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      High-quality genome of the zoophytophagous stink bug, Nesidiocoris tenuis, informs their food habit adaptation

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          Abstract

          The zoophytophagous stink bug, Nesidiocoris tenuis, is a promising natural enemy of micro-pests such as whiteflies and thrips. This bug possesses both phytophagous and entomophagous food habits, enabling it to obtain nutrition from both plants and insects. This trait allows us to maintain its population density in agricultural fields by introducing insectary plants, even when the pest prey density is extremely low. However, if the bugs’ population becomes too dense, they can sometimes damage crop plants. This dual character seems to arise from the food preferences and chemosensation of this predator. To understand the genomic landscape of N. tenuis, we examined the whole genome sequence of a commercially available Japanese strain. We used long-read sequencing and Hi-C analysis to assemble the genome at the chromosomal level. We then conducted a comparative analysis of the genome with previously reported genomes of phytophagous and hematophagous stink bugs to focus on the genetic factors contributing to this species’ herbivorous and carnivorous tendencies. Our findings suggest that the gustatory gene set plays a pivotal role in adapting to food habits, making it a promising target for selective breeding. Furthermore, we identified the whole genomes of microorganisms symbiotic with this species through genomic analysis. We believe that our results shed light on the food habit adaptations of N. tenuis and will accelerate breeding efforts based on new breeding techniques for natural enemy insects, including genomics and genome editing.

          Most cited references84

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          fastp: an ultra-fast all-in-one FASTQ preprocessor

          Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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            IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

            Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.
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              trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses

              Summary: Multiple sequence alignments are central to many areas of bioinformatics. It has been shown that the removal of poorly aligned regions from an alignment increases the quality of subsequent analyses. Such an alignment trimming phase is complicated in large-scale phylogenetic analyses that deal with thousands of alignments. Here, we present trimAl, a tool for automated alignment trimming, which is especially suited for large-scale phylogenetic analyses. trimAl can consider several parameters, alone or in multiple combinations, for selecting the most reliable positions in the alignment. These include the proportion of sequences with a gap, the level of amino acid similarity and, if several alignments for the same set of sequences are provided, the level of consistency across different alignments. Moreover, trimAl can automatically select the parameters to be used in each specific alignment so that the signal-to-noise ratio is optimized. Availability: trimAl has been written in C++, it is portable to all platforms. trimAl is freely available for download (http://trimal.cgenomics.org) and can be used online through the Phylemon web server (http://phylemon2.bioinfo.cipf.es/). Supplementary Material is available at http://trimal.cgenomics.org/publications. Contact: tgabaldon@crg.es
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                Author and article information

                Contributors
                Role: Editor
                Journal
                G3 (Bethesda)
                Genetics
                g3journal
                G3: Genes|Genomes|Genetics
                Oxford University Press (US )
                2160-1836
                February 2024
                19 December 2023
                19 December 2023
                : 14
                : 2
                : jkad289
                Affiliations
                Division of Insect Advanced Technology, Institute of Agrobiological Sciences, NARO , Tsukuba, Ibaraki 305-8634, Japan
                Graduate School of Agricultural and Life Sciences, The University of Tokyo , Tokyo 113-8657, Japan
                Graduate School of Agricultural and Life Sciences, The University of Tokyo , Tokyo 113-8657, Japan
                Division of Insect Advanced Technology, Institute of Agrobiological Sciences, NARO , Tsukuba, Ibaraki 305-8634, Japan
                Division of Insect Advanced Technology, Institute of Agrobiological Sciences, NARO , Tsukuba, Ibaraki 305-8634, Japan
                Division of Insect Advanced Technology, Institute of Agrobiological Sciences, NARO , Tsukuba, Ibaraki 305-8634, Japan
                Division of Insect Advanced Technology, Institute of Agrobiological Sciences, NARO , Tsukuba, Ibaraki 305-8634, Japan
                Author notes
                Corresponding author: Division of Insect Advanced Technology, Institute of Agrobiological Sciences, NARO, Tsukuba, Ibaraki 305-8634, Japan. Email: tue@ 123456affrc.go.jp

                Conflicts of interest The authors declare no conflicts of interest.

                Author information
                https://orcid.org/0009-0000-2830-6472
                https://orcid.org/0000-0002-8447-9295
                https://orcid.org/0000-0001-0000-0000
                https://orcid.org/0000-0001-9912-3489
                https://orcid.org/0000-0003-0785-8694
                https://orcid.org/0000-0002-4871-0197
                Article
                jkad289
                10.1093/g3journal/jkad289
                10849345
                38113473
                0a255235-a76e-4076-9416-0b0f59de3d2f
                © The Author(s) 2023. Published by Oxford University Press on behalf of The 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
                : 21 September 2023
                : 05 December 2023
                : 08 January 2024
                Page count
                Pages: 12
                Funding
                Funded by: JSPS KAKENHI;
                Award ID: #20H02992
                Funded by: JSPS Fostering Joint International Research;
                Award ID: #21KK0273
                Funded by: Cabinet Office, Government of Japan, DOI 10.13039/501100002770;
                Award ID: #JPJ009237
                Categories
                Genome Report
                AcademicSubjects/SCI01180
                AcademicSubjects/SCI01140

                Genetics
                natural enemy,zoophytophagous,ipm,food habit
                Genetics
                natural enemy, zoophytophagous, ipm, food habit

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