Phylogeny Analysis from Gene-Order Data with Massive Duplications

Background: Gene order changes, under rearrangements, insertions, deletions and duplications, have been used as a new type of data source for phylogenetic reconstruction. Because these changes are rare compared to sequence mutations, they allow the inference of phylogeny further back in evolutionary time. There exist many computational methods for the reconstruction of gene-order phylogenies, including widely used maximum parsimonious methods and maximum likelihood methods. However, both methods face challenges in handling large genomes with many duplicated genes, especially in the presence of whole genome duplication. Methods: In this paper, we present three simple yet powerful methods based on maximum-likelihood (ML) approaches that encode multiplicities of both gene adjacency and gene content information for phylogenetic reconstruction. Results: Extensive experiments on simulated data sets show that our new method achieves the most accurate phylogenies compared to existing approaches. We also evaluate our method on real whole-genome data from eleven mammals. The package is publicly accessible at http://www.geneorder.org. Conclusions: Our new encoding schemes successfully incorporate the multiplicity information of gene adjacencies and gene content into an ML framework, and show promising results in reconstruct phylogenies for whole-genome data in the presence of massive duplications

Models and Algorithms for Whole-Genome Evolution and their Use in Phylogenetic Inference

The rapid accumulation of sequenced genomes offers the chance to resolve longstanding questions about the evolutionary histories, or phylogenies, of groups of organisms. The relatively rare occurrence of large-scale evolutionary events in a whole genome, events such as genome rearrangements, duplications and losses, enables us to extract a strong and robust phylogenetic signal from whole-genome data. The work presented in this dissertation focuses on models and algorithms for whole-genome evolution and their use in phylogenetic inference. We designed algorithms to estimate pairwise genomic distances from large-scale genomic changes. We refined the evolutionary models on whole-genome evolution. We also made use of these results to provide fast and accurate methods for phylogenetic inference, that scales up, in both speed and accuracy, to modern high-resolution whole-genome data. We designed algorithms to estimate the true evolutionary distance between two genomes under genome rearrangements, and also under rearrangements, plus gains and losses. We refined the evolutionary model to be the first mathematical model to preserve the structural dichotomy in genomic organization between most prokaryotes and most eukaryotes. Those models and associated distance estimators provide a basis for studying facets of possible mechanisms of evolution through simulation and application to real genomes. Phylogenetic analyses from whole-genome data have been limited to small collections of genomes and low-resolution data; they have also lacked an effective assessment of robustness. We developed an approach that combines our distance estimator, any standard distance-based reconstruction algorithm, and a novel bootstrapping method based on resampling genomic adjacencies. The resulting tool overcomes a serious and long-standing impediment to the use of whole-genome data in phylogenetic inference and provides results comparable in accuracy and robustness to distance-based methods for sequence data. Maximum-likelihood approaches have been successfully applied to phylogenetic inferences for aligned sequences, but such applications remain primitive for whole-genome data. We developed a maximum-likelihood approach to phylogenetic analysis from whole-genome data. In combination with our bootstrap scheme, this new approach yields the first reliable phylogenetic tool for the analysis of whole-genome data at the level of syntenic blocks