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Published on: August 14, 2018
CTree: comparison of clusters between phylogenetic trees made easy
John Archer1, David L Robertson
1Faculty of Life Science, University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PT, UK. john.archer@postgrad.manchester.ac.uk
Bioinformatics (Oxford, England)
|August 25, 2007
Summary
CTree software quantifies clusters in viral phylogenetic trees, simplifying the analysis of diversity within and between viral subtypes. It extracts statistical data for robust phylogenetic interpretation.
Area of Science:
- Bioinformatics
- Computational Biology
- Virology
Background:
- Phylogenetic tree analysis is crucial for understanding viral evolution and diversity.
- Quantifying clusters within these trees aids in subtype identification and tracking.
- Existing software may lack specialized tools for detailed cluster analysis.
Purpose of the Study:
- To introduce CTree, a novel software tool for the quantification of clusters in viral phylogenetic trees.
- To highlight CTree's features for analyzing inter- and intra-cluster diversity.
- To present the implementation of a heuristic algorithm for cluster identification within CTree.
Main Methods:
- CTree employs a heuristic algorithm for identifying clusters in phylogenetic trees.
- Clusters are stored as distinct data structures.
- Statistical data, including Subtype Diversity Ratio (SDR) and Subtype Diversity Variance (SDV), are extracted.
Main Results:
- CTree facilitates the extraction of statistical data from identified clusters.
- The software simplifies the quantification of tree topologies concerning diversity.
- Novel features for cluster identification and analysis are incorporated.
Conclusions:
- CTree provides a specialized solution for quantifying clusters in viral phylogenetic trees.
- The software enhances the analysis of viral diversity metrics like SDR and SDV.
- CTree integrates advanced clustering algorithms with general tree viewing functionalities.
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