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      Transcriptomic analysis reveals protein homeostasis breakdown in the coral Acropora millepora during hypo-saline stress

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          Abstract

          Background

          Coral reefs can experience salinity fluctuations due to rainfall and runoff; these events can have major impacts on the corals and lead to bleaching and mortality. On the Great Barrier Reef (GBR), low salinity events, which occur during summer seasons and can involve salinity dropping ~ 10 PSU correlate with declines in coral cover, and these events are predicted to increase in frequency and severity under future climate change scenarios. In other marine invertebrates, exposure to low salinity causes increased expression of genes involved in proteolysis, responses to oxidative stress, and membrane transport, but the effects that changes in salinity have on corals have so far received only limited attention. To better understand the coral response to hypo-osmotic stress, here we investigated the transcriptomic response of the coral Acropora millepora in both adult and juvenile life stages to acute (1 h) and more prolonged (24 h) exposure to low salinity.

          Results

          Differential gene expression analysis revealed the involvement of both common and specific response mechanisms in Acropora. The general response to environmental stressors included up-regulation of genes involved in the mitigation of macromolecular and oxidative damage, while up-regulation of genes involved in amino acid metabolism and transport represent specific responses to salinity stress.

          Conclusions

          This study is the first comprehensive transcriptomic analysis of the coral response to low salinity stress and provides important insights into the likely consequences of heavy rainfall and runoff events on coral reefs.

          Electronic supplementary material

          The online version of this article (10.1186/s12864-019-5527-2) contains supplementary material, which is available to authorized users.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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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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              HTSeq—a Python framework to work with high-throughput sequencing data

              Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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                Author and article information

                Contributors
                david.miller@jcu.edu.au
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                20 February 2019
                20 February 2019
                2019
                : 20
                : 148
                Affiliations
                [1 ]ISNI 0000 0004 0474 1797, GRID grid.1011.1, AIMS@JCU and Department of Molecular and Cell Biology, , James Cook University, ; Townsville, Queensland 4811 Australia
                [2 ]ISNI 0000 0004 0474 1797, GRID grid.1011.1, ARC Centre of Excellence for Coral Reef Studies and Department of Molecular and Cell Biology, , James Cook University, ; Townsville, Queensland 4811 Australia
                [3 ]ISNI 0000 0004 1936 8606, GRID grid.26790.3a, Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School of Marine & Atmospheric Science, , University of Miami, ; 4600 Rickenbacker Causeway, Miami, Florida 33149 USA
                [4 ]ISNI 0000 0001 1266 2261, GRID grid.3532.7, Atlantic Oceanographic and Meteorological Laboratories (AOML), NOAA, ; 4301 Rickenbacker Causeway, Miami, Florida 33149 USA
                [5 ]ISNI 0000 0004 1936 7611, GRID grid.117476.2, Climate Change Cluster (C3), , University of Technology, ; Sydney, NSW 2007 Australia
                [6 ]ISNI 0000 0001 2180 7477, GRID grid.1001.0, Division of Ecology and Evolution, Research School of Biology, , Australian National University, ; Canberra, ACT 2601 Australia
                [7 ]Laboratoire d’excellence CORAIL, Centre de Recherches Insulaires et Observatoire de l’Environnement (CRIOBE), Moorea, B.P.1013, Papeete French Polynesia
                [8 ]ISNI 0000 0001 0328 1619, GRID grid.1046.3, Australian Institute of Marine Science, ; Townsville, Queensland 4810 Australia
                [9 ]ISNI 0000 0004 0474 1797, GRID grid.1011.1, College of Science and Engineering, , James Cook University, ; Townsville, 4811 Australia
                Article
                5527
                10.1186/s12864-019-5527-2
                6381741
                30786881
                5767260a-5f43-4eb9-b274-1d763fba6387
                © The Author(s). 2019

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 5 September 2018
                : 13 February 2019
                Funding
                Funded by: Centre of Excellence for Coral Reef Studies, Australian Research Council
                Award ID: CE14100020
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Genetics
                coral,transcriptomics,salinity stress,endoplasmic reticulum,amino acid metabolism
                Genetics
                coral, transcriptomics, salinity stress, endoplasmic reticulum, amino acid metabolism

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