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      Performance comparison of two reduced-representation based genome-wide marker-discovery strategies in a multi-taxon phylogeographic framework

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          Abstract

          Multi-locus genetic data are pivotal in phylogenetics. Today, high-throughput sequencing (HTS) allows scientists to generate an unprecedented amount of such data from any organism. However, HTS is resource intense and may not be accessible to wide parts of the scientific community. In phylogeography, the use of HTS has concentrated on a few taxonomic groups, and the amount of data used to resolve a phylogeographic pattern often seems arbitrary. We explore the performance of two genetic marker sampling strategies and the effect of marker quantity in a comparative phylogeographic framework focusing on six species (arthropods and plants). The same analyses were applied to data inferred from amplified fragment length polymorphism fingerprinting (AFLP), a cheap, non-HTS based technique that is able to straightforwardly produce several hundred markers, and from restriction site associated DNA sequencing (RADseq), a more expensive, HTS-based technique that produces thousands of single nucleotide polymorphisms. We show that in four of six study species, AFLP leads to results comparable with those of RADseq. While we do not aim to contest the advantages of HTS techniques, we also show that AFLP is a robust technique to delimit evolutionary entities in both plants and animals. The demonstrated similarity of results from the two techniques also strengthens biological conclusions that were based on AFLP data in the past, an important finding given the wide utilization of AFLP over the last decades. We emphasize that whenever the delimitation of evolutionary entities is the central goal, as it is in many fields of biodiversity research, AFLP is still an adequate technique.

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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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            Detecting the number of clusters of individuals using the software structure: a simulation study

            The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.
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              Non-parametric multivariate analyses of changes in community structure

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                Author and article information

                Contributors
                philipp.kirschner@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                17 February 2021
                17 February 2021
                2021
                : 11
                : 3978
                Affiliations
                [1 ]GRID grid.5771.4, ISNI 0000 0001 2151 8122, Department of Ecology, , University of Innsbruck, ; Technikerstraße 25, 6020 Innsbruck, Austria
                [2 ]GRID grid.5771.4, ISNI 0000 0001 2151 8122, Department of Botany, , University of Innsbruck, ; Sternwartestraße 15, 6020 Innsbruck, Austria
                [3 ]GRID grid.7039.d, ISNI 0000000110156330, Department of Biosciences, , University of Salzburg, ; Hellbrunnerstrasse 34, 5020 Salzburg, Austria
                [4 ]Institute for Alpine Environment, Eurac Research, Drususallee 1/Viale Druso 1, 39100 Bozen/Bolzano, Italy
                [5 ]GRID grid.10420.37, ISNI 0000 0001 2286 1424, Department of Botany and Biodiversity Research, , University of Vienna, ; Rennweg 14, 1030 Vienna, Austria
                [6 ]GRID grid.507618.d, ISNI 0000 0004 1793 7940, Real Jardín Botánico CSIC, ; Plaza de Murillo 2, 28014 Madrid, Spain
                [7 ]GRID grid.7010.6, ISNI 0000 0001 1017 3210, Department of Life and Environmental Sciences, , Marche Polytechnic University, ; Via Brecce Bianche, 60131 Ancona, Italy
                Article
                79778
                10.1038/s41598-020-79778-x
                7889850
                33597550
                e4346fd2-6ae2-491a-9bdc-bd23ae8ae3a7
                © The Author(s) 2021

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 28 January 2020
                : 9 December 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100009968, Tiroler Wissenschaftsförderung;
                Award ID: UNI-0404/2066
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002428, Austrian Science Fund;
                Award ID: P25955
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                evolutionary ecology,molecular ecology,conservation biology,phylogenetics,genotyping and haplotyping,high-throughput screening

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