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      Genome-wide identification and comparative analysis of Dmrt genes in echinoderms

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          Abstract

          The Dmrt (Doublesex-mab3-related transcription factor) gene family is a class of crucial transcription factors characterized by one or several conserved DM (Doublesex/Mab-3) domains. Dmrt family genes can participate in various physiological developmental processes, especially in sex determination/differentiation. Echinoderms are extremely important research objects in various fields, such as sex determination/differentiation and neuroscience. However, to date, the genome-wide characterization and analysis of Dmrt genes in echinoderms have not been investigated. In this study, the identification and analysis of Dmrt genes in 11 representative echinoderms were performed using bioinformatics methods. A total of 43 Dmrt genes have been found in the studied echinoderms, and the number of Dmrt genes in different species ranges from 2 to 5. The phylogenetic tree showed that all Dmrt genes from echinoderms can be subdivided into 5 classes, the Dmrt2-like class, Dmrt3-like class, Dmrt4/5-like class, Dsx-like class, and a novel Dmrt (starfish-specific) class. Furthermore, selective pressure assessment suggested that the Dmrt genes underwent purifying selection pressure. In general, this study provides a molecular basis for echinoderm Dmrt genes and may serve as a reference for in-depth phylogenomics.

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          Most cited references47

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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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            Basic local alignment search tool.

            A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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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
                2315856979@qq.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                11 May 2023
                11 May 2023
                2023
                : 13
                : 7664
                Affiliations
                [1 ]GRID grid.9227.e, ISNI 0000000119573309, Key Laboratory of Coastal Biology and Bioresource Utilization, Yantai Institute of Coastal Zone Research, , Chinese Academy of Sciences, ; Yantai, 264003 China
                [2 ]GRID grid.453137.7, ISNI 0000 0004 0406 0561, Key Laboratory of Ecological Warning, Protection and Restoration for Bohai Sea, , Ministry of Natural Resources, ; Qingdao, 266061 China
                [3 ]Yantai Vocational College, Yantai, 264670 China
                Article
                34819
                10.1038/s41598-023-34819-z
                10175285
                37169947
                84df5a0e-afc5-4429-9d54-08e5c27be072
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 November 2022
                : 8 May 2023
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                © Springer Nature Limited 2023

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                evolution,genetics
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                evolution, genetics

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