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      ATM and PRDM9 regulate SPO11-bound recombination intermediates during meiosis

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          Abstract

          Meiotic recombination is initiated by SPO11-induced double-strand breaks (DSBs). In most mammals, the methyltransferase PRDM9 guides SPO11 targeting, and the ATM kinase controls meiotic DSB numbers. Following MRE11 nuclease removal of SPO11, the DSB is resected and loaded with DMC1 filaments for homolog invasion. Here, we demonstrate the direct detection of meiotic DSBs and resection using END-seq on mouse spermatocytes with low sample input. We find that DMC1 limits both minimum and maximum resection lengths, whereas 53BP1, BRCA1 and EXO1 play surprisingly minimal roles. Through enzymatic modifications to END-seq, we identify a SPO11-bound meiotic recombination intermediate (SPO11-RI) present at all hotspots. We propose that SPO11-RI forms because chromatin-bound PRDM9 asymmetrically blocks MRE11 from releasing SPO11. In Atm –/– spermatocytes, trapped SPO11 cleavage complexes accumulate due to defective MRE11 initiation of resection. Thus, in addition to governing SPO11 breakage, ATM and PRDM9 are critical local regulators of mammalian SPO11 processing.

          Abstract

          Recombination requires DNA break formation by SPO11, following which SPO11 is thought to be released. Here, the authors show that meiotic hotspots retain SPO11 through a recombination intermediate dependent on the methyltransferase PRDM9, and that the ATM kinase governs the release of SPO11.

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          A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

          Heng Li (2011)
          Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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            BigWig and BigBed: enabling browsing of large distributed datasets

            Summary: BigWig and BigBed files are compressed binary indexed files containing data at several resolutions that allow the high-performance display of next-generation sequencing experiment results in the UCSC Genome Browser. The visualization is implemented using a multi-layered software approach that takes advantage of specific capabilities of web-based protocols and Linux and UNIX operating systems files, R trees and various indexing and compression tricks. As a result, only the data needed to support the current browser view is transmitted rather than the entire file, enabling fast remote access to large distributed data sets. Availability and implementation: Binaries for the BigWig and BigBed creation and parsing utilities may be downloaded at http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/. Source code for the creation and visualization software is freely available for non-commercial use at http://hgdownload.cse.ucsc.edu/admin/jksrc.zip, implemented in C and supported on Linux. The UCSC Genome Browser is available at http://genome.ucsc.edu Contact: ann@soe.ucsc.edu Supplementary information: Supplementary byte-level details of the BigWig and BigBed file formats are available at Bioinformatics online. For an in-depth description of UCSC data file formats and custom tracks, see http://genome.ucsc.edu/FAQ/FAQformat.html and http://genome.ucsc.edu/goldenPath/help/hgTracksHelp.html
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              Design and analysis of ChIP-seq experiments for DNA-binding proteins

              Recent progress in massively parallel sequencing platforms has allowed for genome-wide measurements of DNA-associated proteins using a combination of chromatin immunoprecipitation and sequencing (ChIP-seq). While a variety of methods exist for analysis of the established microarray alternative (ChIP-chip), few approaches have been described for processing ChIP-seq data. To fill this gap, we propose an analysis pipeline specifically designed to detect protein binding positions with high accuracy. Using three separate datasets, we illustrate new methods for improving tag alignment and correcting for background signals. We also compare sensitivity and spatial precision of several novel and previously described binding detection algorithms. Finally, we analyze the relationship between the depth of sequencing and characteristics of the detected binding positions, and provide a method for estimating the sequencing depth necessary for a desired coverage of protein binding sites.
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                Author and article information

                Contributors
                andre_nussenzweig@nih.gov
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                12 February 2020
                12 February 2020
                2020
                : 11
                : 857
                Affiliations
                [1 ]ISNI 0000 0001 2237 2479, GRID grid.420086.8, Laboratory of Genome Integrity, National Cancer Institute, NIH, ; Bethesda, MD USA
                [2 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Immunology Graduate Group, , University of Pennsylvania, ; Philadelphia, PA USA
                [3 ]ISNI 0000 0001 2171 9952, GRID grid.51462.34, Molecular Biology Program, Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [4 ]ISNI 0000 0004 0372 2033, GRID grid.258799.8, Department of Radiation Genetics, Graduate School of Medicine, , Kyoto University, ; Kyoto, 606-8501 Japan
                [5 ]ISNI 0000 0004 1936 9510, GRID grid.253615.6, Institute for Biomedical Sciences, George Washington University, ; Washington, DC USA
                [6 ]ISNI 0000 0004 1936 9924, GRID grid.89336.37, The Howard Hughes Medical Institute and The University of Texas at Austin, ; Austin, TX 78712 USA
                [7 ]ISNI 0000 0004 1936 9924, GRID grid.89336.37, The Department of Molecular Biosciences, , The University of Texas at Austin, ; Austin, TX 78712 USA
                Article
                14654
                10.1038/s41467-020-14654-w
                7016097
                32051414
                c53dc61f-9e6e-44d9-b7c2-dc550668db25
                © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020

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

                History
                : 24 December 2019
                : 23 January 2020
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                © The Author(s) 2020

                Uncategorized
                dna damage and repair,dna recombination
                Uncategorized
                dna damage and repair, dna recombination

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