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      Genomic and transcriptomic analysis unveils population evolution and development of pesticide resistance in fall armyworm Spodoptera frugiperda

      research-article
      1 , 12 , 2 , 9 , 1 , 3 , 15 , 1 , 12 , 2 , 2 , 9 , 2 , 2 , 12 , 5 , 16 , 4 , 2 , 1 , 7 , 1 , 5 , 6 , 8 , 10 , 8 , 10 , 2 , 1 , 18 , 18 , 17 , 17 , 19 , 19 , 19 , 20 , 20 , 21 , 20 , 20 , 22 , 5 , 23 , 23 , 13 , 10 , 11 , 14 , , 2 , 10 , , 1 , 12 , , 3 , 4 , 15 ,
      Protein & Cell
      Higher Education Press
      Spodoptera frugiperda, chromosome-level genome, population differentiation, cytochrome p450, pesticides

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          Abstract

          The fall armyworm (FAW), Spodoptera frugiperda, is a destructive pest native to America and has recently become an invasive insect pest in China. Because of its rapid spread and great risks in China, understanding of FAW genetic background and pesticide resistance is urgent and essential to develop effective management strategies. Here, we assembled a chromosome-level genome of a male FAW (SFynMstLFR) and compared re-sequencing results of the populations from America, Africa, and China. Strain identification of 163 individuals collected from America, Africa and China showed that both C and R strains were found in the American populations, while only C strain was found in the Chinese and African populations. Moreover, population genomics analysis showed that populations from Africa and China have close relationship with significantly genetic differentiation from American populations. Taken together, FAWs invaded into China were most likely originated from Africa. Comparative genomics analysis displayed that the cytochrome p450 gene family is extremely expanded to 425 members in FAW, of which 283 genes are specific to FAW. Treatments of Chinese populations with twenty-three pesticides showed the variant patterns of transcriptome profiles, and several detoxification genes such as AOX, UGT and GST specially responded to the pesticides. These findings will be useful in developing effective strategies for management of FAW in China and other invaded areas.

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          The online version of this article (10.1007/s13238-020-00795-7) contains supplementary material, which is available to authorized users.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

              Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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                Author and article information

                Contributors
                FHuang@agcenter.lsu.edu
                liuxin@genomics.cn
                shengjunpuer@163.com
                lkang@ioz.ac.cn
                Journal
                Protein Cell
                Protein Cell
                Protein & Cell
                Higher Education Press (Beijing )
                1674-800X
                1674-8018
                27 October 2020
                27 October 2020
                July 2022
                : 13
                : 7
                : 513-531
                Affiliations
                [1 ]GRID grid.410696.c, ISNI 0000 0004 1761 2898, State Key Laboratory for Conservation and Utilization of Bioresources in Yunnan, , Yunnan Agricultural University, ; Kunming, 650201 China
                [2 ]GRID grid.21155.32, ISNI 0000 0001 2034 1839, State Key Laboratory of Agricultural Genomics, , BGI-Shenzhen, ; Shenzhen, 518083 China
                [3 ]GRID grid.9227.e, ISNI 0000000119573309, State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, , Chinese Academy of Sciences, ; Beijing, 100101 China
                [4 ]GRID grid.9227.e, ISNI 0000000119573309, Beijing Institutes of Life Science, , Chinese Academy of Sciences, ; Beijing, 100101 China
                [5 ]GRID grid.21155.32, ISNI 0000 0001 2034 1839, MGI, , BGI-Shenzhen, ; Shenzhen, 518083 China
                [6 ]GRID grid.21155.32, ISNI 0000 0001 2034 1839, BGI-Qingdao, , BGI-Shenzhen, ; Qingdao, 266555 China
                [7 ]BGI-Yunnan, No. 389 Haiyuan Road, High-tech Development Zone, Kunming, 650106 China
                [8 ]GRID grid.507779.b, ISNI 0000 0004 4910 5858, China National GeneBank, ; Jinsha Road, Dapeng New District, Shenzhen, 518120 China
                [9 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Department of Biology, , University of Copenhagen, ; 2100 Copenhagen, Denmark
                [10 ]GRID grid.21155.32, ISNI 0000 0001 2034 1839, Guangdong Provincial Key Laboratory of Genome Read and Write, , BGI-Shenzhen, ; Shenzhen, 518120 China
                [11 ]GRID grid.21155.32, ISNI 0000 0001 2034 1839, Guangdong Provincial Academician Workstation of BGI Synthetic Genomics, , BGI-Shenzhen, ; Shenzhen, 518120 China
                [12 ]Yunnan Plateau Characteristic Agriculture Industry Research Institute, Kunming, 650201 China
                [13 ]GRID grid.412608.9, ISNI 0000 0000 9526 6338, College of Plant Health and Medicine, , Qingdao Agricultural University, ; Qingdao, 266109 China
                [14 ]GRID grid.250060.1, ISNI 0000 0000 9070 1054, Department of Entomology, , Louisiana State University Agricultural Center, ; Baton Rouge, LA 70803 USA
                [15 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, CAS Center for Excellence in Biotic Interactions, , University of Chinese Academy of Sciences, ; Beijing, 100101 China
                [16 ]Yunnan Plant Protection and Quarantine Station, Kunming, 650034 China
                [17 ]GRID grid.20561.30, ISNI 0000 0000 9546 5767, State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, , South China Agricultural University, ; Guangzhou, 510642 China
                [18 ]BGI-Americas, One Broadway, 14th Floor, Cambridge, MA 02142 USA
                [19 ]GRID grid.49697.35, ISNI 0000 0001 2107 2298, Department of Biochemistry, Genetics and Microbiology, Forestry and Agricultural Biotechnology Institute, , University of Pretoria, ; Pretoria, South Africa
                [20 ]Ethiopian Biotechnology Institute, Addis Ababa, Ethiopia
                [21 ]GRID grid.7123.7, ISNI 0000 0001 1250 5688, College of Natural Science, , Addis Ababa University, ; Addis Ababa, Ethiopia
                [22 ]GRID grid.463251.7, ISNI 0000 0001 2195 6683, Melkassa Agricultural Research Center, , Ethiopian Institute of Agricultural Research, ; Melkassa, Addis Ababa, Ethiopia
                [23 ]GRID grid.473294.f, Kenya Agricultural and Livestock Research Organization, ; P.O. Box 57811, Nairobi, 00800 Kenya
                Author information
                http://orcid.org/0000-0002-8601-7334
                http://orcid.org/0000-0002-6934-0439
                http://orcid.org/0000-0002-7613-9725
                http://orcid.org/0000-0001-9081-9754
                http://orcid.org/0000-0003-3909-0931
                http://orcid.org/0000-0001-5496-8357
                http://orcid.org/0000-0002-4742-9870
                http://orcid.org/0000-0003-0956-1958
                http://orcid.org/0000-0003-3256-2940
                http://orcid.org/0000-0002-8433-1864
                http://orcid.org/0000-0003-4262-2329
                Article
                795
                10.1007/s13238-020-00795-7
                9226219
                33108584
                8fec8a1d-ea70-490f-96a2-ffe0f0a6d99a
                © The Author(s) 2020

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 June 2020
                : 15 September 2020
                Categories
                Research Article
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                © The Author(s) 2022

                spodoptera frugiperda,chromosome-level genome,population differentiation,cytochrome p450,pesticides

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