Literature DB >> 23821440

Genome-scale mapping of DNase I hypersensitivity.

Sam John1, Peter J Sabo, Theresa K Canfield, Kristen Lee, Shinny Vong, Molly Weaver, Hao Wang, Jeff Vierstra, Alex P Reynolds, Robert E Thurman, John A Stamatoyannopoulos.   

Abstract

DNase I-seq is a global and high-resolution method that uses the nonspecific endonuclease DNase I to map chromatin accessibility. These accessible regions, designated as DNase I hypersensitive sites (DHSs), define the regulatory features, (e.g., promoters, enhancers, insulators, and locus control regions) of complex genomes. In this unit, methods are described for nuclei isolation, digestion of nuclei with limiting concentrations of DNase I, and the biochemical fractionation of DNase I hypersensitive sites in preparation for high-throughput sequencing. DNase I-seq is an unbiased and robust method that is not predicated on an a priori understanding of regulatory patterns or chromatin features.
© 2013 by John Wiley & Sons, Inc.

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Year:  2013        PMID: 23821440      PMCID: PMC4405172          DOI: 10.1002/0471142727.mb2127s103

Source DB:  PubMed          Journal:  Curr Protoc Mol Biol        ISSN: 1934-3647


  24 in total

1.  Discovery of functional noncoding elements by digital analysis of chromatin structure.

Authors:  Peter J Sabo; Michael Hawrylycz; James C Wallace; Richard Humbert; Man Yu; Anthony Shafer; Janelle Kawamoto; Robert Hall; Joshua Mack; Michael O Dorschner; Michael McArthur; John A Stamatoyannopoulos
Journal:  Proc Natl Acad Sci U S A       Date:  2004-11-18       Impact factor: 11.205

2.  Genome-scale mapping of DNase I sensitivity in vivo using tiling DNA microarrays.

Authors:  Peter J Sabo; Michael S Kuehn; Robert Thurman; Brett E Johnson; Ericka M Johnson; Hua Cao; Man Yu; Elizabeth Rosenzweig; Jeff Goldy; Andrew Haydock; Molly Weaver; Anthony Shafer; Kristin Lee; Fidencio Neri; Richard Humbert; Michael A Singer; Todd A Richmond; Michael O Dorschner; Michael McArthur; Michael Hawrylycz; Roland D Green; Patrick A Navas; William S Noble; John A Stamatoyannopoulos
Journal:  Nat Methods       Date:  2006-07       Impact factor: 28.547

3.  High-resolution mapping and characterization of open chromatin across the genome.

Authors:  Alan P Boyle; Sean Davis; Hennady P Shulha; Paul Meltzer; Elliott H Margulies; Zhiping Weng; Terrence S Furey; Gregory E Crawford
Journal:  Cell       Date:  2008-01-25       Impact factor: 41.582

Review 4.  Chromatin structure and gene activity.

Authors:  S C Elgin
Journal:  Curr Opin Cell Biol       Date:  1990-06       Impact factor: 8.382

5.  Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities.

Authors:  Sven Heinz; Christopher Benner; Nathanael Spann; Eric Bertolino; Yin C Lin; Peter Laslo; Jason X Cheng; Cornelis Murre; Harinder Singh; Christopher K Glass
Journal:  Mol Cell       Date:  2010-05-28       Impact factor: 17.970

6.  PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls.

Authors:  Joel Rozowsky; Ghia Euskirchen; Raymond K Auerbach; Zhengdong D Zhang; Theodore Gibson; Robert Bjornson; Nicholas Carriero; Michael Snyder; Mark B Gerstein
Journal:  Nat Biotechnol       Date:  2009-01-04       Impact factor: 54.908

7.  Chromatin accessibility pre-determines glucocorticoid receptor binding patterns.

Authors:  Sam John; Peter J Sabo; Robert E Thurman; Myong-Hee Sung; Simon C Biddie; Thomas A Johnson; Gordon L Hager; John A Stamatoyannopoulos
Journal:  Nat Genet       Date:  2011-01-23       Impact factor: 38.330

8.  Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data.

Authors:  Anton Valouev; David S Johnson; Andreas Sundquist; Catherine Medina; Elizabeth Anton; Serafim Batzoglou; Richard M Myers; Arend Sidow
Journal:  Nat Methods       Date:  2008-09       Impact factor: 28.547

9.  Genome-wide identification of in vivo protein-DNA binding sites from ChIP-Seq data.

Authors:  Raja Jothi; Suresh Cuddapah; Artem Barski; Kairong Cui; Keji Zhao
Journal:  Nucleic Acids Res       Date:  2008-08-06       Impact factor: 16.971

10.  Model-based analysis of ChIP-Seq (MACS).

Authors:  Yong Zhang; Tao Liu; Clifford A Meyer; Jérôme Eeckhoute; David S Johnson; Bradley E Bernstein; Chad Nusbaum; Richard M Myers; Myles Brown; Wei Li; X Shirley Liu
Journal:  Genome Biol       Date:  2008-09-17       Impact factor: 13.583

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  42 in total

1.  Competition between DNA methylation and transcription factors determines binding of NRF1.

Authors:  Silvia Domcke; Anaïs Flore Bardet; Paul Adrian Ginno; Dominik Hartl; Lukas Burger; Dirk Schübeler
Journal:  Nature       Date:  2015-12-16       Impact factor: 49.962

2.  Single-cell whole-genome analyses by Linear Amplification via Transposon Insertion (LIANTI).

Authors:  Chongyi Chen; Dong Xing; Longzhi Tan; Heng Li; Guangyu Zhou; Lei Huang; X Sunney Xie
Journal:  Science       Date:  2017-04-14       Impact factor: 47.728

3.  Functionally and phenotypically distinct subpopulations of marrow stromal cells are fibroblast in origin and induce different fates in peripheral blood monocytes.

Authors:  Mineo Iwata; Richard S Sandstrom; Jeffrey J Delrow; John A Stamatoyannopoulos; Beverly Torok-Storb
Journal:  Stem Cells Dev       Date:  2013-11-23       Impact factor: 3.272

4.  Inference of cell type specific regulatory networks on mammalian lineages.

Authors:  Deborah Chasman; Sushmita Roy
Journal:  Curr Opin Syst Biol       Date:  2017-04-17

5.  Cardiac gene expression data and in silico analysis provide novel insights into human and mouse taste receptor gene regulation.

Authors:  Simon R Foster; Enzo R Porrello; Maurizio Stefani; Nicola J Smith; Peter Molenaar; Cristobal G dos Remedios; Walter G Thomas; Mirana Ramialison
Journal:  Naunyn Schmiedebergs Arch Pharmacol       Date:  2015-05-20       Impact factor: 3.000

6.  FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data.

Authors:  Daniel Quang; Xiaohui Xie
Journal:  Methods       Date:  2019-03-26       Impact factor: 3.608

7.  Integrated Functional Genomic Analysis Enables Annotation of Kidney Genome-Wide Association Study Loci.

Authors:  Karsten B Sieber; Anna Batorsky; Kyle Siebenthall; Kelly L Hudkins; Jeff D Vierstra; Shawn Sullivan; Aakash Sur; Michelle McNulty; Richard Sandstrom; Alex Reynolds; Daniel Bates; Morgan Diegel; Douglass Dunn; Jemma Nelson; Michael Buckley; Rajinder Kaul; Matthew G Sampson; Jonathan Himmelfarb; Charles E Alpers; Dawn Waterworth; Shreeram Akilesh
Journal:  J Am Soc Nephrol       Date:  2019-02-13       Impact factor: 10.121

Review 8.  Disruption of long-range gene regulation in human genetic disease: a kaleidoscope of general principles, diverse mechanisms and unique phenotypic consequences.

Authors:  Shipra Bhatia; Dirk A Kleinjan
Journal:  Hum Genet       Date:  2014-02-05       Impact factor: 4.132

9.  Sequencing the AML genome, transcriptome, and epigenome.

Authors:  Elaine R Mardis
Journal:  Semin Hematol       Date:  2014-08-07       Impact factor: 3.851

Review 10.  Next generation sequencing technology and genomewide data analysis: Perspectives for retinal research.

Authors:  Vijender Chaitankar; Gökhan Karakülah; Rinki Ratnapriya; Felipe O Giuste; Matthew J Brooks; Anand Swaroop
Journal:  Prog Retin Eye Res       Date:  2016-06-11       Impact factor: 21.198

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