Danko Charles G, Hyland Stephanie L, Core Leighton J, Martins Andre L, Waters Colin T, Lee Hyung Won, Cheung Vivian G, Kraus W Lee, Lis John T, Siepel Adam
1] Baker Institute for Animal Health, Cornell University, Ithaca, New York, USA. [2] Department of Biomedical Sciences, Cornell University, Ithaca, New York, USA. [3] Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.
Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, USA.
Nat Methods. 2015 May;12(5):433-8. doi: 10.1038/nmeth.3329. Epub 2015 Mar 23.
Modifications to the global run-on and sequencing (GRO-seq) protocol that enrich for 5'-capped RNAs can be used to reveal active transcriptional regulatory elements (TREs) with high accuracy. Here, we introduce discriminative regulatory-element detection from GRO-seq (dREG), a sensitive machine learning method that uses support vector regression to identify active TREs from GRO-seq data without requiring cap-based enrichment (https://github.com/Danko-Lab/dREG/). This approach allows TREs to be assayed together with gene expression levels and other transcriptional features in a single experiment. Predicted TREs are more enriched for several marks of transcriptional activation—including expression quantitative trait loci, disease-associated polymorphisms, acetylated histone 3 lysine 27 (H3K27ac) and transcription factor binding—than those identified by alternative functional assays. Using dREG, we surveyed TREs in eight human cell types and provide new insights into global patterns of TRE function.
对用于富集5'-帽化RNA的全局连续转录和测序（GRO-seq）方案进行修改，可用于高精度地揭示活性转录调控元件（TRE）。在此，我们介绍了从GRO-seq中进行判别性调控元件检测（dREG），这是一种灵敏的机器学习方法，它使用支持向量回归从GRO-seq数据中识别活性TRE，而无需基于帽的富集（https://github.com/Danko-Lab/dREG/）。这种方法允许在单个实验中同时检测TRE以及基因表达水平和其他转录特征。预测的TRE比通过其他功能测定法鉴定的TRE在几种转录激活标记上更富集，包括表达数量性状位点、疾病相关多态性、乙酰化组蛋白3赖氨酸27（H3K27ac）和转录因子结合。使用dREG，我们调查了八种人类细胞类型中的TRE，并对TRE功能的全局模式提供了新的见解。
