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      Museum genomics provide insight into the extinction of a specialist North American warbler species

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          Abstract

          Museum genomics provide an opportunity to investigate population demographics of extinct species, especially valuable when research prior to extinction was minimal. The Bachman’s warbler ( Vermivora bachmanii) is hypothesized to have gone extinct due to loss of its specialized habitat. However, little is known about other potential contributing factors such as natural rarity or changes to connectivity following habitat fragmentation. We examined mitochondrial DNA (mtDNA) and genome-wide SNPs using specimens collected from breeding and migration sites across the range of the Bachman’s warbler. We found no signals of strong population structuring across the breeding range of Bachman’s warblers in both mtDNA and genome-wide SNPs. Thus, long-term population isolation did not appear to be a significant contributor to the extinction of the Bachman’s warbler. Instead, our findings support the theory that Bachman’s warblers underwent a rapid decline likely driven by habitat destruction, which may have been exacerbated by the natural rarity, habitat specificity and low genetic diversity of the species.

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          Most cited references54

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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 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
                byerlyp@si.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                24 July 2024
                24 July 2024
                2024
                : 14
                : 17047
                Affiliations
                [1 ]Center for Conservation Genomics, Smithsonian’s National Zoo and Conservation Biology Institute, ( https://ror.org/04gktak93) Washington, DC 20008 USA
                [2 ]Australian National Wildlife Collection, CSIRO National Research Collections Australia, ( https://ror.org/059mabc80) Canberra, Australia
                [3 ]Department of Biosciences, Durham University, ( https://ror.org/01v29qb04) South Road, Durham, UK
                [4 ]Department of Biology and McCourt School of Public Policy, Georgetown University, ( https://ror.org/05vzafd60) 37th and O Streets NW, Washington, DC 20057 USA
                [5 ]Department of Forest and Conservation Sciences, University of British Columbia, ( https://ror.org/03rmrcq20) Vancouver, BC V6T 1Z4 Canada
                Author information
                http://orcid.org/0000-0003-0461-6462
                http://orcid.org/0000-0002-2792-7055
                Article
                67595
                10.1038/s41598-024-67595-5
                11269716
                39048633
                9300d208-3a2f-483b-ac26-cf09d96f507d
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

                History
                : 8 November 2023
                : 12 July 2024
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                © Springer Nature Limited 2024

                Uncategorized
                conservation biology,conservation genomics
                Uncategorized
                conservation biology, conservation genomics

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