An integrated ChIP-seq analysis platform with customizable workflows

Eugenia G Giannopoulou,Olivier Elemento

doi:10.1186/1471-2105-12-277

Abstract

BackgroundChromatin immunoprecipitation followed by next generation sequencing (ChIP-seq), enables unbiased and genome-wide mapping of protein-DNA interactions and epigenetic marks. The first step in ChIP-seq data analysis involves the identification of peaks (i.e., genomic locations with high density of mapped sequence reads). The next step consists of interpreting the biological meaning of the peaks through their association with known genes, pathways, regulatory elements, and integration with other experiments. Although several programs have been published for the analysis of ChIP-seq data, they often focus on the peak detection step and are usually not well suited for thorough, integrative analysis of the detected peaks.ResultsTo address the peak interpretation challenge, we have developed ChIPseeqer, an integrative, comprehensive, fast and user-friendly computational framework for in-depth analysis of ChIP-seq datasets. The novelty of our approach is the capability to combine several computational tools in order to create easily customized workflows that can be adapted to the user's needs and objectives. In this paper, we describe the main components of the ChIPseeqer framework, and also demonstrate the utility and diversity of the analyses offered, by analyzing a published ChIP-seq dataset.ConclusionsChIPseeqer facilitates ChIP-seq data analysis by offering a flexible and powerful set of computational tools that can be used in combination with one another. The framework is freely available as a user-friendly GUI application, but all programs are also executable from the command line, thus providing flexibility and automatability for advanced users.

Highlights

Chromatin immunoprecipitation followed by generation sequencing (ChIP-seq), enables unbiased and genome-wide mapping of protein-DNA interactions and epigenetic marks
By sequencing millions of immunoprecipitated DNA fragments in a single experiment, ChIP-seq outperforms the array-based ChIP-chip (Chromatin Immunoprecipitation followed by DNA microarray hybridization) technology in terms of quality, specificity, and coverage [1,2,3], and has the potential to greatly improve our understanding of the mechanisms underlying transcriptional regulation [4,5,6,7,8]
We showed that using the ChIPseeqer framework we can perform sophisticated analyses of ChIP-seq datasets, explore the data from multiple perspectives, and address specific biological questions, such as “How do promoter peaks differ from distal peaks?”, “Are there genes with both promoter and enhancer peaks?”

Summary

Introduction

Chromatin immunoprecipitation followed by generation sequencing (ChIP-seq), enables unbiased and genome-wide mapping of protein-DNA interactions and epigenetic marks. The first step in ChIP-seq data analysis involves the identification of peaks (i.e., genomic locations with high density of mapped sequence reads). Several programs have been published for the analysis of ChIP-seq data, they often focus on the peak detection step and are usually not well suited for thorough, integrative analysis of the detected peaks. The use of chromatin immunoprecipitation in combination with high-throughput sequencing (ChIP-seq) has enabled the study of genome-wide mapping of proteinDNA interaction and epigenetic marks. Many peak detection methodologies and software tools have been developed for the analysis of ChIP-seq data since the other tools provided.

Results

Discussion

Conclusion