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      Genome and transcriptome analysis of the beet armyworm Spodoptera exigua reveals targets for pest control

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          Abstract

          The genus Spodoptera (Lepidoptera: Noctuidae) includes some of the most infamous insect pests of cultivated plants including Spodoptera frugiperda, Spodoptera litura, and Spodoptera exigua. To effectively develop targeted pest control strategies for diverse Spodoptera species, genomic resources are highly desired. To this aim, we provide the genome assembly and developmental transcriptome comprising all major life stages of S. exigua, the beet armyworm. Spodoptera exigua is a polyphagous herbivore that can feed on > 130 host plants, including several economically important crops. The 419 Mb beet armyworm genome was sequenced from a female S. exigua pupa. Using a hybrid genome sequencing approach (Nanopore long-read data and Illumina short read), a high-quality genome assembly was achieved (N50 = 1.1 Mb). An official gene set (18,477 transcripts) was generated by automatic annotation and by using transcriptomic RNA-seq datasets of 18 S. exigua samples as supporting evidence. In-depth analyses of developmental stage-specific expression combined with gene tree analyses of identified homologous genes across Lepidoptera genomes revealed four potential genes of interest (three of them Spodoptera-specific) upregulated during first- and third-instar larval stages for targeted pest-outbreak management. The beet armyworm genome sequence and developmental transcriptome covering all major developmental stages provide critical insights into the biology of this devastating polyphagous insect pest species worldwide. In addition, comparative genomic analyses across Lepidoptera significantly advance our knowledge to further control other invasive Spodoptera species and reveals potential lineage-specific target genes for pest control strategies.

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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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              Fast gapped-read alignment with Bowtie 2.

              As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                G3 (Bethesda)
                Genetics
                g3journal
                G3: Genes|Genomes|Genetics
                Oxford University Press
                2160-1836
                November 2021
                02 September 2021
                02 September 2021
                : 11
                : 11
                : jkab311
                Affiliations
                [1 ] Biosystematics Group, Wageningen University & Research , 6708 PB Wageningen, The Netherlands
                [2 ] Future Genomics Technologies , Leiden, The Netherlands
                [3 ] Laboratory of Virology, Wageningen University & Research , 6708 PB Wageningen, The Netherlands
                Author notes
                Corresponding authors: Biosystematics Group, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands. Email: sabrina.simon@ 123456wur.nl (S.S.) and Laboratory of Virology, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands. Email: vera.ros@ 123456wur.nl (V.I.D.R.)

                Sabrina Simo, Thijmen Breeschoten and Vera I D Ros authors contributed equally to this work.

                Article
                jkab311
                10.1093/g3journal/jkab311
                8527508
                34557910
                147d59fd-fb45-44da-bb2a-34656344f577
                © The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 May 2021
                : 19 August 2021
                : 14 October 2021
                Page count
                Pages: 14
                Funding
                Funded by: Netherlands Organization for Health Research and Development;
                Award ID: 40-43500-98-4064
                Funded by: Dutch Research Council, DOI 10.13039/501100003246;
                Award ID: VI.Vidi.192.041
                Categories
                Genome Report
                AcademicSubjects/SCI01180
                AcademicSubjects/SCI01140
                AcademicSubjects/SCI00010
                AcademicSubjects/SCI00960

                Genetics
                beet armyworm,spodoptera exigua,whole genome,transcriptomics,gene expression,pest control
                Genetics
                beet armyworm, spodoptera exigua, whole genome, transcriptomics, gene expression, pest control

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