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      Four new species and three new records of helicosporous hyphomycetes from China and their multi-gene phylogenies

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          Abstract

          Helicosporous hyphomycetes have the potential to produce a variety of bioactive compounds. However, the strain resources of this fungal group are relatively scarce, which limits their further exploitation and utilization. In this study, based on phylogenetic analyses of combined ITS, LSU, RPB2, and TEF1α sequence data and the morphology from 11 isolates, we introduce four new species of helicosporous hyphomycetes, viz. Helicoma wuzhishanense, Helicosporium hainanense, H. viridisporum, and Neohelicomyces hainanensis, as well as three new records, viz. Helicoma guttulatum, H. longisporum, and Helicosporium sexuale. Detailed morphological comparisons of the four new species that distinguish them are provided.

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          Most cited references53

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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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              IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

              Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                25 November 2022
                2022
                : 13
                : 1053849
                Affiliations
                [1] 1Engineering and Research Center for Southwest Bio-Pharmaceutical Resources of National Education Ministry of China, Guizhou University , Guiyang, China
                [2] 2School of Pharmaceutical Engineering, Guizhou Institute of Technology , Guiyang, China
                [3] 3Center of Excellence in Fungal Research, Mae Fah Luang University , Chiang Rai, Thailand
                [4] 4Guizhou Key Laboratory of Agricultural Biotechnology, Guizhou Academy of Agricultural Sciences , Guiyang, China
                Author notes

                Edited by: Kezia Goldmann, Helmholtz Centre for Environmental Research (HZ), Germany

                Reviewed by: Mingkwan Doilom, Zhongkai University of Agriculture and Engineering, China; Noelia Betiana Nuñez Otaño, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina; Danushka Sandaruwan Tennakoon, Chiang Mai University, Thailand

                *Correspondence: Ji-Chuan Kang jckang@ 123456gzu.edu.cn

                This article was submitted to Microbe and Virus Interactions with Plants, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2022.1053849
                9732463
                36504835
                35853def-9d03-440a-a13d-b7fbd951b438
                Copyright © 2022 Lu, Ma, Xiao, Zhang, Xiao and Kang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 26 September 2022
                : 31 October 2022
                Page count
                Figures: 9, Tables: 1, Equations: 0, References: 56, Pages: 23, Words: 8677
                Funding
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Award ID: 31900020
                Award ID: 32170019
                Award ID: 31670027
                Funded by: Guizhou Science and Technology Department, doi 10.13039/501100004001;
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                freshwater fungi,taxonomy,tubeufiales,woody substrates,saprophytic fungi
                Microbiology & Virology
                freshwater fungi, taxonomy, tubeufiales, woody substrates, saprophytic fungi

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