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ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization.

Guangchuang Yu¹, Li-Gen Wang², Qing-Yu He³

¹Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou 510632, China, State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, China and.

Bioinformatics (Oxford, England)

|March 14, 2015

Summary

ChIPseeker is a powerful R package that simplifies ChIP-seq data analysis by annotating peaks and visualizing genomic profiles. It enables comparisons and overlap analysis with existing datasets for biological insights.

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Area of Science:

Bioinformatics
Genomics
Molecular Biology

Background:

ChIP-seq (Chromatin Immunoprecipitation sequencing) is a crucial technique for identifying DNA-binding sites of proteins.
Analyzing and annotating ChIP-seq data can be complex, requiring specialized tools for efficient interpretation.

Purpose of the Study:

To introduce ChIPseeker, an R package designed for comprehensive ChIP-seq data analysis.
To provide functionalities for peak annotation, visualization, and comparative analysis of ChIP-seq datasets.

Main Methods:

Utilizes R programming language and Bioconductor infrastructure.
Implements functions for annotating ChIP-seq peaks to genomic features like Transcription Start Sites (TSS).
Supports visualization of peak coverage across chromosomes and binding profiles near TSS regions.

Main Results:

ChIPseeker facilitates the comparison of ChIP peak profiles and annotations.
Enables evaluation of significant overlaps between user-generated ChIP-seq data and a repository of 15,000 GEO datasets.
Supports exploration of co-regulation and transcription factor complex inference.

Conclusions:

ChIPseeker offers a user-friendly and robust platform for ChIP-seq data analysis.
The package aids in discovering biological relationships by comparing experimental data with public ChIP-seq datasets.
Facilitates deeper understanding of gene regulation through integrated analysis and data mining.