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      Comparative transcriptomics reveals hidden issues in the plant response to arthropod herbivores

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          Abstract

          Plants experience different abiotic/biotic stresses, which trigger their molecular machinery to cope with them. Besides general mechanisms prompted by many stresses, specific mechanisms have been introduced to optimize the response to individual threats. However, these key mechanisms are difficult to identify. Here, we introduce an in‐depth species‐specific transcriptomic analysis and conduct an extensive meta‐analysis of the responses to related species to gain more knowledge about plant responses. The spider mite Tetranychus urticae was used as the individual species, several arthropod herbivores as the related species for meta‐analysis, and Arabidopsis thaliana plants as the common host. The analysis of the transcriptomic data showed typical common responses to herbivory, such as jasmonate signaling or glucosinolate biosynthesis. Also, a specific set of genes likely involved in the particularities of the Arabidopsis‐spider mite interaction was discovered. The new findings have determined a prominent role in this interaction of the jasmonate‐induced pathways leading to the biosynthesis of anthocyanins and tocopherols. Therefore, tandem individual/general transcriptomic profiling has been revealed as an effective method to identify novel relevant processes and specificities in the plant response to environmental stresses.

          Abstract

          An in‐depth, species‐specific transcriptomic analysis and meta‐analysis of the responses to related species improves our understanding of plant responses to arthropod herbivores. This tandem individual/general transcriptomic profiling provides an effective method to identify novel relevant processes and specificities.

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          Most cited references55

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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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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

              Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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                Author and article information

                Contributors
                m.martinez@upm.es
                Journal
                J Integr Plant Biol
                J Integr Plant Biol
                10.1111/(ISSN)1744-7909
                JIPB
                Journal of Integrative Plant Biology
                John Wiley and Sons Inc. (Hoboken )
                1672-9072
                1744-7909
                12 February 2021
                February 2021
                : 63
                : 2 , Plant Biotic Interactions ( doiID: 10.1111/jipb.v63.2 )
                : 312-326
                Affiliations
                [ 1 ] Centro de Biotecnología y Genómica de Plantas, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria Universidad Politécnica de Madrid Madrid Spain
                [ 2 ] Departamento de Biotecnología‐Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas Universidad Politécnica de Madrid Madrid Spain
                Author notes
                [*] [* ]Correspondence: Manuel Martinez ( m.martinez@ 123456upm.es )

                Article
                JIPB13026
                10.1111/jipb.13026
                7898633
                33085192
                70b5858a-aea3-4ae5-b73c-0a821d77b811
                © 2020 The Authors. Journal of Integrative Plant Biology published by John Wiley & Sons Australia, Ltd on behalf of Institute of Botany, Chinese Academy of Sciences

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 August 2020
                : 18 October 2020
                Page count
                Figures: 9, Tables: 0, Pages: 15, Words: 0
                Funding
                Funded by: Ministerio de Economía, Industria y Competitividad
                Award ID: BIO2017‐83472‐R
                Award ID: RED2018‐102407‐T
                Award ID: RyC17MESFB
                Funded by: Convenio Plurianual between Comunidad de Madrid
                Funded by: Universidad Politécnica de Madrid , open-funder-registry 10.13039/501100003759;
                Funded by: Programa de Apoyo a la Realización de Proyectos de I+D para Jóvenes Investigadores
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                February 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.7 mode:remove_FC converted:22.02.2021

                arabidopsis thaliana,arthropod herbivore,comparative transcriptomics,plant defense,tetranychus urticae

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