Paper Info
Title
A novel statistical method for quantitative comparison of multiple ChIP-seq datasets.
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 underdeveloped. 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.
Year
DOI
Venue
2015
10.1093/bioinformatics/btv094
BIOINFORMATICS
DocType
Volume
Issue
Journal
31
12
ISSN
Citations 
PageRank 
1367-4803
7
0.69
References 
Authors
5
4
Name
Order
Citations
PageRank
Li Chen1778.25
Chi Wang2162.85
Zhaohui Qin328630.63
Hao Wu411234.60