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      A chromosome-scale genome assembly and evaluation of mtDNA variation in the willow leaf beetle Chrysomela aeneicollis

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          Abstract

          The leaf beetle Chrysomela aeneicollis has a broad geographic range across Western North America but is restricted to cool habitats at high elevations along the west coast. Central California populations occur only at high altitudes (2,700–3,500 m) where they are limited by reduced oxygen supply and recent drought conditions that are associated with climate change. Here, we report a chromosome-scale genome assembly alongside a complete mitochondrial genome and characterize differences among mitochondrial genomes along a latitudinal gradient over which beetles show substantial population structure and adaptation to fluctuating temperatures. Our scaffolded genome assembly consists of 21 linkage groups; one of which we identified as the X chromosome based on female/male whole genome sequencing coverage and orthology with Tribolium castaneum. We identified repetitive sequences in the genome and found them to be broadly distributed across all linkage groups. Using a reference transcriptome, we annotated a total of 12,586 protein-coding genes. We also describe differences in putative secondary structures of mitochondrial RNA molecules, which may generate functional differences important in adaptation to harsh abiotic conditions. We document substitutions at mitochondrial tRNA molecules and substitutions and insertions in the 16S rRNA region that could affect intermolecular interactions with products from the nuclear genome. This first chromosome-level reference genome will enable genomic research in this important model organism for understanding the biological impacts of climate change on montane insects.

          Most cited references56

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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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            MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

            The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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              BEDTools: a flexible suite of utilities for comparing genomic features

              Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.

                Author and article information

                Contributors
                Role: Editor
                Journal
                G3 (Bethesda)
                Genetics
                g3journal
                G3: Genes|Genomes|Genetics
                Oxford University Press (US )
                2160-1836
                July 2023
                13 May 2023
                13 May 2023
                : 13
                : 7
                : jkad106
                Affiliations
                Department of Integrative Biology, University of California, Berkeley , Berkeley, CA 94720, USA
                Department of Integrative Biology, University of California, Berkeley , Berkeley, CA 94720, USA
                Department of Biology, San Francisco State University , San Francisco, CA 94132, USA
                Department of Biology, Santa Clara University , Santa Clara, CA 95053, USA
                Department of Biology, Sonoma State University , Rohnert Park, CA 94928, USA
                Department of Integrative Biology, University of California, Berkeley , Berkeley, CA 94720, USA
                Department of Integrative Biology, University of California, Berkeley , Berkeley, CA 94720, USA
                Department of Integrative Biology, University of California, Berkeley , Berkeley, CA 94720, USA
                Department of Biology, Sonoma State University , Rohnert Park, CA 94928, USA
                Author notes
                Corresponding author: Department of Biology, Indiana University, 1001 E 3rd St, Bloomington, IN 47405, USA. Email: rbracewe@ 123456iu.edu

                Conflicts of interest statement The author(s) declare no conflict of interest.

                Article
                jkad106
                10.1093/g3journal/jkad106
                10320752
                37178174
                17ee948f-b41c-4887-b10b-6dcdb4d8e545
                © 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
                : 08 March 2023
                : 02 May 2023
                : 30 May 2023
                Page count
                Pages: 9
                Funding
                Funded by: National Science Foundation, DOI 10.13039/100000001;
                Award ID: IOS 1558159
                Award ID: IOS 1457335
                Award ID: IOS 14573
                Funded by: California Conservation Genomics;
                Funded by: XSEDE Allocation;
                Award ID: IBN180012
                Categories
                Genome Report
                AcademicSubjects/SCI01180
                AcademicSubjects/SCI01140

                Genetics
                genome assembly,mitochondria,hi-c
                Genetics
                genome assembly, mitochondria, hi-c

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