Suppr超能文献

CUT&RUN绿色清单:一致噪声的基因组区域是定量表观基因组图谱的有效标准化因子。

The CUT&RUN greenlist: genomic regions of consistent noise are effective normalizing factors for quantitative epigenome mapping.

作者信息

de Mello Fabio N, Tahira Ana C, Berzoti-Coelho Maria Gabriela, Verjovski-Almeida Sergio

机构信息

Cell Cycle Laboratory, Instituto Butantan, São Paulo, Brazil.

Interunit Bioinformatics Graduate Program, Universidade de São Paulo, São Paulo, Brazil.

出版信息

Brief Bioinform. 2024 Jan 22;25(2). doi: 10.1093/bib/bbad538.

Abstract

Cleavage Under Targets and Release Using Nuclease (CUT&RUN) is a recent development for epigenome mapping, but its unique methodology can hamper proper quantitative analyses. As traditional normalization approaches have been shown to be inaccurate, we sought to determine endogenous normalization factors based on the human genome regions of constant nonspecific signal. This constancy was determined by applying Shannon's information entropy, and the set of normalizer regions, which we named the 'Greenlist', was extensively validated using publicly available datasets. We demonstrate here that the greenlist normalization outperforms the current top standards, and remains consistent across different experimental setups, cell lines and antibodies; the approach can even be applied to different species or to CUT&Tag. Requiring no additional experimental steps and no added cost, this approach can be universally applied to CUT&RUN experiments to greatly minimize the interference of technical variation over the biological epigenome changes of interest.

摘要

靶向切割及核酸酶释放法(CUT&RUN)是表观基因组图谱绘制方面的一项最新进展,但其独特的方法可能会妨碍进行适当的定量分析。由于传统的标准化方法已被证明不准确,我们试图根据具有恒定非特异性信号的人类基因组区域来确定内源性标准化因子。这种恒定性是通过应用香农信息熵来确定的,并且使用公开可用的数据集对我们命名为“Greenlist”的标准化区域集进行了广泛验证。我们在此证明,Greenlist标准化优于当前的顶级标准,并且在不同的实验设置、细胞系和抗体中保持一致;该方法甚至可以应用于不同物种或CUT&Tag。该方法无需额外的实验步骤和成本,可以普遍应用于CUT&RUN实验,以极大地减少技术变异对感兴趣的生物表观基因组变化的干扰。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fd5/10818165/d330d315ae7c/bbad538f1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验