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

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 re...

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Vydané v:	Bioinformatics Ročník 25; číslo 18; s. 2334 - 2340
Hlavní autori:	Taslim, Cenny, Wu, Jiejun, Yan, Pearlly, Singer, Greg, Parvin, Jeffrey, Huang, Tim, Lin, Shili, Huang, Kun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Oxford University Press 15.09.2009 Oxford Publishing Limited (England)
Predmet:	Binding Sites Bioinformatics Breast Neoplasms - metabolism Cell Line, Tumor Chromatin Immunoprecipitation - methods Comparative studies Computational Biology - methods Female Humans Original Papers RNA Polymerase II - metabolism Sequence Analysis, DNA - methods
ISSN:	1367-4803, 1367-4811, 1460-2059, 1367-4811
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Popis
Shrnutí:	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.
Bibliografia:	To whom correspondence should be addressed. ark:/67375/HXZ-X45RQQJ7-9 istex:29CE9E59EABE6A2EC3B8BFD3739B49E4B09D2AF8 ArticleID:btp384 Associate Editor: Joaquin Dopazo ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	1367-4803 1367-4811 1460-2059 1367-4811
DOI:	10.1093/bioinformatics/btp384