Journal Article

Comparative study on ChIP-seq data: normalization and binding pattern characterization

Cenny Taslim, Jiejun Wu, Pearlly Yan, Greg Singer, Jeffrey Parvin, Tim Huang, Shili Lin and Kun Huang

in Bioinformatics

Volume 25, issue 18, pages 2334-2340
Published in print September 2009 | ISSN: 1367-4803
Published online June 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp384
Comparative study on ChIP-seq data: normalization and binding pattern characterization

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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@osu.edu; khuang@bmi.osu.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5537 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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