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      Characterization of the Complete Mitochondrial Genome of Basidiomycete Yeast Hannaella oryzae: Intron Evolution, Gene Rearrangement, and Its Phylogeny


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          In this study, the mitogenome of Hannaella oryzae was sequenced by next-generation sequencing (NGS) and successfully assembled. The H. oryzae mitogenome comprised circular DNA molecules with a total size of 26,444 bp. We found that the mitogenome of H. oryzae partially deleted the tRNA gene transferring cysteine. Comparative mitogenomic analyses showed that intronic regions were the main factors contributing to the size variations of mitogenomes in Tremellales. Introns of the cox1 gene in Tremellales species were found to have undergone intron loss/gain events, and introns of the H. oryzae cox1 gene may have different origins. Gene arrangement analysis revealed that H. oryzae contained a unique gene order different from other Tremellales species. Phylogenetic analysis based on a combined mitochondrial gene set resulted in identical and well-supported topologies, wherein H. oryzae was closely related to Tremella fuciformis. This study represents the first report of mitogenome for the Hannaella genus, which will allow further study of the population genetics, taxonomy, and evolutionary biology of this important phylloplane yeast and other related species.

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          Most cited references 77

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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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              MITOS: improved de novo metazoan mitochondrial genome annotation.

              About 2000 completely sequenced mitochondrial genomes are available from the NCBI RefSeq data base together with manually curated annotations of their protein-coding genes, rRNAs, and tRNAs. This annotation information, which has accumulated over two decades, has been obtained with a diverse set of computational tools and annotation strategies. Despite all efforts of manual curation it is still plagued by misassignments of reading directions, erroneous gene names, and missing as well as false positive annotations in particular for the RNA genes. Taken together, this causes substantial problems for fully automatic pipelines that aim to use these data comprehensively for studies of animal phylogenetics and the molecular evolution of mitogenomes. The MITOS pipeline is designed to compute a consistent de novo annotation of the mitogenomic sequences. We show that the results of MITOS match RefSeq and MitoZoa in terms of annotation coverage and quality. At the same time we avoid biases, inconsistencies of nomenclature, and typos originating from manual curation strategies. The MITOS pipeline is accessible online at http://mitos.bioinf.uni-leipzig.de. Copyright © 2012 Elsevier Inc. All rights reserved.

                Author and article information

                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                28 May 2021
                : 12
                [1] 1Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, School of Food and Biological Engineering, Chengdu University , Chengdu, China
                [2] 2Biotechnology and Nuclear Technology Research Institute, Sichuan Academy of Agricultural Sciences , Chengdu, China
                [3] 3College of Life Sciences, Henan Agricultural University , Zhengzhou, China
                [4] 4Panxi Featured Crops Research and Utilization Key Laboratory of Sichuan Province, Xichang University , Xichang, China
                Author notes

                Edited by: Georg Hausner, University of Manitoba, Canada

                Reviewed by: Sajeet Haridas, Joint Genome Institute, United States; Yongjie Zhang, Shanxi University, China

                *Correspondence: Xu Wang, xuwang@ 123456henau.edu.cn

                Present address: Wenli Huang, Sichuan Academy of Agricultural Sciences, Chengdu, China

                This article was submitted to Evolutionary and Genomic Microbiology, a section of the journal Frontiers in Microbiology

                Copyright © 2021 Li, Li, Feng, Tu, Bao, Xiong, Wang, Qing and Huang.

                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.

                Page count
                Figures: 5, Tables: 0, Equations: 0, References: 77, Pages: 11, Words: 0
                Original Research


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