Description
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.
Learning Objective: After participating in this session, the learner should be better able to understand the design and implementation of an automated informatics pipeline to process publicly available ChIP-Seq datasets.
Authors:
Avantika Diwadkar (Presenter)
University of Pennsylvania
Mengyuan Kan, University of Pennsylvania
Blanca Himes, University of Pennsylvania
Presentation Materials: