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      Transcriptome and Metabolome Analyses Provide Insights Into the Composition and Biosynthesis of Grassy Aroma Volatiles in White-Fleshed Pitaya

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          Abstract

          Aroma is one of the major inherent quality characteristics in fruits. Understanding the composition of aroma volatiles and their biosynthesis mechanism is crucial to improving fruit quality. However, the biosynthesis mechanism of aroma volatiles has not been characterized yet in white-fleshed pitaya ( Hylocereus undatus). This study was performed to investigate aroma volatiles and related gene expression patterns in the pulp of “mild grassy” and “strong grassy” aroma cultivars. Analysis of volatile composition and concentration showed that aldehydes, alcohols, esters, and alkenes were predominant in both cultivars. However, comparative analysis revealed a significant difference in the concentration of several metabolites, particularly hexanal and 1-hexanol. The results of the comparative transcriptome identified a large number of aroma-related differentially expressed genes. The majority of these genes were enriched in fatty acid and isoleucine degradation pathways. According to integrative analyses, changes in the expression of lipoxygenase pathway genes, specifically FAD, LOXs, HPLs, and ADHs, probably lead to the difference in strength of “grassy” aroma between both cultivars. The qRT-PCR of 18 aroma-related genes was performed to validate the transcriptome analysis. Our results identified key genes and pathways connected with the biosynthesis of aroma volatiles in white-fleshed pitaya. These results will be useful to dissect the genetic mechanism of fruit aroma in white-fleshed pitaya.

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          Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data

          Massively-parallel cDNA sequencing has opened the way to deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here, we present the Trinity methodology for de novo full-length transcriptome reconstruction, and evaluate it on samples from fission yeast, mouse, and whitefly – an insect whose genome has not yet been sequenced. Trinity fully reconstructs a large fraction of the transcripts present in the data, also reporting alternative splice isoforms and transcripts from recently duplicated genes. In all cases, Trinity performs better than other available de novo transcriptome assembly programs, and its sensitivity is comparable to methods relying on genome alignments. Our approach provides a unified and general solution for transcriptome reconstruction in any sample, especially in the complete absence of a reference genome.
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            KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases

            High-throughput experimental technologies often identify dozens to hundreds of genes related to, or changed in, a biological or pathological process. From these genes one wants to identify biological pathways that may be involved and diseases that may be implicated. Here, we report a web server, KOBAS 2.0, which annotates an input set of genes with putative pathways and disease relationships based on mapping to genes with known annotations. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). KOBAS 2.0 can be accessed at http://kobas.cbi.pku.edu.cn.
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              SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data

              Background Several R packages exist for the detection of differentially expressed genes from RNA-Seq data. The analysis process includes three main steps, namely normalization, dispersion estimation and test for differential expression. Quality control steps along this process are recommended but not mandatory, and failing to check the characteristics of the dataset may lead to spurious results. In addition, normalization methods and statistical models are not exchangeable across the packages without adequate transformations the users are often not aware of. Thus, dedicated analysis pipelines are needed to include systematic quality control steps and prevent errors from misusing the proposed methods. Results SARTools is an R pipeline for differential analysis of RNA-Seq count data. It can handle designs involving two or more conditions of a single biological factor with or without a blocking factor (such as a batch effect or a sample pairing). It is based on DESeq2 and edgeR and is composed of an R package and two R script templates (for DESeq2 and edgeR respectively). Tuning a small number of parameters and executing one of the R scripts, users have access to the full results of the analysis, including lists of differentially expressed genes and a HTML report that (i) displays diagnostic plots for quality control and model hypotheses checking and (ii) keeps track of the whole analysis process, parameter values and versions of the R packages used. Conclusions SARTools provides systematic quality controls of the dataset as well as diagnostic plots that help to tune the model parameters. It gives access to the main parameters of DESeq2 and edgeR and prevents untrained users from misusing some functionalities of both packages. By keeping track of all the parameters of the analysis process it fits the requirements of reproducible research.
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                Author and article information

                Journal
                ACS Omega
                ACS Omega
                ao
                acsodf
                ACS Omega
                American Chemical Society
                2470-1343
                18 February 2022
                01 March 2022
                : 7
                : 8
                : 6518-6530
                Affiliations
                []Horticultural Research Institute, Guangxi Academy of Agricultural Sciences , Nanning 530007, China
                []Guangxi Research Academy of Environmental Sciences , Nanning 530022, China
                Author notes
                Author information
                https://orcid.org/0000-0002-2482-2207
                Article
                10.1021/acsomega.1c05340
                8892475
                35252648
                e51d27cf-b369-4c53-8bec-7a5335596466
                © 2022 The Authors. Published by American Chemical Society

                Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works ( https://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 26 September 2021
                : 13 December 2021
                Funding
                Funded by: Guangxi Academy of Agricultural Sciences, doi 10.13039/501100015407;
                Award ID: GuiNongKe 2021YT047
                Funded by: Guangxi Innovation Team of National Modern Agricultural Industry Technology System, doi NA;
                Award ID: nycytxgxcxtd-17-13
                Funded by: Guangxi Innovation Team of National Modern Agricultural Industry Technology System, doi NA;
                Award ID: nycytxgxcxtd-17-06
                Funded by: Science and Technology Development Foundation, Guangxi Academy of Agricultural Sciences, doi NA;
                Award ID: GuiNongKe 2021JM09
                Funded by: Guangxi Science and Technology Project, doi NA;
                Award ID: GuiKe AD18281080
                Funded by: Guangxi Science and Technology Project, doi NA;
                Award ID: GuiKe AB19245012
                Funded by: Science and Technology Development Foundation, Guangxi Academy of Agricultural Sciences, doi NA;
                Award ID: 31960578
                Funded by: Guangxi Science and Technology Project, doi NA;
                Award ID: 2021GXNSFBA196010
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                ao1c05340

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