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

Abstract

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.

Leukemogenic nucleophosmin mutation disrupts the transcription factor hub that regulates granulomonocytic fates

“…Aligned ChIP-Seq reads were imported, analyzed, and visualized using EaSeq (97). All values were normalized to reads per million per 1 kbp.…”

Crebbp loss cooperates with Bcl2 overexpression to promote lymphoma in mice

“…29 Data were visualized using EaSeq. 30 The nucleotide sequences of regions with significantly altered H3K18Ac in Cbp D/D xEmBcl2 compared with Cbp F/F xEmBcl2 B cells were downloaded using the University of California, Santa Cruz Genome Browser 31 and interrogated for overrepresented DNA motifs and transcription factor binding sites using CisFinder. 32 For further details, refer to supplemental Methods.…”

Jmjd2/Kdm4 demethylases are required for expression ofIl3raand survival of acute myeloid leukemia cells

“…Mapped reads were filtered for PCR duplicates using RmDup (version 0.1.19). Peak calling and visualization of tracks were done using EaSeq (http://www.easeq.net) (Lerdrup et al 2016), and heat maps were made using seqMINER (Ye et al 2011). ChIPseq tracks of H3K4me3 in mouse leukemia stem cells were visualized using EaSeq with data from GSE29130 .…”