Literature DB >> 19271188

Testing phylogenetic methods to identify horizontal gene transfer.

Maria Poptsova1.   

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).

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Year:  2009        PMID: 19271188     DOI: 10.1007/978-1-60327-853-9_13

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  7 in total

1.  Horizontal gene transfer and gene conversion drive evolution of modular polyketide synthases.

Authors:  Jurica Zucko; Paul F Long; Daslav Hranueli; John Cullum
Journal:  J Ind Microbiol Biotechnol       Date:  2012-05-30       Impact factor: 3.346

2.  kdetrees: Non-parametric estimation of phylogenetic tree distributions.

Authors:  Grady Weyenberg; Peter M Huggins; Christopher L Schardl; Daniel K Howe; Ruriko Yoshida
Journal:  Bioinformatics       Date:  2014-04-24       Impact factor: 6.937

3.  Inferring horizontal gene transfer.

Authors:  Matt Ravenhall; Nives Škunca; Florent Lassalle; Christophe Dessimoz
Journal:  PLoS Comput Biol       Date:  2015-05-28       Impact factor: 4.475

4.  A phylogeny-based benchmarking test for orthology inference reveals the limitations of function-based validation.

Authors:  Kalliopi Trachana; Kristoffer Forslund; Tomas Larsson; Sean Powell; Tobias Doerks; Christian von Mering; Peer Bork
Journal:  PLoS One       Date:  2014-11-04       Impact factor: 3.240

5.  Genomic Data Quality Impacts Automated Detection of Lateral Gene Transfer in Fungi.

Authors:  Pierre-Yves Dupont; Murray P Cox
Journal:  G3 (Bethesda)       Date:  2017-04-03       Impact factor: 3.154

6.  The Genome of the Fungal Pathogen Verticillium dahliae Reveals Extensive Bacterial to Fungal Gene Transfer.

Authors:  Xiaoqian Shi-Kunne; Mathijs van Kooten; Jasper R L Depotter; Bart P H J Thomma; Michael F Seidl
Journal:  Genome Biol Evol       Date:  2019-03-01       Impact factor: 3.416

7.  AST: an automated sequence-sampling method for improving the taxonomic diversity of gene phylogenetic trees.

Authors:  Chan Zhou; Fenglou Mao; Yanbin Yin; Jinling Huang; Johann Peter Gogarten; Ying Xu
Journal:  PLoS One       Date:  2014-06-03       Impact factor: 3.240

  7 in total

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