Abstract not found

The estimation of species trees has become popular as a considerable amount of multilocus molecular data is available for inferring the evolutionary history of species. However, the current phylogenetic paradigm, that reconstructs gene trees to represent the species tree suggests that commonly used methods such as the concatenation method, the consensus tree method, or the gene tree parsimony method may be either inconsistent or highly biased. In this paper, we propose a Bayesian hierarchical model to estimate the phylogeny of a group of species using multiple estimated gene tree distributions such as those that arise in a Bayesian analysis of DNA sequence data. Our model employs substitution models used in traditional phylogenetics, but also uses coalescent theory to explain genealogical signals from species trees to gene trees and from gene trees to sequence data, thereby forming a stochastic model to estimate gene trees, species trees, ancestral population sizes and species divergence times simultaneously. Our model is founded on the assumption that gene trees, even of unlinked loci, are correlated due to being derived from a single species tree and therefore should be estimated jointly. We apply the method to two multilocus DNA sequences datasets. The estimates of the

We consider two algorithmic questions related to the evolution of gene families. First, given a gene tree for a gene family, can the evolutionary history of this family be explained with only speciation and duplication events? Such gene trees are called DS-trees. We show that this question can be answered in linear time, and that a DS-tree induces a single species tree. We then study a natural extension of this problem: what is the minimum number of gene losses involved in an evolutionary history leading to an observed gene tree or set of gene trees? Based on our characterization of DS-trees, we propose a heuristic for this problem, and evaluate it on a dataset of plants gene families and on simulated data.

Reconciliation is the process of resolving disagreement between gene and species trees, by invoking gene duplications and losses to explain topological incongruence. The resulting inferred duplication histories are a valuable source of information for a broad range of biological applications, including ortholog identification, estimating gene duplication times, and rooting and correcting gene trees. Reconciliation for binary trees is a tractable and well studied problem. However, a striking proportion of species trees are non-binary. For example, 64 % of branch points in the NCBI taxonomy have three or more children. When applied to non-binary species trees, current algorithms overestimate the number of duplications because they cannot distinguish between duplication and deep coalescence. We present the first formal algorithm for reconciling binary gene trees with non-binary species trees under a duplication-loss parsimony model. Using a space efficient mapping from gene to species tree, our algorithm infers the minimum number of duplications and losses in O(|VG | ·(kS + hS)) time, where VG is the number of nodes in the gene tree, hS is the height of the species tree and kS is the width of its largest multifurcation. We also present a dynamic programming algorithm for a combined loss model, in which losses in sibling species may be represented as a single loss in the common ancestor. Our algorithms have been implemented in NOTUNG, a robust, production quality tree-fitting program, which provides a graphical user interface for exploratory analysis and also supports automated, high-throughput analysis of large data sets.

Abstract. We consider two questions related to the evolution of gene families. First, given a gene tree for a gene family, can the evolutionary history of this family be explained with only speciation and duplication events, and without gene loss. We show that this question can be answered in linear time, and that such a gene tree induces a single species tree consistent with a history with no loss. We then present a heuristic for the following problem: if a gene tree can not be explained without gene loss, what is the minimum number of losses involved in an evolutionary history of the gene family. We finally evaluate our algorithms on a dataset of plants gene families. 1

Abstract. Reconciliation is the commonly used method for inferring the evolutionary scenario for a gene family. It consists in “embedding ” an inferred gene tree into a known species tree, revealing the evolution of the gene family by duplications and losses. The main complaint about reconciliation is that the inferred evolutionary scenario is strongly dependant on the considered gene tree, as few misplaced leaves may lead to a completely different history, with significantly more duplications and losses. As using different phylogenetic methods with different parameters may lead to different gene trees, it is essential to have criteria to choose, among those, the appropriate one for reconciliation. In this paper, following the conclusion of a previous paper, we flag certain duplication vertices of a gene tree, the “non-apparent duplication ” (NAD) vertices, as resulting from the misplacement of leaves, and consider the optimization problem of removing the minimum number of leaves leading to a tree without any NAD vertex. We develop a polynomial-time algorithm that is exact for two special classes of gene trees, and show a good performance on simulated data sets in the general case. 1

Abstract. Computational inference of transcriptional regulatory networks remains a challenging problem, in part due to the lack of strong network models. In this paper we present evolutionary approaches to improve the inference of regulatory networks for a family of organisms by developing an evolutionary model for these networks and taking advantage of established phylogenetic relationships among these organisms. In previous work, we used a simple evolutionary model for regulatory networks and provided extensive simulation results showing that phylogenetic information, combined with such a model, could be used to gain significant improvements on the performance of current inference algorithms. In this paper, we extend the evolutionary model so as to take into account gene duplications and losses, which are viewed as major drivers in the evolution of regulatory networks. We show how to adapt our evolutionary approach to this new model and provide detailed simulation results, which show significant improvement on the reference network inference algorithms. We also provide results on biological data (cis-regulatory modules for 12 species of Drosophila), confirming our simulation results. 1

We consider two algorithmical questions related to the evolution of gene families. First, given a gene tree for a gene family, can the evolutionary history of this family be explained with only speciation and duplication events? Such gene trees are called DS-trees. We show that this question can be answered in linear time, and that a DS-tree induces a single species tree. We then study a natural extension of this problem: what is the minimum number of gene losses involved in an evolutionary history leading to an observed gene tree or set of gene trees? Based on our characterization of DS-trees, we propose a heuristic for this problem, and Background. Genes are the major building blocks of genomic sequences, containing the information necessary to produce all the proteins and non-coding RNAs of a cell. Genes, in a genome or across genomes, that are related by sequence or function similarity are called homologs and grouped into a gene family. The completed sequencing of a variety of genomes

Abstract. Reconciliation is a well-known method for studying the evolution of a gene family through speciation, duplication, and loss. Unfortunately, the inferred history strongly depends on the considered gene tree for the gene family, as a few misplaced leaves can lead to a completely different history, possibly with significantly more duplications and losses. It is therefore essential to develop methods that are able to preprocess and correct gene trees prior to reconciliation. In this paper, we consider a combinatorial problem, known as the Minimum Leaf Removal problem, that has been proposed to remove errors from a gene tree by deleting some of its leaves. We prove that the problem is APX-hard, even in the restricted case of a gene family with at most two copies per genome. On the positive side, we present fixed-parameter algorithms where the parameters are the size of the solution (minimum number of leaf removals) and the number of genomes containing multiple gene copies. 1

Motivation: The increasing availability of large-scale protein–protein interaction (PPI) data has fuelled the efforts to elucidate the building blocks and organization of cellular machinery. Previous studies have shown cross-species comparison to be an effective approach in uncovering functional modules in protein networks. This has in turn driven the research for new network alignment methods with a more solid grounding in network evolution models and better scalability, to allow multiple network comparison. Results: We develop a new framework for protein network alignment, based on reconstruction of an ancestral PPI network. The reconstruction algorithm is built upon a proposed model of protein network evolution, which takes into account phylogenetic history of the proteins and the evolution of their interactions. The application of our methodology to the PPI networks of yeast, worm and fly reveals that the most probable conserved ancestral interactions are often related to known protein complexes. By projecting the conserved ancestral interactions back onto the input networks we are able to identify the corresponding conserved protein modules in the considered species. In contrast to most of the previous methods, our algorithm is able to compare many networks simultaneously. The performed experiments demonstrate the ability of our method to uncover many functional modules with high specificity. Availability: Information for obtaining software and supplementary results are available at