An SVD-based comparison of nine whole eukaryotic genomes supports a coelomate rather than ecdysozoan lineage

BACKGROUND: Eukaryotic whole genome sequences are accumulating at an impressive rate. Effective methods for comparing multiple whole eukaryotic genomes on a large scale are needed. Most attempted solutions involve the production of large scale alignments, and many of these require a high stringency pre-screen for putative orthologs in order to reduce the effective size of the dataset and provide a reasonably high but unknown fraction of correctly aligned homologous sites for comparison. As an alternative, highly efficient methods that do not require the pre-alignment of operationally defined orthologs are also being explored. RESULTS: A non-alignment method based on the Singular Value Decomposition (SVD) was used to compare the predicted protein complement of nine whole eukaryotic genomes ranging from yeast to man. This analysis resulted in the simultaneous identification and definition of a large number of well conserved motifs and gene families, and produced a species tree supporting one of two conflicting hypotheses of metazoan relationships. CONCLUSIONS: Our SVD-based analysis of the entire protein complement of nine whole eukaryotic genomes suggests that highly conserved motifs and gene families can be identified and effectively compared in a single coherent definition space for the easy extraction of gene and species trees. While this occurs without the explicit definition of orthologs or homologous sites, the analysis can provide a basis for these definitions

The bowfin genome illuminates the developmental evolution of ray-finned fishes.

The bowfin (Amia calva) is a ray-finned fish that possesses a unique suite of ancestral and derived phenotypes, which are key to understanding vertebrate evolution. The phylogenetic position of bowfin as a representative of neopterygian fishes, its archetypical body plan and its unduplicated and slowly evolving genome make bowfin a central species for the genomic exploration of ray-finned fishes. Here we present a chromosome-level genome assembly for bowfin that enables gene-order analyses, settling long-debated neopterygian phylogenetic relationships. We examine chromatin accessibility and gene expression through bowfin development to investigate the evolution of immune, scale, respiratory and fin skeletal systems and identify hundreds of gene-regulatory loci conserved across vertebrates. These resources connect developmental evolution among bony fishes, further highlighting the bowfin's importance for illuminating vertebrate biology and diversity in the genomic era

Phylogenetic influence of complex, evolutionary models: a Bayesian approach

Molecular evolution recovers the history of living species by comparing genetic information, exploring genome structure and function from an evolutionary perspective. Here we infer substitution rates and ancestral reconstructions, to better understand mutation responses to some known biochemical phenomena. Mutation processes are commonly inferred using parsimony, maximum likelihood and Bayesian. Parsimony is not explicitly model-based, and is statistically biased due to unrealistic assumptions. The model-based maximum likelihood approaches become computationally inefficient while analyzing large or high-dimensional datasets, leaving little opportunities to incorporate complex evolutionary models. We implemented a posterior probability (Bayesian) approach that evaluates evolutionary models, applying it to primate mitochondrial genomes. The species nucleotide sequence data were augmented with ancestral states at the internal nodes of the phylogeny. We simplified probability calculations for substitution events along the branches by assuming that only up to one or two substitution events occurred per branch per site. These conditional pathway calculations introduce very little bias into the inferred reconstructions, while increasing the feasibility of incorporating complex evolutionary models with higher dimensions. Compositional bias tests, including functional predictions of ancestral tRNAs, show that ancestral sequences from the Bayesian approach are more biologically realistic than those reconstructed by maximum likelihood. To explore other model complexity, we allowed substitution rates to vary among sites by having a different model at each site. With a strand-symmetric model as the base model, asymmetric substitution probabilities for specific substitution types were varied among sites. This model would not be feasible with standard matrix exponentiation methods, particularly maximum likelihood. We observed for A--\u3eG and C--\u3eT substitutions almost linear, respectively, almost asymptotic responses (with some regional deviations). Note that the HMM models had no a priori response built in them. Observed responses fitted predictions from earlier gene by gene likelihood analyses. For A--\u3eG substitutions, deviations from the expected linear response correlated positively with the loop-forming propensity of the corresponding site in the mRNA secondary structure. In the COI region, C--\u3eT substitutions have a prominent dip, suggesting protection against mutations. The C--\u3eT substitution responses differed significantly between primate sub-groups defined based on their single genome A--\u3eG responses

Fast Hash-Based Algorithms for Analyzing Large Collections of Evolutionary Trees

Phylogenetic analysis can produce easily tens of thousands of equally plausible evolutionary trees. Consensus trees and topological distance matrices are often used to summarize the evolutionary relationships among the trees of interest. However, current approaches are not designed to analyze very large tree collections. In this dissertation, we present two fast algorithms— HashCS and HashRF —for analyzing large collections of evolutionary trees based on a novel hash table data structure, which provides a convenient and fast approach to store and access the bipartition information collected from the tree collections. Our HashCS algorithm is a fast () technique for constructing consensus trees, where is the number of taxa and is the number of trees. By reprocessing the bipartition information in our hash table, HashCS constructs strict and majority consensus trees. In addition to a consensus algorithm, we design a fast topological distance algorithm called HashRF to compute the × Robinson-Foulds distance matrix, which requires (^ 2) running time. A RF distance matrix provides plenty of data-mining opportunities to help researchers understand the evolutionary relationships contained in their collection of trees. We also introduce a series of extensions based on HashRF to provide researchers with more convenient set of tools for analyzing their trees. We provide extensive experimentation regarding the practical performance of our hash-based algorithms across a diverse collection of biological and artificial trees. Our results show that both algorithms easily outperform existing consensus and RF matrix implementations. For example, on our biological trees, HashCS and HashRF are 1.8 and 100 times faster than PAUP*, respectively. We show two real-world applications of our fast hashing algorithms: (i) comparing phylogenetic heuristic implementations, and (ii) clustering and visualizing trees. In our first application, we design novel methods to compare the PaupRat and Rec-I-DCM3, two popular phylogenetic heuristics that use the Maximum Parsimony criterion, and show that RF distances are more effective than parsimony scores at identifying heterogeneity within a collection of trees. In our second application, we empirically show how to determine the distinct clusters of trees within large tree collections. We use two different techniques to identify distinct tree groups. Both techniques show that partitioning the trees into distinct groups and summarizing each group separately is a better representation of the data. Additional benefits of our approach are better consensus trees as well as insightful information regarding the convergence behavior of phylogenetic heuristics. Our fast hash-based algorithms provide scientists with a very powerful tools for analyzing the relationships within their large phylogenetic tree collections in new and exciting ways. Our work has many opportunities for future work including detecting convergence and designing better heuristics. Furthermore, our hash tables have lots of potential future extensions. For example, we can also use our novel hashing structure to design algorithms for computing other distance metrics such as Nearest Neighbor Interchange (NNI), Subtree Pruning and Regrafting (SPR), and Tree Bisection and Reconnection (TBR) distances