Abstract
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.
Original language | English (US) |
---|---|
Pages (from-to) | 433-438 |
Number of pages | 6 |
Journal | Nature methods |
Volume | 12 |
Issue number | 5 |
DOIs | |
State | Published - Apr 29 2015 |
ASJC Scopus subject areas
- Biotechnology
- Biochemistry
- Molecular Biology
- Cell Biology