Comparative study on ChIP-seq data: normalization and binding pattern characterization.
BACKGROUND
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-Normal(K) 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.


BACKGROUND
Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/.
Taslim
Cenny
C
Department of Molecular Virology, Immunology & Medical Genetics, Ohio State University, Columbus, OH 43210, USA. taslim.2@osu.edu
Wu
Jiejun
J
Yan
Pearlly
P
Singer
Greg
G
Parvin
Jeffrey
J
Huang
Tim
T
Lin
Shili
S
Huang
Kun
K
D001665:Binding Sites; D001943:Breast Neoplasms / Q000378:metabolism; D045744:Cell Line, Tumor; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D005260:Female; D006801:Humans; D012319:RNA Polymerase II / Q000378:metabolism; D017422:Sequence Analysis, DNA / Q000379:methods
D012319:RNA Polymerase II