Year: 2015
Type: article
Abstract: MOTIVATION: 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. AVAILABILITY AND IMPLEMENTATION: An R software package ChIPComp is freely available at http://web1.sph.emory.edu/users/hwu30/software/ChIPComp.html.
Source: Bioinformatics
Authors: Li Chen, Chi Wang, Zhaohui S. Qin, Hao Wu
Institutions: University of Kentucky, Emory University, Markey Cancer Center
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
Awards: Model-Based Methods for Analyzing ChIP Sequencing Data, Atlanta Clinical and Translational Science Institute (ACTSI) Renewal, UNDERSTANDING MECHANISMS OF hESC SELF-RENEWAL AND CELL FATE COMMITMENT, Emory Alzheimer's Disease Center, G8411566541, University of Kentucky Markey Cancer Center – Cancer Center Support Grant
OpenAlex ID: W2156143491