Literature DB >> 22179591

A computational pipeline for comparative ChIP-seq analyses.

Anaïs F Bardet1, Qiye He, Julia Zeitlinger, Alexander Stark.   

Abstract

Chromatin immunoprecipitation (ChIP) followed by deep sequencing can now easily be performed across different conditions, time points and even species. However, analyzing such data is not trivial and standard methods are as yet unavailable. Here we present a protocol to systematically compare ChIP-sequencing (ChIP-seq) data across conditions. We first describe technical guidelines for data preprocessing, read mapping, read-density visualization and peak calling. We then describe methods and provide code with specific examples to compare different data sets across species and across conditions, including a threshold-free approach to measure global similarity, a strategy to assess the binary conservation of binding events and measurements for quantitative changes of binding. We discuss how differences in binding can be related to gene functions, gene expression and sequence changes. Once established, this protocol should take about 2 d to complete and be generally applicable to many data sets.

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Year:  2011        PMID: 22179591     DOI: 10.1038/nprot.2011.420

Source DB:  PubMed          Journal:  Nat Protoc        ISSN: 1750-2799            Impact factor:   13.491


  47 in total

1.  Genome-wide location and function of DNA binding proteins.

Authors:  B Ren; F Robert; J J Wyrick; O Aparicio; E G Jennings; I Simon; J Zeitlinger; J Schreiber; N Hannett; E Kanin; T L Volkert; C J Wilson; S P Bell; R A Young
Journal:  Science       Date:  2000-12-22       Impact factor: 47.728

2.  Program-specific distribution of a transcription factor dependent on partner transcription factor and MAPK signaling.

Authors:  Julia Zeitlinger; Itamar Simon; Christopher T Harbison; Nancy M Hannett; Thomas L Volkert; Gerald R Fink; Richard A Young
Journal:  Cell       Date:  2003-05-02       Impact factor: 41.582

3.  JASPAR: an open-access database for eukaryotic transcription factor binding profiles.

Authors:  Albin Sandelin; Wynand Alkema; Pär Engström; Wyeth W Wasserman; Boris Lenhard
Journal:  Nucleic Acids Res       Date:  2004-01-01       Impact factor: 16.971

4.  The specificity of protein-DNA crosslinking by formaldehyde: in vitro and in drosophila embryos.

Authors:  J Toth; M D Biggin
Journal:  Nucleic Acids Res       Date:  2000-01-15       Impact factor: 16.971

5.  High conservation of transcription factor binding and evidence for combinatorial regulation across six Drosophila species.

Authors:  Qiye He; Anaïs F Bardet; Brianne Patton; Jennifer Purvis; Jeff Johnston; Ariel Paulson; Madelaine Gogol; Alexander Stark; Julia Zeitlinger
Journal:  Nat Genet       Date:  2011-04-10       Impact factor: 38.330

6.  A global network of transcription factors, involving E2A, EBF1 and Foxo1, that orchestrates B cell fate.

Authors:  Yin C Lin; Suchit Jhunjhunwala; Christopher Benner; Sven Heinz; Eva Welinder; Robert Mansson; Mikael Sigvardsson; James Hagman; Celso A Espinoza; Janusz Dutkowski; Trey Ideker; Christopher K Glass; Cornelis Murre
Journal:  Nat Immunol       Date:  2010-06-13       Impact factor: 25.606

7.  Genetic analysis of variation in transcription factor binding in yeast.

Authors:  Wei Zheng; Hongyu Zhao; Eugenio Mancera; Lars M Steinmetz; Michael Snyder
Journal:  Nature       Date:  2010-03-17       Impact factor: 49.962

8.  Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing.

Authors:  Gordon Robertson; Martin Hirst; Matthew Bainbridge; Misha Bilenky; Yongjun Zhao; Thomas Zeng; Ghia Euskirchen; Bridget Bernier; Richard Varhol; Allen Delaney; Nina Thiessen; Obi L Griffith; Ann He; Marco Marra; Michael Snyder; Steven Jones
Journal:  Nat Methods       Date:  2007-06-11       Impact factor: 28.547

9.  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

10.  GONOME: measuring correlations between GO terms and genomic positions.

Authors:  Stefan M Stanley; Timothy L Bailey; John S Mattick
Journal:  BMC Bioinformatics       Date:  2006-02-25       Impact factor: 3.169

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  53 in total

1.  Genome-Wide Studies Reveal that H3K4me3 Modification in Bivalent Genes Is Dynamically Regulated during the Pluripotent Cell Cycle and Stabilized upon Differentiation.

Authors:  Rodrigo A Grandy; Troy W Whitfield; Hai Wu; Mark P Fitzgerald; Jennifer J VanOudenhove; Sayyed K Zaidi; Martin A Montecino; Jane B Lian; André J van Wijnen; Janet L Stein; Gary S Stein
Journal:  Mol Cell Biol       Date:  2015-12-07       Impact factor: 4.272

2.  Identification of transcription factor binding sites from ChIP-seq data at high resolution.

Authors:  Anaïs F Bardet; Jonas Steinmann; Sangeeta Bafna; Juergen A Knoblich; Julia Zeitlinger; Alexander Stark
Journal:  Bioinformatics       Date:  2013-08-24       Impact factor: 6.937

3.  De novo detection of differentially bound regions for ChIP-seq data using peaks and windows: controlling error rates correctly.

Authors:  Aaron T L Lun; Gordon K Smyth
Journal:  Nucleic Acids Res       Date:  2014-05-22       Impact factor: 16.971

4.  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

5.  Retrieving Chromatin Patterns from Deep Sequencing Data Using Correlation Functions.

Authors:  Jana Molitor; Jan-Philipp Mallm; Karsten Rippe; Fabian Erdel
Journal:  Biophys J       Date:  2017-01-26       Impact factor: 4.033

6.  A MAD-Bayes Algorithm for State-Space Inference and Clustering with Application to Querying Large Collections of ChIP-Seq Data Sets.

Authors:  Chandler Zuo; Kailei Chen; Sündüz Keleş
Journal:  J Comput Biol       Date:  2016-11-11       Impact factor: 1.479

Review 7.  Absence of a simple code: how transcription factors read the genome.

Authors:  Matthew Slattery; Tianyin Zhou; Lin Yang; Ana Carolina Dantas Machado; Raluca Gordân; Remo Rohs
Journal:  Trends Biochem Sci       Date:  2014-08-14       Impact factor: 13.807

8.  Parental epigenetic asymmetry of PRC2-mediated histone modifications in the Arabidopsis endosperm.

Authors:  Jordi Moreno-Romero; Hua Jiang; Juan Santos-González; Claudia Köhler
Journal:  EMBO J       Date:  2016-04-25       Impact factor: 11.598

9.  YY1 plays an essential role at all stages of B-cell differentiation.

Authors:  Eden Kleiman; Haiqun Jia; Salvatore Loguercio; Andrew I Su; Ann J Feeney
Journal:  Proc Natl Acad Sci U S A       Date:  2016-06-22       Impact factor: 11.205

10.  Structural heterogeneity and functional diversity of topologically associating domains in mammalian genomes.

Authors:  Xiao-Tao Wang; Peng-Fei Dong; Hong-Yu Zhang; Cheng Peng
Journal:  Nucleic Acids Res       Date:  2015-07-06       Impact factor: 16.971

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