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      Evolutionary history of Coleoptera revealed by extensive sampling of genes and species

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          Abstract

          Beetles (Coleoptera) are the most diverse and species-rich group of insects, and a robust, time-calibrated phylogeny is fundamental to understanding macroevolutionary processes that underlie their diversity. Here we infer the phylogeny and divergence times of all major lineages of Coleoptera by analyzing 95 protein-coding genes in 373 beetle species, including ~67% of the currently recognized families. The subordinal relationships are strongly supported as Polyphaga (Adephaga (Archostemata, Myxophaga)). The series and superfamilies of Polyphaga are mostly monophyletic. The species-poor Nosodendridae is robustly recovered in a novel position sister to Staphyliniformia, Bostrichiformia, and Cucujiformia. Our divergence time analyses suggest that the crown group of extant beetles occurred ~297 million years ago (Mya) and that ~64% of families originated in the Cretaceous. Most of the herbivorous families experienced a significant increase in diversification rate during the Cretaceous, thus suggesting that the rise of angiosperms in the Cretaceous may have been an ‘evolutionary impetus’ driving the hyperdiversity of herbivorous beetles.

          Abstract

          The phylogeny of beetles, which represent ~25% of known extant animal species, has been a challenge to resolve. Here, Zhang et al. infer a time-calibrated phylogeny for Coleoptera based on 95 protein-coding genes in 373 species and suggest an association between the hyperdiversification of beetles and the rise of angiosperms.

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          Most cited references 71

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          MEGA6: Molecular Evolutionary Genetics Analysis version 6.0.

          We announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software, which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements the RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under evolutionary criteria and implements a more advanced memory management that can double the size of sequence data sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from www.megasoftware.net free of charge.
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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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              Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data

              Massively-parallel cDNA sequencing has opened the way to deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here, we present the Trinity methodology for de novo full-length transcriptome reconstruction, and evaluate it on samples from fission yeast, mouse, and whitefly – an insect whose genome has not yet been sequenced. Trinity fully reconstructs a large fraction of the transcripts present in the data, also reporting alternative splice isoforms and transcripts from recently duplicated genes. In all cases, Trinity performs better than other available de novo transcriptome assembly programs, and its sensitivity is comparable to methods relying on genome alignments. Our approach provides a unified and general solution for transcriptome reconstruction in any sample, especially in the complete absence of a reference genome.
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                Author and article information

                Affiliations
                [1 ]ISNI 0000 0001 2360 039X, GRID grid.12981.33, State Key Laboratory of Biocontrol, College of Ecology and Evolution, School of Life Sciences, , Sun Yat-Sen University, ; Guangzhou, 510006 China
                [2 ]GRID grid.1016.6, Australian National Insect Collection, CSIRO, ; GPO Box 1700, Canberra, ACT 2601 Australia
                Contributors
                Adam.Slipinski@csiro.au
                alarzhang@gmail.com
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                15 January 2018
                15 January 2018
                2018
                : 9
                29335414 5768713 2644 10.1038/s41467-017-02644-4
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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