Methodology article
Improved ChIP-chip analysis by a mixture model approach
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* Corresponding authors: Wei Sun wsun@bios.unc.edu - Ian J Davis ian_davis@med.unc.edu
1 Department of Biostatistics, Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
2 Department of Biochemistry, Center of Excellence in Bioinformatics and Life Sciences, State University of New York at Buffalo, Buffalo, NY, USA
3 Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
4 Department of Pediatrics, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
BMC Bioinformatics 2009, 10:173 doi:10.1186/1471-2105-10-173
Published: 7 June 2009Abstract
Background
Microarray analysis of immunoprecipitated chromatin (ChIP-chip) has evolved from a novel technique to a standard approach for the systematic study of protein-DNA interactions. In ChIP-chip, sites of protein-DNA interactions are identified by signals from the hybridization of selected DNA to tiled oligomers and are graphically represented as peaks. Most existing methods were designed for the identification of relatively sparse peaks, in the presence of replicates.
Results
We propose a data normalization method and a statistical method for peak identification from ChIP-chip data based on a mixture model approach. In contrast to many existing methods, including methods that also employ mixture model approaches, our method is more flexible by imposing less restrictive assumptions and allowing a relatively large proportion of peak regions. In addition, our method does not require experimental replicates and is computationally efficient. We compared the performance of our method with several representative existing methods on three datasets, including a spike-in dataset. These comparisons demonstrate that our approach is more robust and has comparable or higher power than the other methods, especially in the context of abundant peak regions.
Conclusion
Our data normalization and peak detection methods have improved performance to detect peak regions in ChIP-chip data.