ROSE是大名鼎鼎的Richard A.Young实验室开发的,全称是RANK ORDERING OF SUPER ENHANCERS,用来找增强子和超级增强子。 ROSE安装 ROSE是一个python包,代码放在Github上(https://github.com/stjude/ROSE),直接下载压缩包就可以了。 wget https://github.com/stjude/ROSE/archive/refs/heads/master.zip unzip master.zi...
一、先说一下rose( RANK ORDERING OF SUPER-ENHANCERS) 定义一下super enhancer(SE),https://en.wikipedia.org/wiki/Super-enhancer,通用H3K27ac的chip-seq macs2出来的bed,通过rose筛选出来的enhancer_withsuper.bed。 安装方法如下: 点击查看安装方法介绍—1。 点击查看安装方法介绍—2。 安装前须知: Python2....
1. 因为超级增强子附近富集了大量的转录活性标记分子,如组蛋白修饰标记的H3K27ac、H3K4me1,染色质修饰P300等,通过这些活性标记分子的抗体,进行ChIP-seq鉴定其在基因组的富集情况,以确定可能的增强子活性位点,然后通过增强子缝合、排序确定阈值,采用ROSE算法鉴定出超级增强子。目前用的比较多的是用H3K27ac抗体进行ChIP-...
Full size table A computerized algorithm called “Rank Ordering of Super-Enhancers” (ROSE) was developed to predict SEs based on ChIP-seq signal in 2013 [6]. This algorithm stitches together enhancers located within a certain distance (<12.5 kb) and ranks all stitched enhancers based on their...
. In this study, we superimposed the SEs obtained from the Rank Ordering of Super-enhancers (ROSE) algorithm across all examined samples. We identified 2097 SEs across HMCLs (n = 9), of which 12.0% (252/2097) are unique to a single cell line. Notably, 49.4% of SEs (1036/2097)...
Super-enhancer regions were identified using the Rank Order Super-Enhancer (ROSE) algorithm [34] (Fig. 5a). Our analysis determined 403 super-enhancer regions in 10A, 627 in AT1, 1053 in DCIS, and 320 in CA1 cells (Additional file 8: Table S5). Interestingly, a stepwise increase in ...
ROSE active SE locus is represented by the black bar. (C) Significance (log10FDR, y axis) of DE genes following ARID1A loss, ranked by FDR value (x axis). SERPINE1 is the most significantly upregulated gene (arrow). (D) Expression of SERPINE1 (RNA-seq) following indicated 12Z ...
The ROSE algorithm30,31 was applied for super-enhancer analysis. We used enhancer elements identified by H3K27ac, and the default stitching size of 12.5 kb and H3K27ac and BRD4 BAM files as input. Graphs were generated using R (R Project for Statistical Computing, https://www.R-proje...
Enhancers were stitched and SEs were identified using ROSE (https://bitbucket.org/young_computation/rose), as described in (Lovén et al., 2013, Whyte et al., 2013). ROSE was run with a stitching distance of 12,500 bp and a TSS exclusion zone size of 2,500 bp, excluding 5,000bp...
SE calling was performed using the ROSE algorithm110,111. Differentially regulated SEs were identified by overlapping SEs with differentially regulated enhancers. Common SEs between cell lines were defined as SEs that had at least 5 kb overlap between them. ATAC-seq library construction, sequencing...