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      Complete mitochondrial genome sequence of Lepus yarkandensis Günther, 1875 (Lagomorpha, Leporidae): characterization and phylogenetic analysis

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          Lepus yarkandensis is a national second-class protected animal endemic to China and distributed only in the hot and arid Tarim Basin in Xinjiang. We sequenced and described the complete mitogenome of L. yarkandensis to analyze its characteristics and phylogeny. The species’ DNA is a 17,047 bp circular molecule that includes 13 protein-coding genes (PCGs), two rRNA genes, 22 tRNA genes, and one control region. The overall base composition was as follows: A, 31.50%; T, 29.40%; G, 13.30% and C, 25.80%, with a high A+T bias of 60.9%. In the PCGs, ND6 had deviation ranges for AT skew (–0.303) and GC skew (0.636). The Ka/Ks values of ND1 (1.067) and ND6 (1.352) genes were >1, indicating positive selection, which might play an important role in the adaptation of L. yarkandensis to arid and hot environments. The conserved sequence block, the central conserved domain, and the extended termination-associated sequences of the control region and their features were identified and described. The phylogenetic tree based on the complete mitogenome showed that L. yarkandensis was closely related to the sympatric Lepus tibetanus pamirensis . These novel datasets of L. yarkandensis can supply basic data for phylogenetic studies of Lepus spp., apart from providing essential and important resource for further genetic research and the protection of this species.

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          Most cited references 52

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          MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

          We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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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.

                Author and article information

                Pensoft Publishers
                26 January 2021
                : 1012
                : 135-150
                [1 ] Xinjiang Key Laboratory of Biological Resources and Genetic Engineering, College of Life Science and Technology, Xinjiang University, Urumqi, China, 830046 Xinjiang University Urumqi China
                Author notes
                Corresponding author: Wenjuan Shan ( swj@ 123456xju.edu.cn )

                Academic editor: P. Stoev

                Wenjuan Shan, Mayinur Tursun, Shiyu Zhou, Yucong Zhang, Huiying Dai

                This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Research Article
                Evolutionary Biology


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