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ExaML version 3: a tool for phylogenomic analyses on supercomputers

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      Abstract

      Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Because of the next generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. We present ExaML version 3, a dedicated production-level code for inferring phylogenies on whole-transcriptome and whole-genome alignments using supercomputers.

      Results: We introduce several improvements and extensions to ExaML: Extensions of substitution models and supported data types, the integration of a novel load balance algorithm as well as a parallel I/O optimization that significantly improve parallel efficiency, and a production-level implementation for Intel MIC-based hardware platforms.

      Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/ExaML.

      Contact: Alexandros.Stamatakis@ 123456h-its.org

      Supplementary information: Supplementary data are available at Bioinformatics online.

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      Most cited references 8

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      RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

      Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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        IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

        Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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          Phylogenomics resolves the timing and pattern of insect evolution.

          Insects are the most speciose group of animals, but the phylogenetic relationships of many major lineages remain unresolved. We inferred the phylogeny of insects from 1478 protein-coding genes. Phylogenomic analyses of nucleotide and amino acid sequences, with site-specific nucleotide or domain-specific amino acid substitution models, produced statistically robust and congruent results resolving previously controversial phylogenetic relations hips. We dated the origin of insects to the Early Ordovician [~479 million years ago (Ma)], of insect flight to the Early Devonian (~406 Ma), of major extant lineages to the Mississippian (~345 Ma), and the major diversification of holometabolous insects to the Early Cretaceous. Our phylogenomic study provides a comprehensive reliable scaffold for future comparative analyses of evolutionary innovations among insects. Copyright © 2014, American Association for the Advancement of Science.
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            Author and article information

            Affiliations
            1Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany and
            2Department of informatics, Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany
            Author notes
            *To whom correspondence should be addressed.

            Associate Editor: Jonathan Wren

            Journal
            Bioinformatics
            Bioinformatics
            bioinformatics
            bioinfo
            Bioinformatics
            Oxford University Press
            1367-4803
            1367-4811
            01 August 2015
            29 March 2015
            29 March 2015
            : 31
            : 15
            : 2577-2579
            25819675
            4514929
            10.1093/bioinformatics/btv184
            btv184
            © The Author 2015. Published by Oxford University Press.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

            Counts
            Pages: 3
            Categories
            Applications Notes
            Phylogenetics

            Bioinformatics & Computational biology

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