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      Comparative Mitochondrial Genome Analyses of Sesarmid and Other Brachyuran Crabs Reveal Gene Rearrangements and Phylogeny

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          Abstract

          Mitochondrial genomes (mitogenomes) are important for understanding molecular evolution and phylogenetic relationships. The complete mitogenome of Perisesarma bidens was determined, which is 15,641 bp in length. The A + T content of P. bidens mitogenome was 74.81%. The AT skew was slightly negative (−0.021). The 22 tRNAs ranged from 65 to 73 bp and were highly A + T biased. All tRNA genes had typical cloverleaf structures, except for the trnS1 gene, which lacked a dihydrouridine (DHU) arm. The gene order within the P. bidens mitogenome was identical to the pancrustacean ground pattern, except for the translocation of the trnH. Additionally, the gene order of trnI-trnQ-trnM in pancrustacean ground pattern became trnQ-trnI-trnM in P. bidens. Phylogenetic analyses supported the inclusion of P. bidens in Sesarmidae and the promotion of Sesarminae to Sesarmidae. The results will help us to better understand the status and evolutionary history of Grapsoidea crabs.

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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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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              MEGA6: Molecular Evolutionary Genetics Analysis version 6.0.

              We announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software, which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements the RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under evolutionary criteria and implements a more advanced memory management that can double the size of sequence data sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from www.megasoftware.net free of charge.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                02 November 2020
                2020
                : 11
                : 536640
                Affiliations
                [1] 1Jiangsu Key Laboratory for Bioresources of Saline Soils, Jiangsu Provincial Key Laboratory of Coastal Wetland Bioresources and Environmental Protection, Jiangsu Synthetic Innovation Center for Coastal Bio-agriculture, School of Wetland, Yancheng Teachers University , Yancheng, China
                [2] 2School of Pharmaceutical Sciences, Wenzhou Medical University , Wenzhou, China
                [3] 3Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources, College of Aquaculture and Life Science, Shanghai Ocean University , Shanghai, China
                [4] 4College of Life Sciences, Nankai University , Tianjin, China
                [5] 5College of Biotechnology and Pharmaceutical Engineering, Nanjing University of Technology , Nanjing, China
                Author notes

                Edited by: Denis Baurain, University of Liège, Belgium

                Reviewed by: Beng Kah Song, Monash University Malaysia, Malaysia; Benny K. K. Chan, Academia Sinica, Taiwan

                *Correspondence: Bo-Ping Tang, boptang@ 123456163.com

                These authors have contributed equally to this work

                This article was submitted to Evolutionary and Population Genetics, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2020.536640
                7667018
                cffd53bb-c775-4a2a-b41e-203aba3f8072
                Copyright © 2020 Li, Xin, Tang, Yang, Tang, Sun, Zhang, Zhou, Liu and Yu.

                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
                : 20 February 2020
                : 28 August 2020
                Page count
                Figures: 6, Tables: 2, Equations: 0, References: 44, Pages: 11, Words: 0
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Funded by: Jiangsu Agricultural Science and Technology Independent Innovation Fund 10.13039/501100012431
                Funded by: China Postdoctoral Science Foundation 10.13039/501100002858
                Categories
                Genetics
                Original Research

                Genetics
                mitochondrial genomes,phylogeny,gene order,crustacean,perisesarma bidens
                Genetics
                mitochondrial genomes, phylogeny, gene order, crustacean, perisesarma bidens

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