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Bioinformatics Advance Access originally published online on June 26, 2009
Bioinformatics 2009 25(18):2334-2340; 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, 2Department of Statistics, 3Department of Biomedical Informatics and 4OSUCCC Biomedical Informatics Shared Resources, The Ohio State University, Columbus, OH 43210, USA

*To whom correspondence should be addressed.


  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 non-linear 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 non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-NormalK mixture 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 < 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 non-linear normalization method can be used to analyze ChIP-seq data across multiple samples.

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

Contact: taslim.2{at}osu.edu; khuang{at}bmi.osu.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Joaquin Dopazo

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

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