Who is Who in Phylogenetic Networks

Publications of Maria S. Poptsova     Order by:   Type | Year

Associated keywords

abstract-network evaluation from-quartets from-rooted-trees lateral-gene-transfer phylogenetic-network phylogeny Program-EEEP Program-Quartet-Decomposition reconstruction software split-network

2012

1 Fenglou Mao, David Williams, Olga Zhaxybayeva, Maria S. Poptsova, Pascal Lapierre, J. Peter Gogarten and Ying Xu. Quartet decomposition server: a platform for analyzing phylogenetic trees. In BMCB, Vol. 13:123, 2012. Keywords: abstract network, from quartets, phylogenetic network, phylogeny, Program Quartet Decomposition, reconstruction, software, split network.         Toggle abstract "Background: 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.Results: 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.Conclusions: 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. © 2012 Mao et al.; licensee BioMed Central Ltd."

2009

2 Maria S. Poptsova. Testing Phylogenetic Methods to Identify Horizontal Gene Transfer. In Horizontal Gene Transfer, Pages 227-240, 2009. Note: http://dx.doi.org/10.1007/978-1-60327-853-9_13.         Toggle abstract "The subject of this chapter is to describe the methodology for assessing the power of phylogenetic HGT detection methods. Detection power is defined in the framework of hypothesis testing. Rates of false positives and false negatives can be estimated by testing HGT detection methods on HGT-free orthologous sets, and on the same sets with in silico simulated HGT events. The whole process can be divided into three steps: obtaining HGT-free orthologous sets, in silico simulation of HGT events in the same set, and submitting both sets for evaluation by any of the tested methods.Phylogenetic methods of HGT detection can be roughly divided into three types: likelihood-based tests of topologies (Kishino-Hasegawa (KH), Shimodaira-Hasegawa (SH), and Approximately Unbiased (AU) tests), tree distance methods (symmetrical difference of Robinson and Foulds (RF), and Subtree Pruning and Regrafting (SPR) distances), and genome spectral approaches (bipartition and quartet decomposition analysis). Restrictions that are inherent to phylogenetic methods of HGT detection in general and the power and precision of each method are discussed and comparative analyses of different approaches are provided, as well as some examples of assessing the power of phylogenetic HGT detection methods from a case study of orthologous sets from gamma-proteobacteria (Poptsova and Gogarten, BMC Evol Biol 7, 45, 2007) and cyanobacteria (Zhaxybayeva et al., Genome Res 16, 1099-108, 2006)."

2007

3 Maria S. Poptsova and J. Peter Gogarten. The power of phylogenetic approaches to detect horizontally transferred genes. In BMCEB, Vol. 7(45), 2007. Keywords: evaluation, from rooted trees, lateral gene transfer, Program EEEP. Note: http://dx.doi.org/10.1186/1471-2148-7-45.         Toggle abstract "Background. Horizontal gene transfer plays an important role in evolution because it sometimes allows recipient lineages to adapt to new ecological niches. High genes transfer frequencies were inferred for prokaryotic and early eukaryotic evolution. Does horizontal gene transfer also impact phylogenetic reconstruction of the evolutionary history of genomes and organisms? The answer to this question depends at least in part on the actual gene transfer frequencies and on the ability to weed out transferred genes from further analyses. Are the detected transfers mainly false positives, or are they the tip of an iceberg of many transfer events most of which go undetected by current methods? Results. Phylogenetic detection methods appear to be the method of choice to infer gene transfers, especially for ancient transfers and those followed by orthologous replacement. Here we explore how well some of these methods perform using in silico transfers between the terminal branches of a gamma proteobacterial, genome based phylogeny. For the experiments performed here on average the AU test at a 5% significance level detects 90.3% of the transfers and 91% of the exchanges as significant. Using the Robinson-Foulds distance only 57.7% of the exchanges and 60% of the donations were identified as significant. Analyses using bipartition spectra appeared most successful in our test case. The power of detection was on average 97% using a 70% cut-off and 94.2% with 90% cut-off for identifying conflicting bipartitions, while the rate of false positives was below 4.2% and 2.1% for the two cut-offs, respectively. For all methods the detection rates improved when more intervening branches separated donor and recipient. Conclusion. Rates of detected transfers should not be mistaken for the actual transfer rates; most analyses of gene transfers remain anecdotal. The method and significance level to identify potential gene transfer events represent a trade-off between the frequency of erroneous identification (false positives) and the power to detect actual transfer events. © 2007 Poptsova and Gogarten; licensee BioMed Central Ltd."