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      Transcriptome Analysis of Roots from Wheat (Triticum aestivum L.) Varieties in Response to Drought Stress

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      International Journal of Molecular Sciences
      MDPI AG

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          Abstract

          Under climate change, drought is one of the most limiting factors that influences wheat (Triticum aestivum L.) production. Exploring stress-related genes is vital for wheat breeding. To identify genes related to the drought tolerance response, two common wheat cultivars, Zhengmai 366 (ZM366) and Chuanmai 42 (CM42), were selected based on their obvious difference in root length under 15% PEG-6000 treatment. The root length of the ZM366 cultivar was significantly longer than that of CM42. Stress-related genes were identified by RNA-seq in samples treated with 15% PEG-6000 for 7 days. In total, 11,083 differentially expressed genes (DEGs) and numerous single nucleotide polymorphisms (SNPs) and insertions/deletions (InDels) were identified. GO enrichment analysis revealed that the upregulated genes were mainly related to the response to water, acidic chemicals, oxygen-containing compounds, inorganic substances, and abiotic stimuli. Among the DEGs, the expression levels of 16 genes in ZM366 were higher than those in CM42 after the 15% PEG-6000 treatment based on RT-qPCR. Furthermore, EMS-induced mutants in Kronos (T. turgidum L.) of 4 representative DEGs possessed longer roots than the WT after the 15% PEG-6000 treatment. Altogether, the drought stress genes identified in this study represent useful gene resources for wheat breeding.

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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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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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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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                Author and article information

                Journal
                IJMCFK
                International Journal of Molecular Sciences
                IJMS
                MDPI AG
                1422-0067
                April 2023
                April 14 2023
                : 24
                : 8
                : 7245
                Article
                10.3390/ijms24087245
                10139362
                37108408
                651db67b-8ff0-4c69-aeb1-ce38f55d924e
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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