OGtree: a tool for creating genome trees of prokaryotes based on overlapping genes

OGtree is a web-based tool for constructing genome trees of prokaryotic species based on a measure of combining overlapping-gene content and overlapping-gene order in their whole genomes. The overlapping genes (OGs) are defined as adjacent genes whose coding sequences overlap partially or entirely. In fact, OGs are ubiquitous in microbial genomes and more conserved between species than non-OGs. Based on these properties, it has been suggested that OGs can serve as better phylogenetic characters than non-OGs for reconstructing the evolutionary relationships among microbial genomes. OGtree takes the accession numbers of prokaryotic genomes as its input. It then downloads their complete genomes from the National Centre for Biotechnology Information and identifies OGs in each genome and their orthologous OGs in other genomes. Next, OGtree computes an overlapping-gene distance between each pair of input genomes based on a combination of their OG content and orthologous OG order. Finally, it utilizes distance-based methods of building tree to reconstruct the genome trees of input prokaryotic genomes according to their pairwise OG distance. OGtree is available online at http://bioalgorithm.life.nctu.edu.tw/OGtree/

ComPhy: Prokaryotic Composite Distance Phylogenies Inferred from Whole-Genome Gene Sets

doi:10.1186/1471-2105-10-S1-S5With the increasing availability of whole genome sequences, it is becoming more and more important to use complete genome sequences for inferring species phylogenies. We developed a new tool ComPhy, 'Composite Distance Phylogeny', based on a composite distance matrix calculated from the comparison of complete gene sets between genome pairs to produce a prokaryotic phylogeny. The composite distance between two genomes is defined by three components: Gene Dispersion Distance (GDD), Genome Breakpoint Distance (GBD) and Gene Content Distance (GCD). GDD quantifies the dispersion of orthologous genes along the genomic coordinates from one genome to another; GBD measures the shared breakpoints between two genomes; GCD measures the level of shared orthologs between two genomes. The phylogenetic tree is constructed from the composite distance matrix using a neighbor joining method. We tested our method on 9 datasets from 398 completely sequenced prokaryotic genomes. We have achieved above 90% agreement in quartet topologies between the tree created by our method and the tree from the Bergey's taxonomy. In comparison to several other phylogenetic analysis methods, our method showed consistently better performance. ComPhy is a fast and robust tool for genome-wide inference of evolutionary relationship among genomes."This work was supported in part by NSF/ITR-IIS-0407204.

QuartetS: a fast and accurate algorithm for large-scale orthology detection

The unparalleled growth in the availability of genomic data offers both a challenge to develop orthology detection methods that are simultaneously accurate and high throughput and an opportunity to improve orthology detection by leveraging evolutionary evidence in the accumulated sequenced genomes. Here, we report a novel orthology detection method, termed QuartetS, that exploits evolutionary evidence in a computationally efficient manner. Based on the well-established evolutionary concept that gene duplication events can be used to discriminate homologous genes, QuartetS uses an approximate phylogenetic analysis of quartet gene trees to infer the occurrence of duplication events and discriminate paralogous from orthologous genes. We used function- and phylogeny-based metrics to perform a large-scale, systematic comparison of the orthology predictions of QuartetS with those of four other methods [bi-directional best hit (BBH), outgroup, OMA and QuartetS-C (QuartetS followed by clustering)], involving 624 bacterial genomes and >2 million genes. We found that QuartetS slightly, but consistently, outperformed the highly specific OMA method and that, while consuming only 0.5% additional computational time, QuartetS predicted 50% more orthologs with a 50% lower false positive rate than the widely used BBH method. We conclude that, for large-scale phylogenetic and functional analysis, QuartetS and QuartetS-C should be preferred, respectively, in applications where high accuracy and high throughput are required

Developing and applying supertree methods in Phylogenomics and Macroevolution

Supertrees can be used to combine partially overalapping trees and generate more inclusive phylogenies. It has been proposed that Maximum Likelihood (ML) supertrees method (SM) could be developed using an exponential probability distribution to model errors in the input trees (given a proposed supertree). When the tree-­‐to-­‐tree distances used in the ML computation are symmetric differences, the ML SM has been shown to be equivalent to a Majority-­‐Rule consensus SM, and hence, exactly as the latter, it has the desirable property of being a median tree (with reference to the set of input trees). The ability to estimate the likelihood of supertrees, allows implementing Bayesian (MCMC) approaches, which have the advantage to allow the support for the clades in a supertree to be properly estimated. I present here the L.U.St software package; it contains the first implementation of a ML SM and allows for the first time statistical tests on supertrees. I also characterized the first implementation of the Bayesian (MCMC) SM. Both the ML and the Bayesian (MCMC) SMs have been tested for and found to be immune to biases. The Bayesian (MCMC) SM is applied to the reanalyses of a variety of datasets (i.e. the datasets for the Metazoa and the Carnivora), and I have also recovered the first Bayesian supertree-­‐based phylogeny of the Eubacteria and the Archaebacteria. These new SMs are discussed, with reference to other, well-­‐ known SMs like Matrix Representation with Parsimony. Both the ML and Bayesian SM offer multiple attractive advantages over current alternatives

Developing and applying supertree methods in Phylogenomics and Macroevolution

Supertrees can be used to combine partially overalapping trees and generate more inclusive phylogenies. It has been proposed that Maximum Likelihood (ML) supertrees method (SM) could be developed using an exponential probability distribution to model errors in the input trees (given a proposed supertree). When the tree-­‐to-­‐tree distances used in the ML computation are symmetric differences, the ML SM has been shown to be equivalent to a Majority-­‐Rule consensus SM, and hence, exactly as the latter, it has the desirable property of being a median tree (with reference to the set of input trees). The ability to estimate the likelihood of supertrees, allows implementing Bayesian (MCMC) approaches, which have the advantage to allow the support for the clades in a supertree to be properly estimated. I present here the L.U.St software package; it contains the first implementation of a ML SM and allows for the first time statistical tests on supertrees. I also characterized the first implementation of the Bayesian (MCMC) SM. Both the ML and the Bayesian (MCMC) SMs have been tested for and found to be immune to biases. The Bayesian (MCMC) SM is applied to the reanalyses of a variety of datasets (i.e. the datasets for the Metazoa and the Carnivora), and I have also recovered the first Bayesian supertree-­‐based phylogeny of the Eubacteria and the Archaebacteria. These new SMs are discussed, with reference to other, well-­‐ known SMs like Matrix Representation with Parsimony. Both the ML and Bayesian SM offer multiple attractive advantages over current alternatives

Exploring the boundaries of shallow phylogeny in the YESS group and the dynamics of gene cluster and operon formation in bacterial genomes

In this thesis I look at two different problems in bacterial genomic analysis. The first involves reconstructing the evolutionary history between a group of closely related bacteria. I addressed whether or not it is possible to separate such genomes into different genera, species and strains. Specifically, I addressed how different approaches such as the use of 16S rRNA phylogenetic trees, phylogenetic supertrees and concatenation of individual genes in order to construct phylogenetic trees compare with one another. What effect will problems associated with resolving shallow-phylogeny have on recovering a tree of life? Ultimately I show that for the group of genomes involved, different methods and data produce different results and that the true tree, if a tree-like structure does indeed exist for these genomes, is unrecoverable using such approaches. In the second part of my thesis I examine the phenomenon of gene clustering in bacterial genomes. I present a software program, GenClust, for the identification, analysis and visualisation of gene clusters. I show how GenClust can be used to recover and analyse clusters of genes involved in amino acid biosynthesis across a large !-proteobacterial dataset. Finally, I examine models of gene cluster and operon formation and test them with real data, using a combined approach of comparing clusters on both structural similarity and the underlying phylogenetic signals of the clustered genes. I provide a hypothesis for the selective forces driving cluster and operon formation in bacterial genomes

Phylophenetic properties of metabolic pathway topologies as revealed by global analysis

BACKGROUND: As phenotypic features derived from heritable characters, the topologies of metabolic pathways contain both phylogenetic and phenetic components. In the post-genomic era, it is possible to measure the "phylophenetic" contents of different pathways topologies from a global perspective. RESULTS: We reconstructed phylophenetic trees for all available metabolic pathways based on topological similarities, and compared them to the corresponding 16S rRNA-based trees. Similarity values for each pair of trees ranged from 0.044 to 0.297. Using the quartet method, single pathways trees were merged into a comprehensive tree containing information from a large part of the entire metabolic networks. This tree showed considerably higher similarity (0.386) to the corresponding 16S rRNA-based tree than any tree based on a single pathway, but was, on the other hand, sufficiently distinct to preserve unique phylogenetic information not reflected by the 16S rRNA tree. CONCLUSION: We observed that the topology of different metabolic pathways provided different phylogenetic and phenetic information, depicting the compromise between phylogenetic information and varying evolutionary pressures forming metabolic pathway topologies in different organisms. The phylogenetic information content of the comprehensive tree is substantially higher than that of any tree based on a single pathway, which also gave clues to constraints working on the topology of the global metabolic networks, information that is only partly reflected by the topologies of individual metabolic pathways