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The tree of genomes: An empirical comparison of genome-phylogeny reconstruction methods

Overview of attention for article published in BMC Ecology and Evolution, November 2008
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Title
The tree of genomes: An empirical comparison of genome-phylogeny reconstruction methods
Published in
BMC Ecology and Evolution, November 2008
DOI 10.1186/1471-2148-8-312
Pubmed ID
Authors

Angela McCann, James A Cotton, James O McInerney

Abstract

In the past decade or more, the emphasis for reconstructing species phylogenies has moved from the analysis of a single gene to the analysis of multiple genes and even completed genomes. The simplest method of scaling up is to use familiar analysis methods on a larger scale and this is the most popular approach. However, duplications and losses of genes along with horizontal gene transfer (HGT) can lead to a situation where there is only an indirect relationship between gene and genome phylogenies. In this study we examine five widely-used approaches and their variants to see if indeed they are more-or-less saying the same thing. In particular, we focus on Conditioned Reconstruction as it is a method that is designed to work well even if HGT is present. We confirm a previous suggestion that this method has a systematic bias. We show that no two methods produce the same results and most current methods of inferring genome phylogenies produce results that are significantly different to other methods. We conclude that genome phylogenies need to be interpreted differently, depending on the method used to construct them.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
Brazil 3 4%
Germany 2 3%
Netherlands 2 3%
Spain 2 3%
United Kingdom 2 3%
Australia 1 1%
Chile 1 1%
Switzerland 1 1%
Other 2 3%
Unknown 59 76%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 38%
Student > Ph. D. Student 18 23%
Student > Master 6 8%
Student > Postgraduate 5 6%
Professor 5 6%
Other 8 10%
Unknown 6 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 56 72%
Earth and Planetary Sciences 5 6%
Biochemistry, Genetics and Molecular Biology 4 5%
Computer Science 3 4%
Immunology and Microbiology 2 3%
Other 1 1%
Unknown 7 9%