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      Cooperative herbivory between two important pests of rice

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          Abstract

          Normally, when different species of herbivorous arthropods feed on the same plant this leads to fitness-reducing competition. We found this to be different for two of Asia’s most destructive rice pests, the brown planthopper and the rice striped stem borer. Both insects directly and indirectly benefit from jointly attacking the same host plant. Double infestation improved host plant quality, particularly for the stemborer because the planthopper fully suppresses caterpillar-induced production of proteinase inhibitors. It also reduced the risk of egg parasitism, due to diminished parasitoid attraction. Females of both pests have adapted their oviposition behaviour accordingly. Their strong preference for plants infested by the other species even overrides their avoidance of plants already attacked by conspecifics. This cooperation between herbivores is telling of adaptations resulting from the evolution of plant-insect interactions, and points out mechanistic vulnerabilities that can be targeted to control these major pests.

          Abstract

          Herbivore cooperation between insect pests can result in substantially greater damage to crops but also constitutes a good target for improved pest control. Liu et al. reveal how the brown plant-hopper and the rice striped stem-borer obtain mutual benefits when feeding on the same rice plant.

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          Most cited references78

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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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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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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

                Contributors
                liyunhe@caas.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                19 November 2021
                19 November 2021
                2021
                : 12
                : 6772
                Affiliations
                [1 ]GRID grid.410727.7, ISNI 0000 0001 0526 1937, State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, , Chinese Academy of Agricultural Sciences, ; 100193 Beijing, China
                [2 ]GRID grid.463053.7, ISNI 0000 0000 9655 6126, College of Life Sciences, , Xinyang Normal University, ; 464000 Xinyang, China
                [3 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Institute of Insect Sciences, , Zhejiang University, ; 310058 Hangzhou, China
                [4 ]GRID grid.10711.36, ISNI 0000 0001 2297 7718, Laboratory of Fundamental and Applied Research in Chemical Ecology, , University of Neuchâtel, ; 2000 Neuchâtel, Switzerland
                Author information
                http://orcid.org/0000-0003-1804-7731
                http://orcid.org/0000-0003-4937-8867
                http://orcid.org/0000-0002-3262-6134
                http://orcid.org/0000-0002-8315-785X
                http://orcid.org/0000-0003-0780-3327
                Article
                27021
                10.1038/s41467-021-27021-0
                8604950
                34799588
                56ff2537-9068-49b9-97fd-cfad2fd3c70e
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 February 2021
                : 26 October 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 32120103009
                Award ID: 31972984
                Award Recipient :
                Categories
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                Custom metadata
                © The Author(s) 2021

                Uncategorized
                agroecology,behavioural ecology,evolutionary ecology,entomology
                Uncategorized
                agroecology, behavioural ecology, evolutionary ecology, entomology

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