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...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0089618&type=printable |
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| author | Fang Zhou Hannu Toivonen Ross D King |
| author_facet | Fang Zhou Hannu Toivonen Ross D King |
| author_sort | Fang Zhou |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-7caa040e14ea4c2cb824fc212ce26ede |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-7caa040e14ea4c2cb824fc212ce26ede2025-08-20T03:11:25ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0193e8961810.1371/journal.pone.0089618The use of weighted graphs for large-scale genome analysis.Fang ZhouHannu ToivonenRoss D KingThere 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0089618&type=printable |
| spellingShingle | Fang Zhou Hannu Toivonen Ross D King The use of weighted graphs for large-scale genome analysis. PLoS ONE |
| 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://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0089618&type=printable |
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