期刊
NATURE METHODS
卷 12, 期 5, 页码 433-+出版社
NATURE PORTFOLIO
DOI: 10.1038/NMETH.3329
关键词
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资金
- Cornell University Center for Vertebrate Genomics (CVG) and Center for Comparative and Population Genetics [3CPG]
- US National Human Genome Research Institute [5R01HG007070-02]
- US National Institutes of Health [R01 (DK058110)]
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.
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