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      Comparative analysis of twelve mitogenomes of Caliscelidae (Hemiptera: Fulgoromorpha) and their phylogenetic implications

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      Caliscelini, Peltonotellini, Ommatidiotinae, Mitogenome, Phylogenetics

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          Abstract

          Here, the complete mitochondrial genomes (mitogenomes) of 12 Caliscelidae species, Augilina tetraina, Augilina triaina, Symplana brevistrata, Symplana lii, Neosymplana vittatum, Pseudosymplanella nigrifasciata, Symplanella brevicephala, Symplanella unipuncta, Augilodes binghami, Cylindratus longicephalus, Caliscelis shandongensis, and Peltonotellus sp., were determined and comparatively analyzed. The genomes varied from 15,424 to 16,746 bp in size, comprising 37 mitochondrial genes and an A+T-rich region. The typical gene content and arrangement were similar to those of most Fulgoroidea species. The nucleotide compositions of the mitogenomes were biased toward A/T. All protein-coding genes (PCGs) started with a canonical ATN or GTG codon and ended with TAN or an incomplete stop codon, single T. Among 13 PCGs in 16 reported Caliscelidae mitogenomes, cox1 and atp8 showed the lowest and highest nucleotide diversity, respectively. All PCGs evolved under purifying selection, with atp8 considered a comparatively fast-evolving gene. Phylogenetic relationships were reconstructed based on 13 PCGs in 16 Caliscelidae species and five outgroups using maximum likelihood and Bayesian inference analyses. All species of Caliscelidae formed a steadily monophyletic group with high support. Peltonotellini was present at the basal position of the phylogenetic tree. Augilini was the sister group to Caliscelini and Peltonotellini.

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          Most cited references 64

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          MUSCLE: multiple sequence alignment with high accuracy and high throughput.

           Robert Edgar (2004)
          We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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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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              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
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ Inc. (San Diego, USA )
                2167-8359
                16 November 2021
                2021
                : 9
                Affiliations
                [1 ]Guizhou University, Institute of Entomology , Guiyang, Guizhou, China
                [2 ]Guizhou University, The Provincial Special Key Laboratory for Development and Utilization of Insect Resources , Guiyang, Guizhou, China
                [3 ]Guizhou University, The Provincial Key Laboratory for Agricultural Pest Management of Mountainous Regions , Guiyang, Guizhou, China
                Article
                12465
                10.7717/peerj.12465
                8603831
                e48db7ec-fb55-40a6-93c6-f68f75fba66a
                © 2021 Gong et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 32060343, 31472033
                Funded by: Science and Technology Support Program of Guizhou Province
                Award ID: 20201Y129
                Funded by: Program of Excellent Innovation Talents, Guizhou Province
                Award ID: 20154021
                This work was supported by the National Natural Science Foundation of China (grant nos. 32060343, 31472033); the Science and Technology Support Program of Guizhou Province (grant no. 20201Y129); the Program of Excellent Innovation Talents, Guizhou Province (grant no. 20154021). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Biochemistry
                Entomology
                Genomics
                Molecular Biology
                Zoology

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