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      Profiles of phenolics and their synthetic pathways in Asparagus officinalis L.

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          Highlights

          • 204 phenolics were detected with different accumulation in roots, stems and flowering twigs of both purple and green asparagus influences their respective nutritional values and environmental adaption.

          • A complete synthetic pathway of phenols and flavonoids with 97 synthetic and 17 regulatory gene in transcriptional isoforms level was proposed.

          • Alternative splicing and fusion of phenolic synthesis and regulatory genes involved in differential accumulation of phenolic substances in various organs of asparagus were found.

          • MYB_9 and bHLH_2/7 were predicted as major regulatory players for anthocyanin biosynthesis in purple asparagus.

          Abstract

          The synthetic pathways of some phenolics compounds in asparagus have been reported, however, the diversified phenolics compounds including their modification and transcription regulation remains unknown. Thus, multi-omics strategies were applied to detect the phenolics profiles, contents, and screen the key genes for phenolics biosynthesis and regulation in asparagus. A total of 437 compounds, among which 204 phenolics including 105 flavonoids and 82 phenolic acids were detected with fluctuated concentrations in roots (Rs), spears (Ss) and flowering twigs (Fs) of the both green and purple cultivars. Based on the detected phenolics profiles and contents correlated to the gene expressions of screened synthetic enzymes and regulatory TFs, a full phenolics synthetic pathway of asparagus was proposed for the first time, essential for future breeding of asparagus and scaled healthy phenolics production using synthetic biological strategies.

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          Most cited references34

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

            Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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              WGCNA: an R package for weighted correlation network analysis

              Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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                Author and article information

                Contributors
                Journal
                Food Chem (Oxf)
                Food Chem (Oxf)
                Food Chemistry: Molecular Sciences
                Elsevier
                2666-5662
                14 December 2023
                30 July 2024
                14 December 2023
                : 8
                : 100187
                Affiliations
                [a ]College of Agronomy and Biotechnology, Yunnan Agricultural University (YNAU), Kunming, Yunnan 650201, China
                [b ]Industrial Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu, Sichuan 610023, China
                [c ]Institute of Improvement and Utilization of Characteristic Resource Plants, YNAU, Kunming, China
                [d ]The Laboratory for Crop Production and Intelligent Agriculture of Yunnan Province, Kunming, China
                Author notes
                [* ]Corresponding authors at: College of Agronomy and Biotechnology, Yunnan Agricultural University (YNAU), Kunming, Yunnan 650201, China. lzj1022@ 123456163.com zmao@ 123456ynau.edu.cn
                Article
                S2666-5662(23)00027-8 100187
                10.1016/j.fochms.2023.100187
                10767369
                42c7123b-5bd8-4a8e-878d-92ab04e50dea
                © 2023 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 8 October 2023
                : 30 November 2023
                : 9 December 2023
                Categories
                Research Article

                asparagus officinalis l.,phenolics biosynthesis pathway,transcriptomics,metabolomics

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