title: A novel statistical method for quantitative comparison of multiple ChIP-seq datasets.
abstract: BACKGROUND
  ChIP-seq is a powerful technology to measure the protein binding or histone modification strength in the whole genome scale. Although there are a number of methods available for single ChIP-seq data analysis (e.g. 'peak detection'), rigorous statistical method for quantitative comparison of multiple ChIP-seq datasets with the considerations of data from control experiment, signal to noise ratios, biological variations and multiple-factor experimental designs is under-developed.
  
  
  RESULTS
  In this work, we develop a statistical method to perform quantitative comparison of multiple ChIP-seq datasets and detect genomic regions showing differential protein binding or histone modification. We first detect peaks from all datasets and then union them to form a single set of candidate regions. The read counts from IP experiment at the candidate regions are assumed to follow Poisson distribution. The underlying Poisson rates are modeled as an experiment-specific function of artifacts and biological signals. We then obtain the estimated biological signals and compare them through the hypothesis testing procedure in a linear model framework. Simulations and real data analyses demonstrate that the proposed method provides more accurate and robust results compared with existing ones.
  
  
  METHODS
  An R software package ChIPComp is freely available at http://web1.sph.emory.edu/users/hwu30/software/ChIPComp.html.
authors: Department of Mathematics and Computer Science, Atlanta, GA 30322, USA, Department of Biostatistics and Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA, Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA and Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA.
authors: Li
authors: L
authors: Chen
authors: Department of Mathematics and Computer Science, Atlanta, GA 30322, USA, Department of Biostatistics and Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA, Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA and Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA.
authors: Chi
authors: C
authors: Wang
authors: Department of Mathematics and Computer Science, Atlanta, GA 30322, USA, Department of Biostatistics and Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA, Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA and Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA Department of Mathematics and Computer Science, Atlanta, GA 30322, USA, Department of Biostatistics and Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA, Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA and Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA.
authors: Zhaohui S
authors: ZS
authors: Qin
authors: Department of Mathematics and Computer Science, Atlanta, GA 30322, USA, Department of Biostatistics and Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA, Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA and Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA.
authors: Hao
authors: H
authors: Wu
mesh_terms: D000465:Algorithms; D016477:Artifacts; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D066264:Datasets as Topic; D015894:Genome, Human; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006657:Histones / Q000378:metabolism*; D006801:Humans; D015233:Models, Statistical; D016012:Poisson Distribution; D011485:Protein Binding; D017422:Sequence Analysis, DNA / Q000379:methods; D012984:Software; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism*
chemical_list: D006657:Histones; D014157:Transcription Factors