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      Molecular Taxonomy and Diversification of Atlantic Skates (Chondrichthyes, Rajiformes): Adding More Pieces to the Puzzle of Their Evolutionary History

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          Abstract

          Conservation and long-term management plans of marine species need to be based upon the universally recognized key-feature of species identity. This important assignment is particularly challenging in skates (Rajiformes) in which the phenotypic similarity between some taxa and the individual variability in others, hampers accurate species identification. Here, 432 individual skate samples collected from four major ocean areas of the Atlantic were barcoded and taxonomically analysed. A BOLD project ELASMO ATL was implemented with the aim of establishing a new fully available and well curated barcode library containing both biological and molecular information. The evolutionary histories of the 38 skate taxa were estimated with two concatenated mitochondrial markers (COI and NADH2) through Maximum Likelihood and Bayesian inference. New evolutionary lineages within the genus Raja were discovered off Angola, where paleogeographic history coupled with oceanographic discontinuities could have contributed to the establishment of isolated refugia, playing a fundamental role among skates’ speciation events. These data successfully resolved many taxonomic ambiguities, identified cryptic diversity within valid species and demonstrated a highly cohesive monophyletic clustering among the order, laying the background for further inference of evolutionary patterns suitable for addressing management and conservation issues.

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          MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

          The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and 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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              MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

              Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
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                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Life (Basel)
                Life (Basel)
                life
                Life
                MDPI
                2075-1729
                22 June 2021
                July 2021
                : 11
                : 7
                : 596
                Affiliations
                [1 ]Department of Biological, Geological and Environmental Sciences, University of Bologna, 240126 Bologna, Italy; alice.ferrari6@ 123456unibo.it (A.F.); alessia.cariani@ 123456unibo.it (A.C.)
                [2 ]Department of Integrative Biology, University of Guelph, Guelph, ON N1G 2W1, Canada; rhanner@ 123456uoguelph.ca
                [3 ]Department of Agriculture, Forestry and Fisheries (DAFF), Branch Fisheries Management, Cape Town 8018, South Africa; roblesliesa@ 123456hotmail.com
                [4 ]Department of Ichthyology and Fisheries Science (DIFS), Rhodes University, Grahamstown 6139, South Africa
                [5 ]Department of Integrative Biology, Centre for Biodiversity Genomics, University of Guelph, Guelph, ON N1G 2W1, Canada; dsteinke@ 123456uoguelph.ca
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-4210-9755
                https://orcid.org/0000-0002-3499-8396
                https://orcid.org/0000-0003-0703-1646
                https://orcid.org/0000-0002-8649-5387
                https://orcid.org/0000-0002-5513-0086
                Article
                life-11-00596
                10.3390/life11070596
                8303890
                34206388
                4182fb6e-9ff0-41ea-ab7a-d9ab81f50cf0
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 24 May 2021
                : 20 June 2021
                Categories
                Article

                dna barcoding,phylogenetic inference,skates,atlantic ocean,coi,nadh2

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