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Bioinformatics Advance Access published online on June 26, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp384
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© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Comparative Study on ChIP-seq Data: Normalization and Binding Pattern Characterization

Cenny Taslim 1,2,*, Jiejun Wu 1, Pearlly Yan 1, Greg Singer 1, Jeffrey Parvin 3,4, Tim Huang 1, Shili Lin 2 and Kun Huang 3,4,*

1Department of Molecular Virology, Immunology & Medical Genetics, The Ohio State University, Columbus, OH 43210.
2Department of Statistics, The Ohio State University, Columbus, OH 43210.
3Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210.
4OSUCCC Biomedical Informatics Shared Resources, The Ohio State University, Columbus, OH 43210.

*To whom correspondence should be addressed. Dr. Cenny Taslim, E-mail: taslim.2{at}osu.edu


  Abstract

Motivation: Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here we present a nonlinear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns.

Results: We apply a two-step nonlinear normalization method based on locally weighted regression (LOESS) approach to compare ChIPseq data across multiple samples and model the difference using an Exponential-NormalKmixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (p-value < 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen resistant cells. These results show that the nonlinear normalization method can be used to analyze ChIP-seq data.

Availability: Data is available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/

Contact: cenny.taslim{at}osumc.edu; khuang{at}bmi.osu.edu

Supplementary info: Supplementary figures and tables are available at Bioinformatics online.

Associate Editor: Dr. Joaquin Dopazo

Received on February 28, 2009; revised on May 5, 2009; accepted on May 27, 2009

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