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      Comparative and phylogenetic analysis of the complete chloroplast genomes of Uncaria (Rubiaceae) species

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          Abstract

          The genus Uncaria is famous for its high medicinal value. However, the high morphological similarities and unclear interspecific genetic relationships have posed challenges to the classification and identification of Uncaria species. Here, we newly sequenced six chloroplast genomes of Uncaria species: U. hirsuta, U. rhynchophylla, U. rhynchophylloides, U. homomalla, U. sinensis, and U. lancifolia. Comparisons among the chloroplast genomes of Uncaria species showed their conservation in structure, gene content, and order. Ten highly variable loci could be potentially used as specific molecular markers in the identification of Uncaria species. The third position of codons tended to use A/U base, and natural selection contributed more to the formation of codon usage bias in comparison to mutation pressure. Four genes ( rbcL, ndhF, rps8, and ycf2) were detected to be subjected to positive selection. Phylogenetic analysis showed that the genus Uncaria was a monophyletic group, belonging to the tribe Naucleeae. Moreover, U. sinensis was not a variant of U. rhynchophylla. U. rhynchophylloides and U. rhynchophylla were not the same species. The results of the comparative and phylogenetic analysis provide valuable references for further research studies of classification, identification, breeding improvement, and phylogenetic relationships in Uncaria species.

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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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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                Author and article information

                Contributors
                Role: Role: Role: Role: Role: Role:
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                URI : https://loop.frontiersin.org/people/2383951Role: Role: Role: Role:
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                22 December 2023
                2023
                : 14
                : 1271689
                Affiliations
                [1] 1 School of Biosciences and Biopharmaceutics, Guangdong Pharmaceutical University , Guangzhou, China
                [2] 2 School of Pharmacy, Guangdong Pharmaceutical University , Guangzhou, China
                Author notes

                Edited by: Sara M. Handy, United States Food and Drug Administration, United States

                Reviewed by: Bangxing Han, West Anhui University, China

                Gao Jihai, Chengdu University of Traditional Chinese Medicine, China

                *Correspondence: Shuang Zhu, 15683727@ 123456qq.com
                Article
                10.3389/fpls.2023.1271689
                10766718
                5a1527fa-eae7-456b-bb4b-27cda2075652
                Copyright © 2023 Dai, Liu, Xu, Tan, Lin, Gao and Zhu

                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
                : 02 August 2023
                : 05 December 2023
                Page count
                Figures: 8, Tables: 2, Equations: 0, References: 60, Pages: 13, Words: 5296
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by Science and Technology Planning Project of Guangdong Province (2017A020213014).
                Categories
                Plant Science
                Original Research
                Custom metadata
                Plant Bioinformatics

                Plant science & Botany
                uncaria,chloroplast genome,taxonomy,codon usage bias,phylogenetic analysis
                Plant science & Botany
                uncaria, chloroplast genome, taxonomy, codon usage bias, phylogenetic analysis

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