Data from: A Bayesian method for analyzing lateral gene transfer

Lateral gene transfer (LGT)—which transfers DNA between two non-vertically
related individuals belonging to the same or different species—is
recognized as a major force in prokaryotic evolution, and evidence of its
impact on eukaryotic evolution is ever increasing. LGT has attracted much
public attention for its potential to transfer pathogenic elements and
antibiotic resistance in bacteria, and to transfer pesticide resistance
from genetically modified crops to other plants. In a wider perspective,
there is a growing body of studies highlighting the role of LGT in
enabling organisms to occupy new niches or adapt to environmental changes.
The challenge LGT poses to the standard tree-based conception of evolution
is also being debated. Studies of LGT have, however, been severely limited
by a lack of computational tools. The best currently available LGT
algorithms are parsimony-based phylogenetic methods, which require a
pre-computed gene tree and cannot choose between sometimes wildly
differing most-parsimonious solutions. Moreover, in many studies, simple
heuristics are applied that can only handle putative orthologs and
completely disregard gene duplications. Consequently, proposed LGT among
specific gene families, and the rate of LGT in general remain debated. We
present a Bayesian MCMC-based method that integrates gene duplication,
gene loss, LGT, and sequence evolution, and apply the method in a
genome-wide analysis of two groups of bacteria: Mollicutes and
Cyanobacteria. Our analyses show that although the LGT rate between
distant species is high, the net combined rate of duplication and close
species-LGT is on average higher. We also show that the common practice of
disregarding reconcilability in gene tree inference overestimates the
number of LGT and duplication events.