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      Genome and evolution of the shade‐requiring medicinal herb Panax ginseng

      research-article
      1 , 1 , 1 , 2 , 1 , 1 , 3 , 1 , 3 , 4 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 5 , 6 , 6 , 2 , 7 , 1 , 1 , 1 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 4 , 5 , 21 , 1 ,
      Plant Biotechnology Journal
      John Wiley and Sons Inc.
      Panax ginseng, ginsenosides, evolution, metabolic network, adaptation

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          Summary

          Panax ginseng C. A. Meyer, reputed as the king of medicinal herbs, has slow growth, long generation time, low seed production and complicated genome structure that hamper its study. Here, we unveil the genomic architecture of tetraploid P. ginseng by de novo genome assembly, representing 2.98 Gbp with 59 352 annotated genes. Resequencing data indicated that diploid Panax species diverged in association with global warming in Southern Asia, and two North American species evolved via two intercontinental migrations. Two whole genome duplications ( WGD) occurred in the family Araliaceae (including Panax) after divergence with the Apiaceae, the more recent one contributing to the ability of P. ginseng to overwinter, enabling it to spread broadly through the Northern Hemisphere. Functional and evolutionary analyses suggest that production of pharmacologically important dammarane‐type ginsenosides originated in Panax and are produced largely in shoot tissues and transported to roots; that newly evolved P. ginseng fatty acid desaturases increase freezing tolerance; and that unprecedented retention of chlorophyll a/b binding protein genes enables efficient photosynthesis under low light. A genome‐scale metabolic network provides a holistic view of Panax ginsenoside biosynthesis. This study provides valuable resources for improving medicinal values of ginseng either through genomics‐assisted breeding or metabolic engineering.

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          Most cited references56

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          psRNATarget: a plant small RNA target analysis server

          Plant endogenous non-coding short small RNAs (20–24 nt), including microRNAs (miRNAs) and a subset of small interfering RNAs (ta-siRNAs), play important role in gene expression regulatory networks (GRNs). For example, many transcription factors and development-related genes have been reported as targets of these regulatory small RNAs. Although a number of miRNA target prediction algorithms and programs have been developed, most of them were designed for animal miRNAs which are significantly different from plant miRNAs in the target recognition process. These differences demand the development of separate plant miRNA (and ta-siRNA) target analysis tool(s). We present psRNATarget, a plant small RNA target analysis server, which features two important analysis functions: (i) reverse complementary matching between small RNA and target transcript using a proven scoring schema, and (ii) target-site accessibility evaluation by calculating unpaired energy (UPE) required to ‘open’ secondary structure around small RNA’s target site on mRNA. The psRNATarget incorporates recent discoveries in plant miRNA target recognition, e.g. it distinguishes translational and post-transcriptional inhibition, and it reports the number of small RNA/target site pairs that may affect small RNA binding activity to target transcript. The psRNATarget server is designed for high-throughput analysis of next-generation data with an efficient distributed computing back-end pipeline that runs on a Linux cluster. The server front-end integrates three simplified user-friendly interfaces to accept user-submitted or preloaded small RNAs and transcript sequences; and outputs a comprehensive list of small RNA/target pairs along with the online tools for batch downloading, key word searching and results sorting. The psRNATarget server is freely available at http://plantgrn.noble.org/psRNATarget/.
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            A protocol for generating a high-quality genome-scale metabolic reconstruction.

            Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.
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              Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0.

              Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.
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                Author and article information

                Contributors
                tjyang@snu.ac.kr
                Journal
                Plant Biotechnol J
                Plant Biotechnol. J
                10.1111/(ISSN)1467-7652
                PBI
                Plant Biotechnology Journal
                John Wiley and Sons Inc. (Hoboken )
                1467-7644
                1467-7652
                25 May 2018
                November 2018
                : 16
                : 11 ( doiID: 10.1111/pbi.2018.16.issue-11 )
                : 1904-1917
                Affiliations
                [ 1 ] Department of Plant Science, Plant Genomics and Breeding Institute Research Institute of Agriculture and Life Sciences College of Agriculture and Life Sciences Seoul National University Seoul Korea
                [ 2 ] Phyzen Genomics Institute Seongnam Gyeonggi‐do Korea
                [ 3 ] Department of Life Science Chromosome Research Institute Sahmyook University Seoul Korea
                [ 4 ] Bioprocessing Technology Institute Agency for Science, Technology and Research (A*STAR) Singapore City Singapore
                [ 5 ] School of Chemical Engineering Sungkyunkwan University Jangan‐gu, Suwon, Gyeonggi‐do Korea
                [ 6 ] College of Pharmacy and Research Institute of Pharmaceutical Science Seoul National University Seoul Korea
                [ 7 ] Department of Organismic and Evolutionary Biology Harvard University Herbaria Cambridge MA USA
                [ 8 ] Planning and Coordination Division NIHS, RDA Wanju‐gun Jeollabuk‐do Korea
                [ 9 ] Ginseng Research Division National Institute of Horticultural & Herbal Science, RDA Eumseong Chungcheongbuk‐do Korea
                [ 10 ] Department of Biological Sciences Chungnam National University Daejeon Korea
                [ 11 ] Department of Crop Science Chungnam National University Daejeon Korea
                [ 12 ] Genomics Division National Institute of Agricultural Sciences Jeonju Jeollabuk‐do Korea
                [ 13 ] Department of Bioindustry and Bioresource Engineering Plant Engineering Research Institute Sejong University Seoul Korea
                [ 14 ] Division of Integrative Bioscience and Biotechnology Sejong University Seoul Korea
                [ 15 ] Department of Industrial Plant Science & Technology Chungbuk National University Cheongju Chungcheongbuk‐do Korea
                [ 16 ] Laboratory of Resource and Analysis R&D Headquarters Korea Ginseng Corporation Daejeon Korea
                [ 17 ] Department of Biological Sciences College of Natural Sciences Pusan National University Busan Korea
                [ 18 ] Korean Bioinformation Center Korea Research Institute of Bioscience and Biotechnology Daejeon Korea
                [ 19 ] Graduate School of Biotechnology and Ginseng Bank Kyung Hee University Yongin Gyeonggi‐do Korea
                [ 20 ] Arizona Genomics Institute School of Plant Sciences The University of Arizona Tucson AZ USA
                [ 21 ] Plant Genome Mapping Laboratory College of Agricultural and Environmental Sciences and Franklin College of Arts and Sciences University of Georgia Athens GA USA
                Author notes
                [*] [* ] Correspondence (Tel 82‐2‐880‐4547; fax 82‐2‐873‐2056; email tjyang@ 123456snu.ac.kr )
                [†]

                These authors contributed equally to this work.

                [‡]

                These authors jointly supervised this work.

                Author information
                http://orcid.org/0000-0001-9210-5843
                http://orcid.org/0000-0002-9676-8801
                Article
                PBI12926
                10.1111/pbi.12926
                6181221
                29604169
                e00a1ce3-34d5-4bbf-8ae7-ff1f25e304e2
                © 2018 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 December 2017
                : 19 February 2018
                : 18 March 2018
                Page count
                Figures: 5, Tables: 1, Pages: 14, Words: 10915
                Funding
                Funded by: Next‐Generation BioGreen21 Program
                Award ID: PJ01311901
                Award ID: PJ013238
                Award ID: PJ01334605
                Funded by: Rural Development Administration
                Funded by: Republic of Korea
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                pbi12926
                November 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.5.0.1 mode:remove_FC converted:11.10.2018

                Biotechnology
                panax ginseng,ginsenosides,evolution,metabolic network,adaptation
                Biotechnology
                panax ginseng, ginsenosides, evolution, metabolic network, adaptation

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