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      Integrated metabolomics and transcriptomics analysis of roots of Bupleurum chinense and B. scorzonerifolium, two sources of medicinal Chaihu

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      Scientific Reports
      Nature Publishing Group UK
      Genetics, Plant sciences

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          Abstract

          Radix Bupleuri (Chaihu in Chinese) is a traditional Chinese medicine commonly used to treat colds and fevers. The root metabolome and transcriptome of two cultivars of B. chinense (BCYC and BCZC) and one of B. scorzonerifolium (BSHC) were determined and analyzed. Compared with BSHC, 135 and 194 differential metabolites were identified in BCYC and BCZC, respectively, which were mainly fatty acyls, organooxygen metabolites. A total of 163 differential metabolites were obtained between BCYC and BCZC, including phenolic acids and lipids. Compared with BSHC, 6557 and 5621 differential expression genes (DEGs) were found in BCYC and BSHC, respectively, which were annotated into biosynthesis of unsaturated fatty acid and fatty acid metabolism. A total of 4,880 DEGs existed between the two cultivars of B. chinense. The abundance of flavonoids in B. scorzonerifolium was higher than that of B. chinense, with the latter having higher saikosaponin A and saikosaponin D than the former. Pinobanksin was the most major flavonoid which differ between the two cultivars of B. chinense. The expression of chalcone synthase gene was dramatically differential, which had a positive correlation with the biosynthesis of pinobanksin. The present study laid a foundation for further research on biosynthesis of flavonoids and terpenoids of Bupleurum L.

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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              StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.

              Methods used to sequence the transcriptome often produce more than 200 million short sequences. We introduce StringTie, a computational method that applies a network flow algorithm originally developed in optimization theory, together with optional de novo assembly, to assemble these complex data sets into transcripts. When used to analyze both simulated and real data sets, StringTie produces more complete and accurate reconstructions of genes and better estimates of expression levels, compared with other leading transcript assembly programs including Cufflinks, IsoLasso, Scripture and Traph. For example, on 90 million reads from human blood, StringTie correctly assembled 10,990 transcripts, whereas the next best assembly was of 7,187 transcripts by Cufflinks, which is a 53% increase in transcripts assembled. On a simulated data set, StringTie correctly assembled 7,559 transcripts, which is 20% more than the 6,310 assembled by Cufflinks. As well as producing a more complete transcriptome assembly, StringTie runs faster on all data sets tested to date compared with other assembly software, including Cufflinks.
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                Author and article information

                Contributors
                791537534@qq.com
                wjianh@263.net
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                26 December 2022
                26 December 2022
                2022
                : 12
                : 22335
                Affiliations
                GRID grid.506261.6, ISNI 0000 0001 0706 7839, Institute of Medicinal Plant Development (IMPLAD), , Chinese Academy of Medical Sciences & Peking Union Medical College (Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education & National Engineering Laboratory for Breeding of Endangered Medicinal Materials), ; Beijing, 100193 China
                Article
                27019
                10.1038/s41598-022-27019-8
                9792521
                36572795
                f08dccaf-0b3d-4a0e-8e4c-2b3c90df468d
                © The Author(s) 2022

                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
                : 2 July 2022
                : 23 December 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100012166, National Key Research and Development Program of China;
                Award ID: 2019YFC1710800
                Funded by: CAMS Innovation Fund for Medical Sciences (CIFMS)
                Award ID: 2016-I2M-2-003
                Award ID: 2019-I2M-3-001
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                genetics,plant sciences
                Uncategorized
                genetics, plant sciences

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