Molecular Biology and Evolution 2010 27(3):703-713; doi:10.1093/molbev/msp240
Ofir Cohen and Tal Pupko*
Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
* Corresponding author: E-mail: talp@post.tau.ac.il.
Macrogenomic events, in which genes are gained and lost, play a pivotal evolutionary role in microbial evolution. Nevertheless, probabilistic-evolutionary models describing such events and methods for their robust inference are considerably less developed than existing methodologies for analyzing site-specific sequence evolution. Here, we present a novel method for the inference of gains and losses of gene families. First, we develop probabilistic-evolutionarymodels describing the dynamics of gene-family content, which are more biologically realistic than previously suggested models. In our likelihood-based models, gains and losses are represented by transitions between presence and absence, given an underlying phylogeny. We employ a mixture-model approach in which we allow both the gain rate and the loss rate to vary among gene families. Second, we use these models together with the analytic implementation of stochastic mapping to infer branch-specific events. Our novel methodology allows us to infer and quantify horizontal genetransfer (HGT) events. This enables us to rank various gene families and lineages according to their propensity to undergogains and losses. Applying our methodology to 4,873 gene families shows that: 1) the novel mixture models describe the observed variability in gene-family content among microbes significantly better than previous models; 2) The stochastic mapping approach enables accurate inference of gain and loss events based on simulations; 3) At least 34% of the gene families analyzed are inferred to have experienced HGT at least once during their evolution; and 4) Gene families that were inferred to experience HGT are both enriched and depleted with respect to specific functional categories.
Key Words: phyletic pattern • probabilistic-evolutionary models • mixture models • genome evolution • horizontal gene transfer • gene-family content
Associate editor: Andrew Roger
Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
* Corresponding author: E-mail: talp@post.tau.ac.il.
Macrogenomic events, in which genes are gained and lost, play a pivotal evolutionary role in microbial evolution. Nevertheless, probabilistic-evolutionary models describing such events and methods for their robust inference are considerably less developed than existing methodologies for analyzing site-specific sequence evolution. Here, we present a novel method for the inference of gains and losses of gene families. First, we develop probabilistic-evolutionarymodels describing the dynamics of gene-family content, which are more biologically realistic than previously suggested models. In our likelihood-based models, gains and losses are represented by transitions between presence and absence, given an underlying phylogeny. We employ a mixture-model approach in which we allow both the gain rate and the loss rate to vary among gene families. Second, we use these models together with the analytic implementation of stochastic mapping to infer branch-specific events. Our novel methodology allows us to infer and quantify horizontal genetransfer (HGT) events. This enables us to rank various gene families and lineages according to their propensity to undergogains and losses. Applying our methodology to 4,873 gene families shows that: 1) the novel mixture models describe the observed variability in gene-family content among microbes significantly better than previous models; 2) The stochastic mapping approach enables accurate inference of gain and loss events based on simulations; 3) At least 34% of the gene families analyzed are inferred to have experienced HGT at least once during their evolution; and 4) Gene families that were inferred to experience HGT are both enriched and depleted with respect to specific functional categories.
Key Words: phyletic pattern • probabilistic-evolutionary models • mixture models • genome evolution • horizontal gene transfer • gene-family content
Associate editor: Andrew Roger
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Supplementary Figure S1 - Manuscript file of format doc
