QChIPat: a quantitative method to identify distinct binding patterns for two biological ChIP-seq samples in different experimental conditions

BMC Genomics

Research

Gustavo Leone¹  Aishwarya SV²  Jimmy Yi²  Yilin Ma²  Xun Lan²  Bin Liu³  Victor X Jin⁴  Tim HM Huang⁴ 

[1] Comprenhensive Cancer Center, The Ohio State University, 43210, Columbus, OH, USA;Department of Biomedical Informatics, The Ohio State University, 43210, Columbus, OH, USA;Department of Biomedical Informatics, The Ohio State University, 43210, Columbus, OH, USA;School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, 518055, Shenzhen, Guangdong, P.R., China;Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, 518055, Shenzhen, Guangdong, China;Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, P. R., China;Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, 78229, San Antonio, TX, USA;

关键词: Histone Modification;    Normalization Method;    PANC1 Cell;    Binding Pattern;    Epithelial Ovarian Cancer Cell;   

DOI  :  10.1186/1471-2164-14-S8-S3

来源: Springer

【 摘 要 】

BackgroundMany computational programs have been developed to identify enriched regions for a single biological ChIP-seq sample. Given that many biological questions are often asked to compare the difference between two different conditions, it is important to develop new programs that address the comparison of two biological ChIP-seq samples. Despite several programs designed to address this question, these programs suffer from some drawbacks, such as inability to distinguish whether the identified differential enriched regions are indeed significantly enriched, lack of distinguishing binding patterns, and neglect of the normalization between samples.ResultsIn this study, we developed a novel quantitative method for comparing two biological ChIP-seq samples, called QChIPat. Our method employs a new global normalization method: nonparametric empirical Bayes (NEB) correction normalization, utilizes pre-defined enriched regions identified from single-sample peak calling programs, uses statistical methods to define differential enriched regions, then defines binding (histone modification) pattern information for those differential enriched regions. Our program was tested on a benchmark data: histone modifications data used by ChIPDiffs. It was then applied on two study cases: one to identify differential histone modification sites for ChIP-seq of H3K27me3 and H3K9me2 data in AKT1-transfected MCF10A cells; the other to identify differential binding sites for ChIP-seq of TCF7L2 data in MCF7 and PANC1 cells.ConclusionsSeveral advantages of our program include: 1) it considers a control (or input) experiment; 2) it incorporates a novel global normalization strategy: nonparametric empirical Bayes correction normalization; 3) it provides the binding pattern information among different enriched regions. QChIPat is implemented in R, Perl and C++, and has been tested under Linux. The R package is available at http://motif.bmi.ohio-state.edu/QChIPat.

【 授权许可】

Unknown   
© Liu et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.