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

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

Content Provider	Springer Nature : BioMed Central
Author	Liu, Bin Yi, Jimmy SV, Aishwarya Lan, Xun Ma, Yilin Huang, Tim HM Leone, Gustavo Jin, Victor X
Abstract	Background Many 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. Results In 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. Conclusions Several 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 .
Related Links	https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/1471-2164-14-S8-S3.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	14712164
DOI	10.1186/1471-2164-14-S8-S3
Journal	BMC Genomics
Issue Number	8
Volume Number	14
Language	English
Publisher	BioMed Central
Publisher Date	2013-12-09
Access Restriction	Open
Subject Keyword	Life Sciences Microarrays Proteomics Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Histone Modification Normalization Method PANC1 Cell Binding Pattern Epithelial Ovarian Cancer Cell
Content Type	Text
Resource Type	Article
Subject	Biotechnology Genetics
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.1/2023

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

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

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

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