Clustering-local-unique-enriched-signals (CLUES) promotes identification of novel regulators of ES cell self-renewal and pluripotency

BackgroundKey regulators of developmental processes can be prioritized through integrated analysis of ChIP-Seq data of master transcriptional factors (TFs) such as Nanog and Oct4, active histone modifications (HMs) such as H3K4me3 and H3K27ac, and repressive HMs such as H3K27me3. Recent studies show that broad enrichment signals such as super-enhancers and broad H3K4me3 enrichment signals play more dominant roles than short enrichment signals of the master TFs and H3K4me3 in epigenetic regulatory mechanism. Besides the broad enrichment signals, up to ten thousands of short enrichment signals of these TFs and HMs exist in genome. Prioritization of these broad enrichment signals from ChIP-Seq data is a prerequisite for such integrated analysis.ResultsHere, we present a method named Clustering-Local-Unique-Enriched-Signals (CLUES), which uses an adaptive-size-windows strategy to identify enriched regions (ERs) and cluster them into broad enrichment signals. Tested on 62 ENCODE ChIP-Seq datasets of Ctcf and Nrsf, CLUES performs equally well as MACS2 regarding prioritization of ERs with the TF’s motif. Tested on 165 ENCODE ChIP-Seq datasets of H3K4me3, H3K27me3, and H3K36me3, CLUES performs better than existing algorithms on prioritizing broad enrichment signals implicating cell functions influenced by epigenetic regulatory mechanism in cells. Most importantly, CLUES helps to confirm several novel regulators of mouse ES cell self-renewal and pluripotency through integrated analysis of prioritized broad enrichment signals of H3K4me3, H3K27me3, Nanog and Oct4 with the support of a CRISPR/Cas9 negative selection genetic screen.ConclusionsCLUES holds promise for prioritizing broad enrichment signals from ChIP-Seq data. The download site for CLUES is https://github.com/Wuchao1984/CLUESv1.研究背景：可通过整合分析核心转录因子（transcription factors, TFs，如Nanog与Oct4）、活性组蛋白修饰（histone modifications, HMs，如H3K4me3与H3K27ac）以及抑制性组蛋白修饰（如H3K27me3）的染色质免疫共沉淀测序（ChIP-Seq）数据，对发育过程的关键调控因子进行优先级筛选。近期研究表明，在表观遗传调控机制中，超级增强子、宽域H3K4me3富集信号等宽富集信号，相较核心转录因子与H3K4me3的短富集信号，发挥更为主导的调控作用。除宽富集信号外，基因组中还存在多达数万个此类转录因子与组蛋白修饰的短富集信号。从ChIP-Seq数据中筛选宽富集信号，是开展此类整合分析的前提条件。 研究结果：本文提出一种名为聚类局部唯一富集信号（Clustering-Local-Unique-Enriched-Signals, CLUES）的分析方法，该方法采用自适应窗口策略识别富集区域（enriched regions, ERs）并将其聚类为宽富集信号。在针对Ctcf与Nrsf的62组DNA元件百科全书（ENCODE）ChIP-Seq数据集上的测试显示，CLUES在筛选携带转录因子基序的富集区域方面，性能与MACS2相当。在针对H3K4me3、H3K27me3与H3K36me3的165组ENCODE ChIP-Seq数据集上的测试表明，CLUES在筛选关联受细胞表观遗传调控机制影响的细胞功能的宽富集信号方面，表现优于现有算法。尤为重要的是，依托成簇规律间隔短回文重复序列相关蛋白9（CRISPR/Cas9）负筛选遗传筛选的支持，通过对H3K4me3、H3K27me3、Nanog及Oct4的优先级宽富集信号进行整合分析，CLUES助力验证了数个调控小鼠胚胎干细胞（ES细胞）自我更新与多能性的新型调控因子。 研究结论：CLUES有望从ChIP-Seq数据中实现宽富集信号的优先级筛选。CLUES的下载地址为https://github.com/Wuchao1984/CLUESv1。