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      Metabolite signatures of diverse Camellia sinensis tea populations

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          Abstract

          The tea plant ( Camellia sinensis) presents an excellent system to study evolution and diversification of the numerous classes, types and variable contents of specialized metabolites. Here, we investigate the relationship among C. sinensis phylogenetic groups and specialized metabolites using transcriptomic and metabolomic data on the fresh leaves collected from 136 representative tea accessions in China. We obtain 925,854 high-quality single-nucleotide polymorphisms (SNPs) enabling the refined grouping of the sampled tea accessions into five major clades. Untargeted metabolomic analyses detect 129 and 199 annotated metabolites that are differentially accumulated in different tea groups in positive and negative ionization modes, respectively. Each phylogenetic group contains signature metabolites. In particular, CSA tea accessions are featured with high accumulation of diverse classes of flavonoid compounds, such as flavanols, flavonol mono-/di-glycosides, proanthocyanidin dimers, and phenolic acids. Our results provide insights into the genetic and metabolite diversity and are useful for accelerated tea plant breeding.

          Abstract

          The molecular basis for the unique taste and aroma of tea cultivars is largely unknown, but is critical for breeding new cultivars. Here the authors use transcriptomics and metabolomics to study the relationship among phylogenetic groups and specialized metabolites from 136 tea accessions in China.

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          Most cited references75

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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              PLINK: a tool set for whole-genome association and population-based linkage analyses.

              Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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                Author and article information

                Contributors
                liangchen@tricaas.com
                yang@ucr.edu
                ryliu@fafu.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                4 November 2020
                4 November 2020
                2020
                : 11
                : 5586
                Affiliations
                [1 ]GRID grid.256111.0, ISNI 0000 0004 1760 2876, FAFU-UCR Joint Center for Horticultural Biology and Metabolomics, Haixia Institute of Science and Technology, , Fujian Agriculture and Forestry University, ; 350002 Fuzhou, China
                [2 ]GRID grid.9227.e, ISNI 0000000119573309, Shanghai Center for Plant Stress Biology, , Chinese Academy of Sciences, ; 3888 Chenhua Road, 201602 Shanghai, China
                [3 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, University of Chinese Academy of Sciences, ; 100049 Beijing, China
                [4 ]GRID grid.464455.2, Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture and Rural Affairs, Tea Research Institute, Chinese Academy of Agricultural Sciences, ; 310008 Hangzhou, China
                [5 ]GRID grid.108266.b, ISNI 0000 0004 1803 0494, College of Horticulture, , Henan Agricultural University, ; 450000 Zhengzhou, China
                [6 ]Wuyi Star Tea Industry Co., Ltd, 354300 Wuyishan, China
                [7 ]GRID grid.266097.c, ISNI 0000 0001 2222 1582, Institute of Integrative Genome Biology, , University of California at Riverside, ; Riverside, CA 92521 USA
                [8 ]GRID grid.266097.c, ISNI 0000 0001 2222 1582, Department of Botany and Plant Sciences, , University of California at Riverside, ; Riverside, CA 92521 USA
                [9 ]GRID grid.256111.0, ISNI 0000 0004 1760 2876, Center for Agroforestry Mega Data Science, Haixia Institute of Science and Technology, , Fujian Agriculture and Forestry University, ; 350002 Fuzhou, China
                Author information
                http://orcid.org/0000-0002-8650-6795
                http://orcid.org/0000-0001-5453-2377
                http://orcid.org/0000-0002-7507-3947
                http://orcid.org/0000-0001-7482-6540
                http://orcid.org/0000-0002-2534-4171
                Article
                19441
                10.1038/s41467-020-19441-1
                7642434
                33149146
                49e5667d-af59-4cd0-a8dd-67cd319e013c
                © The Author(s) 2020

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 28 November 2019
                : 14 October 2020
                Funding
                Funded by: the Fujian Agriculture and Forestry University (FAFU) Construction Project for Technological Innovation and Service System of Tea Industry Chain (K1520005A02) and other funds form FAFU.
                Funded by: Funds from FAFU.
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                metabolomics,genome informatics,natural variation in plants,plant evolution,secondary metabolism

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