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      Genome-Wide Identification and Expression Profile Analysis of WRKY Family Genes in the Autopolyploid Saccharum spontaneum

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          Abstract

          WRKY is one of the largest transcription factor families in plants and plays important roles in the regulation of developmental and physiological processes. To date, the WRKY gene family has not been identified in Saccharum species because of its complex polyploid genome. In this study, a total of 294 sequences for 154 SsWRKY genes were identified in the polyploid Saccharum spontaneum genome and then named on the basis of their chromosome locations, including 13 (8.4%) genes with four alleles, 29 (18.8%) genes with three alleles and 41 (26.6%) genes with two alleles. Among them, 73.8% and 16.0% of the SsWRKY genes originated from segmental duplications and tandem duplications, respectively. The WRKY members exhibited conserved gene structures and amino acid sequences among the allelic haplotypes, which were accompanied by variations in intron sizes. Phylogenetic and collinearity analyses revealed that 27 SsWRKYs originated after the split of sorghum and Saccharum, resulting in a significantly higher number of WRKYs in sugarcane than in the proximal diploid species sorghum. The analysis of RNA-seq data revealed that SsWRKYs’ expression profiles in 46 different samples including different developmental stages revealed distinct temporal and spatial patterns with 52 genes expressed in all tissues, four genes not expressed in any tissues and 21 SsWRKY genes likely to be involved in photosynthesis. The comprehensive analysis of SsWRKYs’ expression will provide an important and valuable foundation for further investigation of the regulatory mechanisms of WRKYs in physiological roles in sugarcane S. spontaneum.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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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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              Is Open Access

              MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity

              MCScan is an algorithm able to scan multiple genomes or subgenomes in order to identify putative homologous chromosomal regions, and align these regions using genes as anchors. The MCScanX toolkit implements an adjusted MCScan algorithm for detection of synteny and collinearity that extends the original software by incorporating 14 utility programs for visualization of results and additional downstream analyses. Applications of MCScanX to several sequenced plant genomes and gene families are shown as examples. MCScanX can be used to effectively analyze chromosome structural changes, and reveal the history of gene family expansions that might contribute to the adaptation of lineages and taxa. An integrated view of various modes of gene duplication can supplement the traditional gene tree analysis in specific families. The source code and documentation of MCScanX are freely available at http://chibba.pgml.uga.edu/mcscan2/.
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                Author and article information

                Journal
                Plant and Cell Physiology
                Oxford University Press (OUP)
                0032-0781
                1471-9053
                March 2020
                March 01 2020
                December 12 2019
                March 2020
                March 01 2020
                December 12 2019
                : 61
                : 3
                : 616-630
                Affiliations
                [1 ]Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Center for Genomics and Biotechnology, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
                [2 ]Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning, Guangxi 530004, China
                [3 ]Provincial Key Laboratory for Developmental Biology and Neurosciences, College of Life Sciences, Fujian Normal University, Fuzhou 350007, China
                [4 ]Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
                Article
                10.1093/pcp/pcz227
                16b6389a-5b4d-4500-8e8a-de1eb95f7c4f
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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