Quartet decomposition server: a platform for analyzing phylogenetic trees
1 Department of Biochemistry and Molecular Biology, University of Georgia, 120 Green St, Athens, GA, 30622, USA
2 Department of Molecular and Cell Biology, University of Connecticut, 91 North Eagleville Road, Storrs, CT, 06269, USA
3 Biotechnology-Bioservices Center, University of Connecticut, Storrs, CT, 06269-3149, USA
4 Department of Biology, West Virginia University, 53 Campus Drive, Morgantown, WV, 26506-6057, USA
5 College of Computer Science and Technology, Jilin University, Changchun, Jilin, China
6 Present Address: Department of Biological Sciences, Dartmouth College, 78 College Street, Hanover, NH, 03755, USA
Citation and License
BMC Bioinformatics 2012, 13:123 doi:10.1186/1471-2105-13-123Published: 7 June 2012
The frequent exchange of genetic material among prokaryotes means that extracting a majority or plurality phylogenetic signal from many gene families, and the identification of gene families that are in significant conflict with the plurality signal is a frequent task in comparative genomics, and especially in phylogenomic analyses. Decomposition of gene trees into embedded quartets (unrooted trees each with four taxa) is a convenient and statistically powerful technique to address this challenging problem. This approach was shown to be useful in several studies of completely sequenced microbial genomes.
We present here a web server that takes a collection of gene phylogenies, decomposes them into quartets, generates a Quartet Spectrum, and draws a split network. Users are also provided with various data download options for further analyses. Each gene phylogeny is to be represented by an assessment of phylogenetic information content, such as sets of trees reconstructed from bootstrap replicates or sampled from a posterior distribution. The Quartet Decomposition server is accessible at http://quartets.uga.edu webcite.
The Quartet Decomposition server presented here provides a convenient means to perform Quartet Decomposition analyses and will empower users to find statistically supported phylogenetic conflicts.