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      Leucheria cantillanensis (Nassauvieae, Asteraceae), a new species endemic to Central Chile

      Author(s): , ,
      Publication date Created:
      Publication date (Electronic):
      Journal: PhytoKeys
      Publisher: Pensoft Publishers

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          Abstract

          A new species, Leucheria cantillanensis sp. nov., endemic to the coastal mountain range of Central Chile, is described. By using both nDNA and cpDNA, phylogenetic relationships of the new species were investigated. This new species belongs to the acaulescent/subacaulescent clade of Leucheria, which is congruent with the morphology of the species. A detailed description, distribution map, insights about its habitat, conservation status, and illustrations are provided. An updated key for acaulescent/subacaulescent species of Leucheria from Central Chile is also given.

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          RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

          Alexandros Stamatakis (2014)
          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

            Fredrik Ronquist, Maxim Teslenko, Paul van der Mark … (2012)
            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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              Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7

              Andrew Rambaut, Alexei J. Drummond, Dong Xie … (2018)
              Abstract Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the posterior distribution is a key step in any non-trivial Bayesian inference. We present the software package Tracer (version 1.7) for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more. Tracer is open-source and available at http://beast.community/tracer.
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                Author and article information

                Contributors
                Nicolás Lavandero: (View ORCID Profile)
                Journal
                Title: PhytoKeys
                Abbreviated Title: PK
                Publisher: Pensoft Publishers
                ISSN (Electronic): 1314-2003
                ISSN (Print): 1314-2011
                Publication date Created: December 08 2020
                Publication date (Electronic): December 08 2020
                Volume: 169
                Pages: 99-117
                Article
                DOI: 10.3897/phytokeys.169.57532
                SO-VID: 3a2bc380-cdde-4cd6-8dd0-9af837297bbd
                Copyright © © 2020
                License:

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

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