Literature DB >> 22057161

Detecting differential binding of transcription factors with ChIP-seq.

Kun Liang1, Sündüz Keles.   

Abstract

SUMMARY: Increasing number of ChIP-seq experiments are investigating transcription factor binding under multiple experimental conditions, for example, various treatment conditions, several distinct time points and different treatment dosage levels. Hence, identifying differential binding sites across multiple conditions is of practical importance in biological and medical research. To this end, we have developed a powerful and flexible program, called DBChIP, to detect differentially bound sharp binding sites across multiple conditions, with or without matching control samples. By assigning uncertainty measure to the putative differential binding sites, DBChIP facilitates downstream analysis. DBChIP is implemented in R programming language and can work with a wide range of sequencing file formats. AVAILABILITY: R package DBChIP is available at http://pages.cs.wisc.edu/~kliang/DBChIP/ CONTACT: kliang@stat.wisc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Year:  2011        PMID: 22057161      PMCID: PMC3244766          DOI: 10.1093/bioinformatics/btr605

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  7 in total

1.  A signal-noise model for significance analysis of ChIP-seq with negative control.

Authors:  Han Xu; Lusy Handoko; Xueliang Wei; Chaopeng Ye; Jianpeng Sheng; Chia-Lin Wei; Feng Lin; Wing-Kin Sung
Journal:  Bioinformatics       Date:  2010-04-05       Impact factor: 6.937

2.  Variation in transcription factor binding among humans.

Authors:  Maya Kasowski; Fabian Grubert; Christopher Heffelfinger; Manoj Hariharan; Akwasi Asabere; Sebastian M Waszak; Lukas Habegger; Joel Rozowsky; Minyi Shi; Alexander E Urban; Mi-Young Hong; Konrad J Karczewski; Wolfgang Huber; Sherman M Weissman; Mark B Gerstein; Jan O Korbel; Michael Snyder
Journal:  Science       Date:  2010-03-18       Impact factor: 47.728

3.  Diverse transcription factor binding features revealed by genome-wide ChIP-seq in C. elegans.

Authors:  Wei Niu; Zhi John Lu; Mei Zhong; Mihail Sarov; John I Murray; Cathleen M Brdlik; Judith Janette; Chao Chen; Pedro Alves; Elicia Preston; Cindie Slightham; Lixia Jiang; Anthony A Hyman; Stuart K Kim; Robert H Waterston; Mark Gerstein; Michael Snyder; Valerie Reinke
Journal:  Genome Res       Date:  2010-12-22       Impact factor: 9.043

4.  Genome-wide identification of binding sites defines distinct functions for Caenorhabditis elegans PHA-4/FOXA in development and environmental response.

Authors:  Mei Zhong; Wei Niu; Zhi John Lu; Mihail Sarov; John I Murray; Judith Janette; Debasish Raha; Karyn L Sheaffer; Hugo Y K Lam; Elicia Preston; Cindie Slightham; LaDeana W Hillier; Trisha Brock; Ashish Agarwal; Raymond Auerbach; Anthony A Hyman; Mark Gerstein; Susan E Mango; Stuart K Kim; Robert H Waterston; Valerie Reinke; Michael Snyder
Journal:  PLoS Genet       Date:  2010-02-19       Impact factor: 5.917

5.  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

6.  Evaluation of algorithm performance in ChIP-seq peak detection.

Authors:  Elizabeth G Wilbanks; Marc T Facciotti
Journal:  PLoS One       Date:  2010-07-08       Impact factor: 3.240

7.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

Authors:  Mark D Robinson; Davis J McCarthy; Gordon K Smyth
Journal:  Bioinformatics       Date:  2009-11-11       Impact factor: 6.937

  7 in total
  49 in total

1.  O2 availability impacts iron homeostasis in Escherichia coli.

Authors:  Nicole A Beauchene; Erin L Mettert; Laura J Moore; Sündüz Keleş; Emily R Willey; Patricia J Kiley
Journal:  Proc Natl Acad Sci U S A       Date:  2017-10-30       Impact factor: 11.205

2.  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

Review 3.  Analytical tools and current challenges in the modern era of neuroepigenomics.

Authors:  Ian Maze; Li Shen; Bin Zhang; Benjamin A Garcia; Ningyi Shao; Amanda Mitchell; HaoSheng Sun; Schahram Akbarian; C David Allis; Eric J Nestler
Journal:  Nat Neurosci       Date:  2014-10-28       Impact factor: 24.884

4.  A novel statistical method for quantitative comparison of multiple ChIP-seq datasets.

Authors:  Li Chen; Chi Wang; Zhaohui S Qin; Hao Wu
Journal:  Bioinformatics       Date:  2015-02-13       Impact factor: 6.937

5.  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 6.  Defining bacterial regulons using ChIP-seq.

Authors:  Kevin S Myers; Dan M Park; Nicole A Beauchene; Patricia J Kiley
Journal:  Methods       Date:  2015-05-29       Impact factor: 3.608

7.  Differential Sox10 genomic occupancy in myelinating glia.

Authors:  Camila Lopez-Anido; Guannan Sun; Matthias Koenning; Rajini Srinivasan; Holly A Hung; Ben Emery; Sunduz Keles; John Svaren
Journal:  Glia       Date:  2015-05-14       Impact factor: 7.452

8.  PePr: a peak-calling prioritization pipeline to identify consistent or differential peaks from replicated ChIP-Seq data.

Authors:  Yanxiao Zhang; Yu-Hsuan Lin; Timothy D Johnson; Laura S Rozek; Maureen A Sartor
Journal:  Bioinformatics       Date:  2014-06-03       Impact factor: 6.937

Review 9.  -Omic and Electronic Health Record Big Data Analytics for Precision Medicine.

Authors:  Po-Yen Wu; Chih-Wen Cheng; Chanchala D Kaddi; Janani Venugopalan; Ryan Hoffman; May D Wang
Journal:  IEEE Trans Biomed Eng       Date:  2016-10-10       Impact factor: 4.538

10.  csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows.

Authors:  Aaron T L Lun; Gordon K Smyth
Journal:  Nucleic Acids Res       Date:  2015-11-17       Impact factor: 16.971

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