Diwadkar Avantika R, Kan Mengyuan, Himes Blanca E
Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, US.
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.
染色质免疫沉淀测序（ChIP-Seq）是一种能够对与转录因子或组蛋白结合的DNA序列进行定量分析的技术，已被广泛用于在基线状态以及特定暴露诱导下对全基因组DNA-蛋白质结合情况进行表征。整合多个ChIP-Seq数据集的结果是识别稳定的DNA-蛋白质结合位点并确定其细胞类型特异性的便捷方法。我们开发了Brocade，这是一个用于对公开可用的ChIP-Seq数据进行可重复分析的计算流程，它会生成包含有关下载的数据集信息、质量控制指标和差异结合结果的R markdown报告。糖皮质激素是常用的抗炎药物，其组织特异性作用尚未完全明确。我们通过分析五个涉及糖皮质激素受体（GR）的ChIP-Seq数据集（GR是一种介导糖皮质激素反应的转录因子）来证明Brocade的实用性，以识别五种细胞类型中细胞类型特异性和共享的GR结合位点。我们的结果表明，Brocade有助于对单个ChIP-Seq数据集进行分析以及涉及多个数据集的比较研究。
