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Identification, Nomenclature, and Evolutionary Relationships of Mitogen-Activated Protein Kinase (MAPK) Genes in Soybean

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      Abstract

      Mitogen-activated protein kinase (MAPK) genes in eukaryotes regulate various developmental and physiological processes including those associated with biotic and abiotic stresses. Although MAPKs in some plant species including Arabidopsis have been identified, they are yet to be identified in soybean. Major objectives of this study were to identify GmMAPKs, assess their evolutionary relationships, and analyze their functional divergence. We identified a total of 38 MAPKs, eleven MAPKKs, and 150 MAPKKKs in soybean. Within the GmMAPK family, we also identified a new clade of six genes: four genes with TEY and two genes with TQY motifs requiring further investigation into possible legume-specific functions. The results indicated the expansion of the GmMAPK families attributable to the ancestral polyploidy events followed by chromosomal rearrangements. The GmMAPK and GmMAPKKK families were substantially larger than those in other plant species. The duplicated GmMAPK members presented complex evolutionary relationships and functional divergence when compared to their counterparts in Arabidopsis. We also highlighted existing nomenclatural issues, stressing the need for nomenclatural consistency. GmMAPK identification is vital to soybean crop improvement, and novel insights into the evolutionary relationships will enhance our understanding about plant genome evolution.

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      MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

      Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.
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        Clustal W and Clustal X version 2.0.

        The Clustal W and Clustal X multiple sequence alignment programs have been completely rewritten in C++. This will facilitate the further development of the alignment algorithms in the future and has allowed proper porting of the programs to the latest versions of Linux, Macintosh and Windows operating systems. The programs can be run on-line from the EBI web server: http://www.ebi.ac.uk/tools/clustalw2. The source code and executables for Windows, Linux and Macintosh computers are available from the EBI ftp site ftp://ftp.ebi.ac.uk/pub/software/clustalw2/
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          Confidence Limits on Phylogenies: An Approach Using the Bootstrap

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            Author and article information

            Affiliations
            [1 ]Department of Biology and Microbiology, South Dakota State University, Brookings SD, USA.
            Author notes
            Corresponding author email: m adhav.nepal@ 123456sdstate.edu
            Journal
            Evol Bioinform Online
            Evol. Bioinform. Online
            101256319
            Evolutionary Bioinformatics Online
            Libertas Academica
            1176-9343
            2013
            22 September 2013
            : 9
            : 363-386
            24137047
            3785387
            10.4137/EBO.S12526
            ebo-9-2013-363
            © 2013 the author(s), publisher and licensee Libertas Academica Ltd.

            This is an open access article published under the Creative Commons CC-BY-NC 3.0 license.

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            Original Research

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