Tiffani L. Williams

Impacts of the Cretaceous Terrestrial Revolution and KPg extinction on mammal diversification.

Molecular phylogenetic analysis, calibrated with fossils, resolves the time frame of the mammalian radiation. Previous analyses of relations, divergence times, and diversification patterns among extant mammalian families have relied on supertree methods and local molecular clocks. We constructed a molecular supermatrix for mammalian families and analyzed these data with likelihood-based methods and relaxed molecular clocks. Phylogenetic analyses resulted in a robust phylogeny with better resolution than phylogenies from supertree methods. Relaxed clock analyses support the long-fuse model of diversification and highlight the importance of including multiple fossil calibrations that are spread across the tree. Molecular time trees and diversification analyses suggest important roles for the Cretaceous Terrestrial Revolution and Cretaceous-Paleogene (KPg) mass extinction in opening up ecospace that promoted interordinal and intraordinal diversification, respectively. By contrast, diversification analyses provide no support for the hypothesis concerning the delayed rise of present-day mammals during the Eocene Period.

Synthesis of phylogeny and taxonomy into a comprehensive tree of life

Significance Scientists have used gene sequences and morphological data to construct tens of thousands of evolutionary trees that describe the evolutionary history of animals, plants, and microbes. This study is the first, to our knowledge, to apply an efficient and automated process for assembling published trees into a complete tree of life. This tree and the underlying data are available to browse and download from the Internet, facilitating subsequent analyses that require evolutionary trees. The tree can be easily updated with newly published data. Our analysis of coverage not only reveals gaps in sampling and naming biodiversity but also further demonstrates that most published phylogenies are not available in digital formats that can be summarized into a tree of life. Reconstructing the phylogenetic relationships that unite all lineages (the tree of life) is a grand challenge. The paucity of homologous character data across disparately related lineages currently renders direct phylogenetic inference untenable. To reconstruct a comprehensive tree of life, we therefore synthesized published phylogenies, together with taxonomic classifications for taxa never incorporated into a phylogeny. We present a draft tree containing 2.3 million tips—the Open Tree of Life. Realization of this tree required the assembly of two additional community resources: (i) a comprehensive global reference taxonomy and (ii) a database of published phylogenetic trees mapped to this taxonomy. Our open source framework facilitates community comment and contribution, enabling the tree to be continuously updated when new phylogenetic and taxonomic data become digitally available. Although data coverage and phylogenetic conflict across the Open Tree of Life illuminate gaps in both the underlying data available for phylogenetic reconstruction and the publication of trees as digital objects, the tree provides a compelling starting point for community contribution. This comprehensive tree will fuel fundamental research on the nature of biological diversity, ultimately providing up-to-date phylogenies for downstream applications in comparative biology, ecology, conservation biology, climate change, agriculture, and genomics.

MrsRF: an efficient MapReduce algorithm for analyzing large collections of evolutionary trees

BackgroundMapReduce is a parallel framework that has been used effectively to design large-scale parallel applications for large computing clusters. In this paper, we evaluate the viability of the MapReduce framework for designing phylogenetic applications. The problem of interest is generating the all-to-all Robinson-Foulds distance matrix, which has many applications for visualizing and clustering large collections of evolutionary trees. We introduce MrsRF (MapReduce Speeds up RF), a multi-core algorithm to generate a t × t Robinson-Foulds distance matrix between t trees using the MapReduce paradigm.ResultsWe studied the performance of our MrsRF algorithm on two large biological trees sets consisting of 20,000 trees of 150 taxa each and 33,306 trees of 567 taxa each. Our experiments show that MrsRF is a scalable approach reaching a speedup of over 18 on 32 total cores. Our results also show that achieving top speedup on a multi-core cluster requires different cluster configurations. Finally, we show how to use an RF matrix to summarize collections of phylogenetic trees visually.ConclusionOur results show that MapReduce is a promising paradigm for developing multi-core phylogenetic applications. The results also demonstrate that different multi-core configurations must be tested in order to obtain optimum performance. We conclude that RF matrices play a critical role in developing techniques to summarize large collections of trees.

An investigation of phylogenetic likelihood methods

We analyze the performance of likelihood-based approaches used to reconstruct phylogenetic trees. Unlike other techniques such as Neighbor-joining (NJ) and Maximum Parsimony (MP), relatively little is known regarding the behavior of algorithms founded on the principle of likelihood. We study the accuracy, speed, and likelihood scores of four representative likelihood-based methods (fastDNAml, Mr Bayes, PAUP*-ML, and TREE-PUZZLE) that use either Maximum Likelihood (ML) or Bayesian inference to find the optimal tree. NJ is also studied to provide a baseline comparison. Our simulation study is based on random birth-death trees, which are deviated from ultrametricity, and uses the Kimura 2-parameter +Gamma model of sequence evolution. We find that Mr Bayes (a Bayesian inference approach) consistently outperforms the other methods in terms of accuracy and running time.

The Heterogeneous Bulk Synchronous Parallel Model

Trends in parallel computing indicate that heterogeneous parallel computing will be one of the most widespread platforms for computation-intensive applications. A heterogeneous computing environment offers considerably more computational power at a lower cost than a parallel computer. We propose the Heterogeneous Bulk Synchronous Parallel (HBSP) model, which is based on the BSP model of parallel computation, as a framework for developing applications for heterogeneous parallel environments. HBSP enhances the applicability of the BSP model by incorporating parameters that reflect the relative speeds of the heterogeneous computing components. Moreover, we demonstrate the utility of the model by developing parallel algorithms for heterogeneous systems.

A RANDOMIZED ALGORITHM FOR COMPARING SETS OF PHYLOGENETIC TREES

Phylogenetic analysis often produce a large number of candidate evolutionary trees, each a hypothesis of the ”true” tree. Post-processing techniques such as stri ct consensus trees are widely used to summarize the evolutionary relationships into a single tree. H owever, valuable information is lost during the summarization process. A more elementary step is to produce estimates of the topological differences that exist among all pairs of trees. We design a new randomized algorithm, called Hash-RF, that computes the all-to-all Robinson-Foulds (RF) distance—the most common distance metric for comparing two phylogenetic trees. Our approach uses a hash table to organize the bipartitions of a tree, and a universal hashing function makes our algorithm randomized. We compare the performance of our Hash-RF algorithm to PAUP*’s implementation of computing the all-to-all RF distance matrix. Our experiments focus on the algorithmic performance of comparing sets of biological trees, where the size of each tree ranged from 500 to 2,000 taxa and the collection of trees varied from 200 to 1,000 trees. Our experimental results clearly show that our Hash-RF algorithm is up to 500 times faster than PAUP*’s approach. Thus, Hash-RF provides an efficient alter native to a single tree summary of a collection of trees and potentially gives researchers the abil ity to explore their data in new and interesting ways.

Paper Mâché: Creating Dynamic Reproducible Science

Abstract For centuries, the research paper have been the main vehicle for scientific progress. From the paper, readers in the scientific community are expected to extract all the relevant information necessary to reproduce and validate the results presented by the papers authors. However, the increased use of computer software in science makes reproducing scientific results increasingly difficult. The research paper in its current state is no longer sufficient to fully reproduce, validate, or review a papers experimental results and conclusions. This impedes scientific progress. To remedy these concerns, we introduce PaperMâche, a new system for creating dynamic, executable research papers. The key novelty of PaperMâche is its use of virtual machines, which lets readers and reviewers easily view and interact with a paper, and reproduce key experimental results. For authors, the Paper Mâche workbench provides an easy-touse interface to build an executable paper. By transforming the static research paper into a dynamic and interactive entity, Paper Mâche brings the presentation of scientific results into the 21st century. We believe that Paper Mâche will become indispensable to the scientific process, and increase the visibility of key findings among members and non-members of the scientific community.

An Experimental Analysis of Robinson-Foulds Distance Matrix Algorithms

In this paper, we study two fast algorithms--HashRF and PGM-Hashed--for computing the Robinson-Foulds (RF) distance matrix between a collection of evolutionary trees. The RF distance matrix represents a tremendous data-mining opportunity for helping biologists understand the evolutionary relationships depicted among their trees. The novelty of our work results from using a variety of different architecture- and implementation-independent measures (i.e., percentage of bipartition sharing, number of bipartition comparisons, and memory usage) in addition to CPU time to explore practical algorithmic performance. Overall, our study concludes that HashRF performs better across the various performance measures than its competitor, PGM-Hashed. Thus, the HashRF algorithm provides scientists with a fast approach for understanding the evolutionary relationships among a set of trees.

An Experimental Analysis of Consensus Tree Algorithms for Large-Scale Tree Collections

Consensus trees are a popular approach for summarizing the shared evolutionary relationships in a collection of trees. Many popular techniques such as Bayesian analyses produce results that can contain tens of thousands of trees to summarize. We develop a fast consensus algorithm called HashCS to construct large-scale consensus trees. We perform an extensive empirical study for comparing the performance of several consensus tree algorithms implemented in widely-used, phylogenetic software such as PAUP* and MrBayes. Our collections of biological and artificial trees range from 128 to 16,384 trees on 128 to 1,024 taxa. Experimental results show that our HashCS approach is up to 100 times faster than MrBayes and up to 9 times faster than PAUP*. Fast consensus algorithms such as HashCS can be used in a variety of ways, such as in real-time to detect whether a phylogenetic search has converged.

Using tree diversity to compare phylogenetic heuristics

BackgroundEvolutionary trees are family trees that represent the relationships between a group of organisms. Phylogenetic heuristics are used to search stochastically for the best-scoring trees in tree space. Given that better tree scores are believed to be better approximations of the true phylogeny, traditional evaluation techniques have used tree scores to determine the heuristics that find the best scores in the fastest time. We develop new techniques to evaluate phylogenetic heuristics based on both tree scores and topologies to compare Pauprat and Rec-I-DCM3, two popular Maximum Parsimony search algorithms.ResultsOur results show that although Pauprat and Rec-I-DCM3 find the trees with the same best scores, topologically these trees are quite different. Furthermore, the Rec-I-DCM3 trees cluster distinctly from the Pauprat trees. In addition to our heatmap visualizations of using parsimony scores and the Robinson-Foulds distance to compare best-scoring trees found by the two heuristics, we also develop entropy-based methods to show the diversity of the trees found. Overall, Pauprat identifies more diverse trees than Rec-I-DCM3.ConclusionOverall, our work shows that there is value to comparing heuristics beyond the parsimony scores that they find. Pauprat is a slower heuristic than Rec-I-DCM3. However, our work shows that there is tremendous value in using Pauprat to reconstruct trees—especially since it finds identical scoring but topologically distinct trees. Hence, instead of discounting Pauprat, effort should go in improving its implementation. Ultimately, improved performance measures lead to better phylogenetic heuristics and will result in better approximations of the true evolutionary history of the organisms of interest.