Greenscreen: A simple method to remove artifactual signals and enrich for true peaks in genomic datasets including ChIP-seq data
Greenscreen: A simple method to remove artifactual signals and enrich for true peaks in genomic datasets including ChIP-seq data
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DOI：
10.1093/plcell/koac282
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发表时间：
2022-09-19
期刊：
影响因子：
11.6
通讯作者：
Wagner, Doris
中科院分区：
文献类型：
--
作者：
Klasfeld, Samantha;Roule, Thomas;Wagner, Doris
染色质免疫沉淀测序（ChIP-seq）被广泛用于鉴定与基因组DNA结合的因子和染色质修饰。ChIP-seq数据分析受到产生超高人工信号的基因组区域的影响。为了从ChIP-seq数据中去除这些信号，DNA元件百科全书（ENCODE）项目开发了一套全面的区域，这些区域由低可映射性和超高信号定义，称为黑名单，适用于人类、小鼠（小家鼠）、线虫（隐线虫）和果蝇（果蝇）。然而，黑名单目前还不能用于许多模型和非模型物种。在这里，我们描述了一种去除假阳性峰的替代方法，称为绿屏。Greenscreen易于实现，需要很少的输入样本，并使用ChIP-seq常用的分析工具。Greenscreen在拟南芥和人类ChIP-seq数据集中像黑名单一样有效地去除人工信号，同时覆盖较少的基因组，并显着改善ChIP-seq峰值调用和下游分析。绿幕过滤揭示了不同遗传背景或组织中真实的因子结合重叠和占用变化。因为它只需要两种输入就能有效，所以“绿屏”很容易适用于任何物种或基因组构建。虽然是为ChIP-seq开发的，但greenscreen也可以识别来自其他基因组数据集的人工信号，包括靶下切割和使用核酸酶释放。我们提出了一种改进的ChIP-seq管道，结合绿屏，比其他方法检测更多的真峰。
Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is widely used to identify factor binding to genomic DNA and chromatin modifications. ChIP-seq data analysis is affected by genomic regions that generate ultra-high artifactual signals. To remove these signals from ChIP-seq data, the Encyclopedia of DNA Elements (ENCODE) project developed comprehensive sets of regions defined by low mappability and ultra-high signals called blacklists for human, mouse (Mus musculus), nematode (Caenorhabditis elegans), and fruit fly (Drosophila melanogaster). However, blacklists are not currently available for many model and nonmodel species. Here, we describe an alternative approach for removing false-positive peaks called greenscreen. Greenscreen is easy to implement, requires few input samples, and uses analysis tools frequently employed for ChIP-seq. Greenscreen removes artifactual signals as effectively as blacklists in Arabidopsis thaliana and human ChIP-seq dataset while covering less of the genome and dramatically improves ChIP-seq peak calling and downstream analyses. Greenscreen filtering reveals true factor binding overlap and occupancy changes in different genetic backgrounds or tissues. Because it is effective with as few as two inputs, greenscreen is readily adaptable for use in any species or genome build. Although developed for ChIP-seq, greenscreen also identifies artifactual signals from other genomic datasets including Cleavage Under Targets and Release Using Nuclease. We present an improved ChIP-seq pipeline incorporating greenscreen that detects more true peaks than other methods.