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      Molecular data resolving the systematics of the related Blattellidae genera Symploce, Episymploce, and Blattella (Blattodea: Blaberoidea)

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      Arthropod Systematics & Phylogeny
      Pensoft Publishers

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          Abstract

          Members of the morphologically and ecologically diverse Blattellidae provide a dilemma: their systematic assignment, whether morphologically similar or different, is uncertain. We pay special attention to several taxa: the morphologically similar Episymploce Bey-Bienko, 1950 and Symploce Hebard, 1916, which were strongly disputed because of their extremely similar generic diagnosis in the past century, and one brachypterous species of Blattella Caudell, 1903, which can be easily distinguished from other macropterous members, but is at risk of being misassigned to other genera on the basis of morphological characters. We address the phylogeny of Blattellidae using DNA sequences (mitochondrial 12S rRNA, 16S rRNA, COII, nuclear 28S rRNA, histone H3) from a broad sample of taxa. A new genus (Centrocolumna gen. nov.) and four new species (Centrocolumna ericea sp. nov., Symploce nigra sp. nov., Symploce tubercularis sp. nov., Blattella foliolata sp. nov.) are established on the basis of morphological characters combined with the molecular data. The phylogenetic results indicate the relationships ((Episymploce + Blattella) + Centrocolumna gen. nov.) + Symploce. Furthermore, we delimited the taxonomic status of 12 new combinations, which involve Centrocolumna gen. nov., Episymploce, Symploce, and Blattella.

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          MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

          We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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            PartitionFinder 2: New Methods for Selecting Partitioned Models of Evolution for Molecular and Morphological Phylogenetic Analyses

            PartitionFinder 2 is a program for automatically selecting best-fit partitioning schemes and models of evolution for phylogenetic analyses. PartitionFinder 2 is substantially faster and more efficient than version 1, and incorporates many new methods and features. These include the ability to analyze morphological datasets, new methods to analyze genome-scale datasets, new output formats to facilitate interoperability with downstream software, and many new models of molecular evolution. PartitionFinder 2 is freely available under an open source license and works on Windows, OSX, and Linux operating systems. It can be downloaded from www.robertlanfear.com/partitionfinder. The source code is available at https://github.com/brettc/partitionfinder.
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              A rapid bootstrap algorithm for the RAxML Web servers.

              Despite recent advances achieved by application of high-performance computing methods and novel algorithmic techniques to maximum likelihood (ML)-based inference programs, the major computational bottleneck still consists in the computation of bootstrap support values. Conducting a probably insufficient number of 100 bootstrap (BS) analyses with current ML programs on large datasets-either with respect to the number of taxa or base pairs-can easily require a month of run time. Therefore, we have developed, implemented, and thoroughly tested rapid bootstrap heuristics in RAxML (Randomized Axelerated Maximum Likelihood) that are more than an order of magnitude faster than current algorithms. These new heuristics can contribute to resolving the computational bottleneck and improve current methodology in phylogenetic analyses. Computational experiments to assess the performance and relative accuracy of these heuristics were conducted on 22 diverse DNA and AA (amino acid), single gene as well as multigene, real-world alignments containing 125 up to 7764 sequences. The standard BS (SBS) and rapid BS (RBS) values drawn on the best-scoring ML tree are highly correlated and show almost identical average support values. The weighted RF (Robinson-Foulds) distance between SBS- and RBS-based consensus trees was smaller than 6% in all cases (average 4%). More importantly, RBS inferences are between 8 and 20 times faster (average 14.73) than SBS analyses with RAxML and between 18 and 495 times faster than BS analyses with competing programs, such as PHYML or GARLI. Moreover, this performance improvement increases with alignment size. Finally, we have set up two freely accessible Web servers for this significantly improved version of RAxML that provide access to the 200-CPU cluster of the Vital-IT unit at the Swiss Institute of Bioinformatics and the 128-CPU cluster of the CIPRES project at the San Diego Supercomputer Center. These Web servers offer the possibility to conduct large-scale phylogenetic inferences to a large part of the community that does not have access to, or the expertise to use, high-performance computing resources.
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                Author and article information

                Journal
                Arthropod Systematics & Phylogeny
                ASP
                Pensoft Publishers
                1864-8312
                1863-7221
                May 31 2022
                May 31 2022
                : 80
                : 187-208
                Article
                10.3897/asp.80.e62469
                20926e42-3a7d-4a50-a7f8-aef7e34c35aa
                © 2022

                http://creativecommons.org/licenses/by/4.0/

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