Identification of active transcriptional regulatory elements from GRO-seq data.
Identification of active transcriptional regulatory elements from GRO-seq data.
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DOI：
10.1038/nmeth.3329
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发表时间：
2015-05
期刊：
影响因子：
48
通讯作者：
Siepel A
中科院分区：
文献类型：
--
作者：
Danko CG;Hyland SL;Core LJ;Martins AL;Waters CT;Lee HW;Cheung VG;Kraus WL;Lis JT;Siepel A
转录调控元件（TREs），包括增强子和启动子，决定相关基因的转录水平。我们最近的研究表明，对5'端rna进行富集的全局运行和测序（GRO-seq）可以高精度地显示活性TREs。在这里，我们证明可以通过对标准GRO-seq数据应用敏感的机器学习方法来识别活跃的TREs。这种方法允许在单个实验中分析TREs与基因表达水平和其他转录特征。我们的预测方法被称为GRO-seq的判别性调控元件检测（discriminative Regulatory Element detection from GRO-seq, dREG），它总结了GRO-seq在多个尺度上的读取计数，并使用支持向量回归来识别活跃的TREs。预测的TREs在转录激活的几个标记上更丰富，包括eQTL、gwas相关的SNPs、H3K27ac和转录因子结合，而不是通过其他功能测定确定的标记。利用dREG，我们调查了八种人类细胞类型中的TREs，并为TREs功能的全局模式提供了新的见解。
Transcriptional regulatory elements (TREs), including enhancers and promoters, determine the transcription levels of associated genes. We have recently shown that global run-on and sequencing (GRO-seq) with enrichment for 5'-capped RNAs reveals active TREs with high accuracy. Here, we demonstrate that active TREs can be identified by applying sensitive machine-learning methods to standard GRO-seq data. This approach allows TREs to be assayed together with gene expression levels and other transcriptional features in a single experiment. Our prediction method, called discriminative Regulatory Element detection from GRO-seq (dREG), summarizes GRO-seq read counts at multiple scales and uses support vector regression to identify active TREs. The predicted TREs are more strongly enriched for several marks of transcriptional activation, including eQTL, GWAS-associated SNPs, H3K27ac, and transcription factor binding than those identified by alternative functional assays. Using dREG, we survey TREs in eight human cell types and provide new insights into global patterns of TRE function.