An interactive environment for agile analysis and visualization of ChIP-sequencing data

• Published in: Nature structural & molecular biology Vol. 23; no. 4; pp. 349 - 357
• Main Authors: Lerdrup, Mads, Johansen, Jens Vilstrup, Agrawal-Singh, Shuchi, Hansen, Klaus
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.04.2016; Nature Publishing Group
• Subjects: 45/15; 631/114; 631/1647/2217/2088; 631/208/176/2016; 631/208/212/177; 64/60; Animals; Biochemistry; Biological Microscopy; Biology; Chromatin; Chromatin Immunoprecipitation - methods; Epigenetics; Genome; Genomes; Genomics; Life Sciences; Membrane Biology; Mice; Polycomb-Group Proteins - analysis; Protein Structure; Scientists; Software; Stem cells; technical-report; Visualization (Computers); United States
• ISSN: 1545-9993; 1545-9985; 1545-9985
• DOI: 10.1038/nsmb.3180

EaSeq, a user-friendly and freely available software tool, offers fast and comprehensive ChIP-sequencing data analyses, enabling experimentalists to easily extract information and generate hypotheses from genome-wide datasets. To empower experimentalists with a means for fast and comprehensive chromatin immunoprecipitation sequencing (ChIP-seq) data analyses, we introduce an integrated computational environment, EaSeq. The software combines the exploratory power of genome browsers with an extensive set of interactive and user-friendly tools for genome-wide abstraction and visualization. It enables experimentalists to easily extract information and generate hypotheses from their own data and public genome-wide datasets. For demonstration purposes, we performed meta-analyses of public Polycomb ChIP-seq data and established a new screening approach to analyze more than 900 datasets from mouse embryonic stem cells for factors potentially associated with Polycomb recruitment. EaSeq, which is freely available and works on a standard personal computer, can substantially increase the throughput of many analysis workflows, facilitate transparency and reproducibility by automatically documenting and organizing analyses, and enable a broader group of scientists to gain insights from ChIP-seq data.