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

Sorting genomes with rearrangements and segmental duplications through trajectory graphs

We study the problem of sorting genomes under an evolutionary model that includes genomic rearrangements and segmental duplications. We propose an iterative algorithm to improve any initial evolutionary trajectory between two genomes in terms of parsimony. Our algorithm is based on a new graphical model, the trajectory graph, which models not only the final states of two genomes but also an existing evolutionary trajectory between them. We show that redundant rearrangements in the trajectory correspond to certain cycles in the trajectory graph, and prove that our algorithm converges to an optimal trajectory for any initial trajectory involving only rearrangements

A New Genomic Evolutionary Model for Rearrangements, Duplications, and Losses that Applies across Eukaryotes and Prokaryotes

Genomic rearrangements have been studied since the beginnings of modern genetics and models for such rearrangements have been the subject of many papers over the last 10 years. However, none of the extant models can predict the evolution of genomic organization into circular unichromosomal genomes (as in most prokaryotes) and linear multichromosomal genomes (as in most eukaryotes). Very few of these models support gene duplications and losses-yet these events may be more common in evolutionary history than rearrangements and themselves cause apparent rearrangements. We propose a new evolutionary model that integrates gene duplications and losses with genome rearrangements and that leads to genomes with either one (or a very few) circular chromosome or a collection of linear chromosomes. Our model is based on existing rearrangement models and inherits their linear-time algorithms for pairwise distance computation (for rearrangement only). Moreover, our model predictions fit observations about the evolution of gene family sizes and agree with the existing predictions about the growth in the number of chromosomes in eukaryotic genomes