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      Evidence for divergent patterns of local selection driving venom variation in Mojave Rattlesnakes ( Crotalus scutulatus)

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          Abstract

          Snake venoms represent an enriched system for investigating the evolutionary processes that lead to complex and dynamic trophic adaptations. It has long been hypothesized that natural selection may drive geographic variation in venom composition, yet previous studies have lacked the population genetic context to examine these patterns. We leverage range-wide sampling of Mojave Rattlesnakes ( Crotalus scutulatus) and use a combination of venom, morphological, phylogenetic, population genetic, and environmental data to characterize the striking dichotomy of neurotoxic (Type A) and hemorrhagic (Type B) venoms throughout the range of this species. We find that three of the four previously identified major lineages within C. scutulatus possess a combination of Type A, Type B, and a ‘mixed’ Type A + B venom phenotypes, and that fixation of the two main venom phenotypes occurs on a more fine geographic scale than previously appreciated. We also find that Type A + B individuals occur in regions of inferred introgression, and that this mixed phenotype is comparatively rare. Our results support strong directional local selection leading to fixation of alternative venom phenotypes on a fine geographic scale, and are inconsistent with balancing selection to maintain both phenotypes within a single population. Our comparisons to biotic and abiotic factors further indicate that venom phenotype correlates with fang morphology and climatic variables. We hypothesize that links to fang morphology may be indicative of co-evolution of venom and other trophic adaptations, and that climatic variables may be linked to prey distributions and/or physiology, which in turn impose selection pressures on snake venoms.

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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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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              New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

              PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.
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                Author and article information

                Contributors
                viper@clemson.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                4 December 2018
                4 December 2018
                2018
                : 8
                : 17622
                Affiliations
                [1 ]ISNI 0000 0001 2159 2859, GRID grid.170430.1, Department of Biology, , University of Central Florida, ; 4110 Libra Drive, Orlando, FL 32816 USA
                [2 ]ISNI 0000 0001 2097 3086, GRID grid.266877.a, School of Biological Sciences, , University of Northern Colorado, ; 501 20th Street, Greeley, CO 80639 USA
                [3 ]ISNI 0000 0001 0665 0280, GRID grid.26090.3d, Department of Biological Sciences, , Clemson University, ; 190 Collings St., Clemson, SC 29634 USA
                [4 ]ISNI 0000 0001 2181 9515, GRID grid.267315.4, Department of Biology, , University of Texas at Arlington, ; 501 S. Nedderman Drive, Arlington, TX 76010 USA
                [5 ]ISNI 0000 0000 8724 8383, GRID grid.412198.7, Facultad de Ciencias Biológicas, , Universidad Juárez del Estado de Durango, ; Av. Universidad s/n. Fracc. Filadelfia, C.P. 35070 Gómez Palacio, Dgo. Mexico
                [6 ]ISNI 0000 0001 2181 7878, GRID grid.47840.3f, Museum of Vertebrate Zoology, , University of California, ; 3101 Valley Life Sciences Building, Berkeley, CA 94720 USA
                [7 ]ISNI 0000000122986657, GRID grid.34477.33, Department of Biology and Burke Museum of Natural History and Culture, , University of Washington, ; Box 351800, Seattle, WA 98195 USA
                [8 ]ISNI 0000 0001 2159 0001, GRID grid.9486.3, Museo de Zoología, Department of Evolutionary Biology, Faculta de Ciencias, , Universidad Nacional Autónoma de México, ; External Circuit of Ciudad Universitaria, México City, Mexico
                [9 ]Instituto Politécnico Nacional, Escuela Nacional de Ciencias Biológicas, Laboratorio de Cordados Terrestres, Colección Herpetológica, Del. Miguel Hidalgo, México City, Mexico
                [10 ]ISNI 0000 0004 0472 0419, GRID grid.255986.5, Department of Biological Science, , Florida State University, ; Tallahassee, Florida 32306 USA
                [11 ]ISNI 0000 0001 0665 0280, GRID grid.26090.3d, Present Address: Department of Biological Sciences, , Clemson Univeristy, ; 190 Collings St., Clemson, SC 29634 USA
                [12 ]ISNI 0000 0001 0665 0280, GRID grid.26090.3d, Present Address: Department of Biological Sciences & Department of Forestry and Environmental Conservation, , Clemson University, ; 190 Collings St., Clemson, SC 29634 USA
                Author information
                http://orcid.org/0000-0002-1927-7259
                http://orcid.org/0000-0003-0297-1313
                http://orcid.org/0000-0003-4098-7279
                http://orcid.org/0000-0002-1896-0937
                http://orcid.org/0000-0003-4515-2545
                http://orcid.org/0000-0002-5912-1574
                http://orcid.org/0000-0002-0356-2178
                http://orcid.org/0000-0002-2020-6992
                Article
                35810
                10.1038/s41598-018-35810-9
                6279745
                30514908
                5b6cb45d-25c5-494a-b355-eb0a10dc560d
                © 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/.

                History
                : 18 September 2018
                : 9 November 2018
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