Abstract
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.
Highlights
Many computational programs have been developed to identify enriched regions for a single biological ChIP-seq sample
The reason for choosing this data for the evaluation is the following: 1) it was used by ChIPDiff [11] and is compared to our program; and 2) differential histone modification sites have been validated in the same study [18]
We found that compared with ChIPDiff, QChIPat identified fewer differential histone modification sites (DHMSs)
Summary
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. Many computational programs [4,7,8,9] have been developed to identify enriched regions (referring to either binding sites or histone modification sites) for the ChIP-seq data. Given that many biological questions are often asked to compare the enriched regions under two different conditions, such as before and after using drugs, with and without chemical or hormone treatment, binding patterns of different transcription factors, or one transcription factor binding information in two cell types, it is critical to develop new programs to meet this need and to quantitatively compare two biological ChIPseq samples.
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