Literature DB >> 33219165

A new approach for quantifying epigenetic landscapes.

Wolfgang Fischle1.   

Abstract

ChIP-Seq is a widespread experimental method for determining the global enrichment of chromatin modifications and genome-associated factors. Whereas it is straightforward to compare the relative genomic distribution of these epigenetic features, researchers have also made efforts to compare their signal strength using external references for normalization. New work now suggests that these "spike-ins" could lead to inaccurate conclusions due to intrinsic issues of the methodology and instead calls for new criteria of experimental reporting that may permit internal standardization when certain parameters are fulfilled.
© 2020 Fischle.

Year:  2020        PMID: 33219165      PMCID: PMC7681020          DOI: 10.1074/jbc.H120.016430

Source DB:  PubMed          Journal:  J Biol Chem        ISSN: 0021-9258            Impact factor:   5.157


  10 in total

1.  The Overlooked Fact: Fundamental Need for Spike-In Control for Virtually All Genome-Wide Analyses.

Authors:  Kaifu Chen; Zheng Hu; Zheng Xia; Dongyu Zhao; Wei Li; Jessica K Tyler
Journal:  Mol Cell Biol       Date:  2015-12-28       Impact factor: 4.272

2.  Calibrating ChIP-Seq with Nucleosomal Internal Standards to Measure Histone Modification Density Genome Wide.

Authors:  Adrian T Grzybowski; Zhonglei Chen; Alexander J Ruthenburg
Journal:  Mol Cell       Date:  2015-05-21       Impact factor: 17.970

3.  Quantitative ChIP-Seq normalization reveals global modulation of the epigenome.

Authors:  David A Orlando; Mei Wei Chen; Victoria E Brown; Snehakumari Solanki; Yoon J Choi; Eric R Olson; Christian C Fritz; James E Bradner; Matthew G Guenther
Journal:  Cell Rep       Date:  2014-10-30       Impact factor: 9.423

4.  Formaldehyde-mediated DNA-protein crosslinking: a probe for in vivo chromatin structures.

Authors:  M J Solomon; A Varshavsky
Journal:  Proc Natl Acad Sci U S A       Date:  1985-10       Impact factor: 11.205

5.  Genome-wide mapping of in vivo protein-DNA interactions.

Authors:  David S Johnson; Ali Mortazavi; Richard M Myers; Barbara Wold
Journal:  Science       Date:  2007-05-31       Impact factor: 47.728

6.  Quantifying ChIP-seq data: a spiking method providing an internal reference for sample-to-sample normalization.

Authors:  Nicolas Bonhoure; Gergana Bounova; David Bernasconi; Viviane Praz; Fabienne Lammers; Donatella Canella; Ian M Willis; Winship Herr; Nouria Hernandez; Mauro Delorenzi
Journal:  Genome Res       Date:  2014-04-07       Impact factor: 9.043

7.  An Alternative Approach to ChIP-Seq Normalization Enables Detection of Genome-Wide Changes in Histone H3 Lysine 27 Trimethylation upon EZH2 Inhibition.

Authors:  Brian Egan; Chih-Chi Yuan; Madeleine Lisa Craske; Paul Labhart; Gulfem D Guler; David Arnott; Tobias M Maile; Jennifer Busby; Chisato Henry; Theresa K Kelly; Charles A Tindell; Suchit Jhunjhunwala; Feng Zhao; Charlie Hatton; Barbara M Bryant; Marie Classon; Patrick Trojer
Journal:  PLoS One       Date:  2016-11-22       Impact factor: 3.240

8.  An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites.

Authors:  Peter J Skene; Steven Henikoff
Journal:  Elife       Date:  2017-01-16       Impact factor: 8.140

9.  Parallel factor ChIP provides essential internal control for quantitative differential ChIP-seq.

Authors:  Michael J Guertin; Amy E Cullen; Florian Markowetz; Andrew N Holding
Journal:  Nucleic Acids Res       Date:  2018-07-06       Impact factor: 16.971

10.  A physical basis for quantitative ChIP-sequencing.

Authors:  Bradley M Dickson; Rochelle L Tiedemann; Alison A Chomiak; Evan M Cornett; Robert M Vaughan; Scott B Rothbart
Journal:  J Biol Chem       Date:  2020-09-29       Impact factor: 5.157

  10 in total

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