TITLE

An improved probability mapping approach to assess genome mosaicism

AUTHOR(S)
Zhaxybayeva, Olga; Gogarten, J. Peter
PUB. DATE
January 2003
SOURCE
BMC Genomics;2003, Vol. 4, p37
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Background: Maximum likelihood and posterior probability mapping are useful visualization techniques that are used to ascertain the mosaic nature of prokaryotic genomes. However, posterior probabilities, especially when calculated for four-taxon cases, tend to overestimate the support for tree topologies. Furthermore, because of poor taxon sampling four-taxon analyses suffer from sensitivity to the long branch attraction artifact. Here we extend the probability mapping approach by improving taxon sampling of the analyzed datasets, and by using bootstrap support values, a more conservative tool to assess reliability. Results: Quartets of orthologous proteins were complemented with homologs from selected reference genomes. The mapping of bootstrap support values from these extended datasets gives results similar to the original maximum likelihood and posterior probability mapping. The more conservative nature of the plotted support values allows to focus further analyses on those protein families that strongly disagree with the majority or plurality of genes present in the analyzed genomes. Conclusion: Posterior probability is a non-conservative measure for support, and posterior probability mapping only provides a quick estimation of phylogenetic information content of four genomes. This approach can be utilized as a pre-screen to select genes that might have been horizontally transferred. Better taxon sampling combined with subtree analyses prevents the inconsistencies associated with four-taxon analyses, but retains the power of visual representation. Nevertheless, a case-by-case inspection of individual multi-taxon phylogenies remains necessary to differentiate unrecognized paralogy and shared phylogenetic reconstruction artifacts from horizontal gene transfer events.
ACCESSION #
28834596

 

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