Comparing Multiple Protein Binding Profiles in ChIP-seq Experiments

Hatice Gulcin Ozer*, Jiejun Wu, Yi-Wen Huang, Jeffrey Parvin, Tim Huang, Kun Huang

Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210. ozer@bmi.osu.edu

Proc LSS Comput Syst Bioinform Conf. August, 2010. Vol. 9, p. 92-99. Full-Text PDF

*To whom correspondence should be addressed.

New high-throughput sequencing technologies can generate millions of short sequences in a single experiment. As the size of the data increases, comparison of multiple experiments on different cell lines under different experimental conditions becomes a big challenge. In this paper, we investigate ways to compare multiple ChIP-seq experiments. We specifically studied epigenetic regulation of breast cancer and the effect of estrogen using 50 ChIP-seq data from Illumina Genome Analizer II. First, we evaluate the correlation among different experiments focusing on total number of reads in transcribed regions of the genome. Then, we adopt the method that is used to identify most stable genes in RT-PCR experiments to understand background signal across all experiments and to identify most variably transcribed regions of the genome. Gene ontology and function enrichment analysis on the 100 most variable genes demonstrate the biological relevance of the results. In this study, we present a method can effectively select differentially transcribed regions based on protein binding profiles over multiple experiments using real data points without any normalization among the samples.