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Differential gene expression and gene ontologies associated with increasing water-stress in leaf and root transcriptomes of perennial ryegrass (Lolium perenne)

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      Abstract

      Perennial ryegrass ( Lolium perenne) is a forage and amenity grass species widely cultivated in temperate regions worldwide. As such, perennial ryegrass populations are exposed to a range of environmental conditions and stresses on a seasonal basis and from year to year. One source of potential stress is limitation on water availability. The ability of these perennial grasses to be able to withstand and recover after periods of water limitation or drought can be a key component of grassland performance. Thus, we were interested in looking at changes in patterns of gene expression associated with increasing water stress. Clones of a single genotype of perennial ryegrass were grown under non-flowering growth room conditions in vermiculite supplemented with nutrient solution. Leaf and root tissue was sampled at 4 times in quadruplicate relating to estimated water contents of 35%, 15%, 5% and 1%. RNA was extracted and RNAseq used to generate transcriptome profiles at each sampling point. Transcriptomes were assembled using the published reference genome sequence and differential gene expression analysed using 3 different programmes, DESeq2, edgeR and limma (with the voom transformation), individually and in combination, deriving Early, Middle and Late stage comparisons. Identified differentially expressed genes were then associated with enriched GO terms using BLAST2GO. For the leaf, up-regulated differentially expressed genes were strongly associated with GO terms only during the Early stage and the majority of GO terms were associated with only down-regulated genes at the Middle or Late stages. For the roots, few differentially expressed genes were identified at either Early or Middle stages. Only one replicate at 1% estimated water content produced high quality data for the root, however, this indicated a high level of differential expression. Again the majority of enriched GO terms were associated with down-regulated genes. The performance of the different analysis programmes and the annotations associated with identified differentially expressed genes is discussed.

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      Most cited references 71

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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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        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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          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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            Author and article information

            Affiliations
            [1 ] Quantitative Proteomics, Institute of Molecular Biology (IMB), Mainz, Germany
            [2 ] Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
            Louisiana State University, UNITED STATES
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            Contributors
            Role: Data curation, Role: Formal analysis, Role: Investigation, Role: Software, Role: Writing – review & editing
            Role: Data curation, Role: Formal analysis, Role: Methodology, Role: Resources, Role: Writing – review & editing
            Role: Resources, Role: Writing – review & editing
            Role: Resources, Role: Writing – review & editing
            Role: Methodology, Role: Resources, Role: Writing – review & editing
            Role: Conceptualization, Role: Formal analysis, Role: Methodology, Role: Writing – original draft, Role: Writing – review & editing
            ORCID: http://orcid.org/0000-0002-6421-1080, Role: Conceptualization, Role: Data curation, Role: Formal analysis, Role: Investigation, Role: Methodology, Role: Project administration, Role: Supervision, Role: Validation, Role: Writing – original draft, Role: Writing – review & editing
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, CA USA )
            1932-6203
            30 July 2019
            2019
            : 14
            : 7
            31361773 6667212 10.1371/journal.pone.0220518 PONE-D-19-09634
            © 2019 Fradera-Sola et al

            This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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            Figures: 3, Tables: 10, Pages: 29
            Product
            Funding
            Funded by: funder-id http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
            Award ID: BB/J004405/1
            Funded by: funder-id http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
            Award ID: BB/CSP1730/1
            This work has been funded by the Biotechnology and Biological Science Research Council (BBSRC) through the following institutional awards: BB/J004405/1 (ISPG) and BB/CSP1730/1 (CS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Categories
            Research Article
            Biology and Life Sciences
            Computational Biology
            Genome Analysis
            Gene Ontologies
            Biology and Life Sciences
            Genetics
            Genomics
            Genome Analysis
            Gene Ontologies
            Biology and Life Sciences
            Genetics
            Gene Expression
            Biology and Life Sciences
            Computational Biology
            Genome Analysis
            Transcriptome Analysis
            Biology and Life Sciences
            Genetics
            Genomics
            Genome Analysis
            Transcriptome Analysis
            Biology and Life Sciences
            Biochemistry
            Metabolism
            Metabolic Processes
            Biology and life sciences
            Biochemistry
            Proteins
            DNA-binding proteins
            Biology and Life Sciences
            Cell Biology
            Cell Physiology
            Cell Binding
            Biology and Life Sciences
            Organisms
            Eukaryota
            Plants
            Grasses
            Ryegrass
            Biology and Life Sciences
            Genetics
            Gene Expression
            Gene Regulation
            Custom metadata
            The data underlying the results presented in the study are available from ENA repository accession number PRJEB31812.

            Uncategorized

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