Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates by incorporating a model of rate heterogeneity across sites not previously considered in this context and by allowing concurrent searches of model space and tree space.

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ModelFinder: fast model selection for accurate phylogenetic estimates

This article presents W-IQ-TREE, an intuitive and user-friendly web interface and server for IQ-TREE, an efficient phylogenetic software for maximum likelihood analysis. W-IQ-TREE supports multiple sequence types (DNA, protein, codon, binary and morphology) in common alignment formats and a wide range of evolutionary models including mixture and partition models. W-IQ-TREE performs fast model selection, partition scheme finding, efficient tree reconstruction, ultrafast bootstrapping, branch tests, and tree topology tests. All computations are conducted on a dedicated computer cluster and the users receive the results via URL or email. W-IQ-TREE is available at http://iqtree.cibiv.univie.ac.at It is free and open to all users and there is no login requirement.

W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis.

Nonparametric bootstrap has been a widely used tool in phylogenetic analysis to assess the clade support of phylogenetic trees. However, with the rapidly growing amount of data, this task remains a computational bottleneck. Recently, approximation methods such as the RAxML rapid bootstrap (RBS) and the Shimodaira-Hasegawa-like approximate likelihood ratio test have been introduced to speed up the bootstrap. Here, we suggest an ultrafast bootstrap approximation approach (UFBoot) to compute the support of phylogenetic groups in maximum likelihood (ML) based trees. To achieve this, we combine the resampling estimated log-likelihood method with a simple but effective collection scheme of candidate trees. We also propose a stopping rule that assesses the convergence of branch support values to automatically determine when to stop collecting candidate trees. UFBoot achieves a median speed up of 3.1 (range: 0.66-33.3) to 10.2 (range: 1.32-41.4) compared with RAxML RBS for real DNA and amino acid alignments, respectively. Moreover, our extensive simulations show that UFBoot is robust against moderate model violations and the support values obtained appear to be relatively unbiased compared with the conservative standard bootstrap. This provides a more direct interpretation of the bootstrap support. We offer an efficient and easy-to-use software (available at http://www.cibiv.at/software/iqtree) to perform the UFBoot analysis with ML tree inference.

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Ultrafast Approximation for Phylogenetic Bootstrap

The degree of evolutionary conservation of an amino acid in a protein or a nucleic acid in DNA/RNA reflects a balance between its natural tendency to mutate and the overall need to retain the structural integrity and function of the macromolecule. The ConSurf web server (http://consurf.tau.ac.il), established over 15 years ago, analyses the evolutionary pattern of the amino/nucleic acids of the macromolecule to reveal regions that are important for structure and/or function. Starting from a query sequence or structure, the server automatically collects homologues, infers their multiple sequence alignment and reconstructs a phylogenetic tree that reflects their evolutionary relations. These data are then used, within a probabilistic framework, to estimate the evolutionary rates of each sequence position. Here we introduce several new features into ConSurf, including automatic selection of the best evolutionary model used to infer the rates, the ability to homology-model query proteins, prediction of the secondary structure of query RNA molecules from sequence, the ability to view the biological assembly of a query (in addition to the single chain), mapping of the conservation grades onto 2D RNA models and an advanced view of the phylogenetic tree that enables interactively rerunning ConSurf with the taxa of a sub-tree.

/pdf/consurf-2016-an-improved-methodology-to-estimate-and-4e3p549tv3.pdf

ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules.

Because many animal species are undescribed, and because the identification of known species is often difficult, interim taxonomic nomenclature has often been used in biodiversity analysis. By assigning individuals to presumptive species, called operational taxonomic units (OTUs), these systems speed investigations into the patterning of biodiversity and enable studies that would otherwise be impossible. Although OTUs have conventionally been separated through their morphological divergence, DNA-based delineations are not only feasible, but have important advantages. OTU designation can be automated, data can be readily archived, and results can be easily compared among investigations. This study exploits these attributes to develop a persistent, species-level taxonomic registry for the animal kingdom based on the analysis of patterns of nucleotide variation in the barcode region of the cytochrome c oxidase I (COI) gene. It begins by examining the correspondence between groups of specimens identified to a species through prior taxonomic work and those inferred from the analysis of COI sequence variation using one new (RESL) and four established (ABGD, CROP, GMYC, jMOTU) algorithms. It subsequently describes the implementation, and structural attributes of the Barcode Index Number (BIN) system. Aside from a pragmatic role in biodiversity assessments, BINs will aid revisionary taxonomy by flagging possible cases of synonymy, and by collating geographical information, descriptive metadata, and images for specimens that are likely to belong to the same species, even if it is undescribed. More than 274,000 BIN web pages are now available, creating a biodiversity resource that is positioned for rapid growth.

/pdf/a-dna-based-registry-for-all-animal-species-the-barcode-190e3osdti.pdf

A DNA-Based Registry for All Animal Species: The Barcode Index Number (BIN) System

jModelTest 2: more models, new heuristics and parallel computing Diego Darriba, Guillermo L. Taboada, Ramón Doallo and David Posada Supplementary Table 1. New features in jModelTest 2 Supplementary Table 2. Model selection accuracy Supplementary Table 3. Mean square errors for model averaged estimates Supplementary Note 1. Hill-climbing hierarchical clustering algorithm Supplementary Note 2. Heuristic filtering Supplementary Note 3. Simulations from prior distributions Supplementary Note 4. Speed-up benchmark on real and simulated datasets

/pdf/jmodeltest-2-more-models-new-heuristics-and-parallel-psfom8xd43.pdf

jModelTest 2: more models, new heuristics and parallel computing.

Summary: We have implemented a high-performance computing (HPC) version of ProtTest that can be executed in parallel in multicore desktops and clusters. This version, called ProtTest 3, includes new features and extended capabilities.

Availability: ProtTest 3 source code and binaries are freely available under GNU license for download from http://darwin.uvigo.es/software/prottest3, linked to a Mercurial repository at Bitbucket (https://bitbucket.org/).

Contact: dposada@uvigo.es

Supplementary information:Supplementary data are available at Bioinformatics online.

ProtTest 3

Summary: We have implemented a High Performance Computing (HPC) version of ProtTest (Abascal et al., 2007) that can be executed in parallel in multi-core desktops and clusters. This version, called ProtTest 3, includes new features and extended capabilities. Availability: ProtTest 3 source code and binaries are freely available under GNU license for download fromhttp://darwin.uvigo.es/ software/prottest3, linked to a Mercurial repository at Bitbucket

ProtTest 3: fast selection of best-t models of protein evolution

The use of probabilistic models of amino acid replacement is essential for the study of protein evolution, and programs like ProtTest implement different strategies to identify the best-fit model for the data at hand. For large protein alignments, this task can demand vast computational resources, preventing the justification of the model used in the analysis.

We have implemented a High Performance Computing (HPC) version of ProtTest. ProtTest-HPC can be executed in parallel in HPC environments as: (1) a GUI-based desktop version that uses multi-core processors and (2) a cluster-based version that distributes the computational load among nodes. The use of ProtTest-HPC resulted in significant performance gains, with speedups of up to 50 on a high performance cluster.

/pdf/prottest-hpc-fast-selection-of-best-fit-models-of-protein-2e8q5rg2b1.pdf

ProtTest-HPC: fast selection of best-fit models of protein evolution

http://gac.des.udc.es/~gltaboada/papers/taboada_scp11preprint.pdf

Guillermo L. Taboada

Papers

jModelTest 2: more models, new heuristics and parallel computing.

ProtTest 3

ProtTest 3: fast selection of best-t models of protein evolution

ProtTest-HPC: fast selection of best-fit models of protein evolution

Java in the High Performance Computing arena: Research, practice and experience