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      The expansion of agriculture has shaped the recent evolutionary history of a specialized squash pollinator


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          The conversion of natural to agricultural environments results in a dramatic modification of existing ecological conditions, and there are well-studied examples of crop pests that have rapidly evolved to fill novel agricultural niches. However, the degree to which agricultural intensification influences the evolution of wild insect pollinators is unknown, despite the importance of these mutualists to the global food supply and the persistence of plant populations. This study demonstrates that historical human agriculture in North America has had a profound impact on the recent evolutionary history of a wild, squash-specialized bee that is an essential pollinator of cucurbit crops. This provides a clear example of the role of agriculture as an evolutionary force acting on wild insect pollinators.


          The expansion of agriculture is responsible for the mass conversion of biologically diverse natural environments into managed agroecosystems dominated by a handful of genetically homogeneous crop species. Agricultural ecosystems typically have very different abiotic and ecological conditions from those they replaced and create potential niches for those species that are able to exploit the abundant resources offered by crop plants. While there are well-studied examples of crop pests that have adapted into novel agricultural niches, the impact of agricultural intensification on the evolution of crop mutualists such as pollinators is poorly understood. We combined genealogical inference from genomic data with archaeological records to demonstrate that the Holocene demographic history of a wild specialist pollinator of Cucurbita (pumpkins, squashes, and gourds) has been profoundly impacted by the history of agricultural expansion in North America. Populations of the squash bee Eucera pruinosa experienced rapid growth in areas where agriculture intensified within the past 1,000 y, suggesting that the cultivation of Cucurbita in North America has increased the amount of floral resources available to these bees. In addition, we found that roughly 20% of this bee species’ genome shows signatures of recent selective sweeps. These signatures are overwhelmingly concentrated in populations from eastern North America where squash bees were historically able to colonize novel environments due to human cultivation of Cucurbita pepo and now exclusively inhabit agricultural niches. These results suggest that the widespread cultivation of crops can prompt adaptation in wild pollinators through the distinct ecological conditions imposed by agricultural environments.

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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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            SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.

            The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.
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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.

                Author and article information

                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                3 April 2023
                11 April 2023
                3 April 2023
                : 120
                : 15
                : e2208116120
                [1] aDepartment of Entomology , The Pennsylvania State University , University Park, PA 16802
                [2] bInstitute of Ecology and Evolution , University of Oregon , Eugene, OR 97403
                [3] cBee Research Laboratory, Agricultural Research Service , United States Department of Agriculture , Beltsville, MD 20705
                [4] dDepartment of Biology , Utah State University , Logan, UT 84322
                Author notes
                1To whom correspondence may be addressed. Email: mml64@ 123456psu.edu or natep@ 123456uoregon.edu .

                Edited by Gene Robinson, University of Illinois at Urbana-Champaign Institute for Genomic Biology, Urbana, IL; received May 10, 2022; accepted February 16, 2023

                Author information
                Copyright © 2023 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                : 10 May 2022
                : 16 February 2023
                Page count
                Pages: 10, Words: 8002
                Funded by: National Science Foundation (NSF), FundRef 100000001;
                Award ID: DEB-2046474
                Award Recipient : Margarita M Lopez-Uribe
                Funded by: USDA | National Institute of Food and Agriculture (NIFA), FundRef 100005825;
                Award ID: PEN04716
                Award Recipient : Margarita M Lopez-Uribe
                Funded by: USDA | Agricultural Research Service (ARS), FundRef 100007917;
                Award ID: 0500-00093-001-00-D
                Award Recipient : Anna K Childers Award Recipient : Jay D Evans
                dataset, Dataset
                research-article, Research Article
                evolution, Evolution
                Biological Sciences

                crop cultivation,agricultural adaptation,cucurbita,bees
                crop cultivation, agricultural adaptation, cucurbita, bees


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