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      Maternal H3K27me3 controls DNA methylation-independent genomic imprinting

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          Abstract

          Mammalian sperm and oocytes have different epigenetic landscapes and are organized in different fashion. Following fertilization, the initially distinct parental epigenomes become largely equalized with the exception of certain loci including imprinting control regions (ICRs). How parental chromatin becomes equalized and how ICRs escape from this reprogramming is largely unknown. Here we profiled parental allele-specific DNase I hypersensitive sites (DHSs) in mouse zygotes and morula embryos, and investigated the epigenetic mechanisms underlying allelic DHSs. Integrated analyses of DNA methylome and H3K27me3 ChIP-seq data sets revealed 76 genes with paternal allele-specific DHSs that are devoid of DNA methylation but harbor maternal allele-specific H3K27me3. Interestingly, these genes are paternally expressed in preimplantation embryos, and ectopic removal of H3K27me3 induces maternal allele expression. H3K27me3-dependent imprinting is largely lost in the embryonic cell lineage, but at least 5 genes maintain their imprinting in the extra-embryonic cell lineage. The 5 genes include all previously identified DNA methylation-independent imprinted autosomal genes. Thus, our study identifies maternal H3K27me3 as a DNA methylation-independent imprinting mechanism.

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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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            featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

            Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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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.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                14 November 2022
                27 July 2017
                19 July 2017
                18 November 2022
                : 547
                : 7664
                : 419-424
                Affiliations
                [1 ]Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
                [2 ]Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
                [3 ]Division of Hematology/Oncology, Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
                [4 ]Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
                [5 ]Harvard Stem Cell Institute, WAB-149G, 200 Longwood Avenue, Boston, Massachusetts 02115, USA
                Author notes
                [# ]To whom correspondence should be addressed: yzhang@ 123456genetics.med.harvard.edu
                [*]

                These authors contributed equally to this work

                AUTHOR CONTRIBUTIONS

                A.I. and Y.Z. conceived the project and designed the experiments. A.I. performed the experiments; L.J. analyzed sequencing datasets. F.L. performed liDNase-seq and RNA-seq; T.S. assisted in embryo manipulation experiments; A.I., L.J., and Y.Z. interpreted the data. A.I. and Y.Z. wrote the manuscript.

                Article
                HHMIMS1847486
                10.1038/nature23262
                9674007
                28723896
                75a58eda-c5bf-4b76-bb7f-fc8ed588fe89

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

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                allelic chromatin accessibility,allelic gene expression,germline dna methylation,allelic h3k27me3,h3k27me3-dependent genomic imprinting,mouse early development

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