Facilitating Analysis of Publicly Available ChIP-Seq Data for Integrative Studies

AMIA Annu Symp Proc. 2020 Mar 4:2019:371-379. eCollection 2019.


ChIP-Seq, a technique that allows for quantification of DNA sequences bound by transcription factors or histones, has been widely used to characterize genome-wide DNA-protein binding at baseline and induced by specific exposures. Integrating results of multiple ChIP-Seq datasets is a convenient approach to identify robust DNA- protein binding sites and determine their cell-type specificity. We developed brocade, a computational pipeline for reproducible analysis of publicly available ChIP-Seq data that creates R markdown reports containing information on datasets downloaded, quality control metrics, and differential binding results. Glucocorticoids are commonly used anti-inflammatory drugs with tissue-specific effects that are not fully understood. We demonstrate the utility of brocade via the analysis of five ChIP-Seq datasets involving glucocorticoid receptor (GR), a transcription factor that mediates glucocorticoid response, to identify cell type-specific and shared GR binding sites across the five cell types. Our results show that brocade facilitates analysis of individual ChIP-Seq datasets and comparative studies involving multiple datasets.

MeSH terms

  • Base Sequence
  • Chromatin Immunoprecipitation Sequencing*
  • Datasets as Topic*
  • Gene Expression Regulation
  • Genome-Wide Association Study
  • Glucocorticoids / metabolism
  • Glucocorticoids / pharmacology
  • Humans
  • Integrative Medicine
  • Quality Control
  • Receptors, Glucocorticoid / genetics
  • Receptors, Glucocorticoid / metabolism*
  • Transcription Factors / drug effects
  • Transcription Factors / metabolism*


  • Glucocorticoids
  • Receptors, Glucocorticoid
  • Transcription Factors