
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| Online Access: | https://eprints.nottingham.ac.uk/47352/ |
| _version_ | 1848797523883327488 |
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| author | Zhou, Fang Toivonen, Hannu King, Ross D. |
| author_facet | Zhou, Fang Toivonen, Hannu King, Ross D. |
| author_sort | Zhou, Fang |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:05:14Z |
| format | Article |
| id | nottingham-47352 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:05:14Z |
| publishDate | 2014 |
| publisher | Public Library of Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-473522020-05-04T16:45:07Z https://eprints.nottingham.ac.uk/47352/ The use of weighted graphs for large-scale genome analysis Zhou, Fang Toivonen, Hannu King, Ross D. 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. Public Library of Science 2014-03-11 Article PeerReviewed Zhou, Fang, 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 https://doi.org/10.1371/journal.pone.0089618 doi:10.1371/journal.pone.0089618 doi:10.1371/journal.pone.0089618 |
| spellingShingle | Zhou, Fang Toivonen, Hannu King, Ross D. The use of weighted graphs for large-scale genome analysis |
| title | The use of weighted graphs for large-scale genome analysis |
| title_full | The use of weighted graphs for large-scale genome analysis |
| title_fullStr | The use of weighted graphs for large-scale genome analysis |
| title_full_unstemmed | The use of weighted graphs for large-scale genome analysis |
| title_short | The use of weighted graphs for large-scale genome analysis |
| title_sort | use of weighted graphs for large-scale genome analysis |
| url | https://eprints.nottingham.ac.uk/47352/ https://eprints.nottingham.ac.uk/47352/ https://eprints.nottingham.ac.uk/47352/ |