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      The INO80 Complex Regulates Epigenetic Inheritance of Heterochromatin

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          SUMMARY

          One key aspect of epigenetic inheritance is that chromatin structures can be stably inherited through generations after the removal of the signals that establish such structures. In fission yeast, the RNA interference (RNAi) pathway is critical for the targeting of histone methyltransferase Clr4 to pericentric repeats to establish heterochromatin. However, pericentric heterochromatin cannot be properly inherited in the absence of RNAi, suggesting the existence of mechanisms that counteract chromatin structure inheritance. Here, we show that mutations of components of the INO80 chromatin-remodeling complex allow pericentric heterochromatin inheritance in RNAi mutants. The ability of INO80 to counter heterochromatin inheritance is attributed to one subunit, Iec5, which promotes histone turnover at heterochromatin but has little effects on nucleosome positioning at heterochromatin, gene expression, or the DNA damage response. These analyses demonstrate the importance of the INO80 chromatin-remodeling complex in controlling heterochromatin inheritance and maintaining the proper heterochromatin landscape of the genome.

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          In Brief

          Shan et al. report that the INO80 chromatin-remodeling complex regulates heterochromatin inheritance in wild-type cells and RNAi mutants. Further characterization of the INO80 complex demonstrates that the Iec5 subunit counteracts heterochromatin inheritance through regulating histone turnover but not nucleosome positioning at heterochromatin during DNA replication.

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          Most cited references68

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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              HISAT: a fast spliced aligner with low memory requirements.

              HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                7 January 2021
                29 December 2020
                20 February 2021
                : 33
                : 13
                : 108561
                Affiliations
                [1 ]Department of Biological Sciences, Columbia University, New York, NY 10027, USA
                [2 ]Department of Chemical Physiology, The Scripps Research Institute, La Jolla, CA 92037, USA
                [3 ]Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA
                [4 ]Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
                [5 ]Lead Contact
                Author notes

                AUTHOR CONTRIBUTIONS

                C.-M.S., K.B., and S.J. designed and performed the experiments; J.D. and J.R.Y. performed mass spectrometry analyses; and X.C. and C.L. analyzed deep-sequencing data. S.J. and C.-M.S. wrote the manuscript with input from all authors.

                Article
                NIHMS1658625
                10.1016/j.celrep.2020.108561
                7896557
                33378674
                bbd18f48-5fa6-4271-9613-ac483baa2a3e

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Cell biology
                Cell biology

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