The power of phylogenetic approaches to detect horizontally transferred genes.
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.


CONCLUSIONS
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.
Poptsova
Maria S
MS
Department of Molecular and Cell Biology, University of Connecticut, CT, USA. maria.poptsova@uconn.edu <maria.poptsova@uconn.edu>
Gogarten
J Peter
JP
D002965:Classification / Q000379:methods*; D016000:Cluster Analysis; D019295:Computational Biology; D020563:Gammaproteobacteria / Q000235:genetics*; D022761:Gene Transfer, Horizontal / Q000235:genetics*; D016680:Genome, Bacterial; D016013:Likelihood Functions; D008957:Models, Genetic; D005810:Multigene Family / Q000235:genetics*; D010802:Phylogeny