Danko, C. G., Hyland, S. L., Core, L. J., Martins, A. L., Waters, C. T., Lee, H. W., Cheung, V. G., Kraus, W. L., Lis, J. T., Siepel, A. (May 2015) Identification of active transcriptional regulatory elements from GRO-seq data. Nat Methods, 12 (5). pp. 433-8. ISSN 1548-7105 (Electronic)1548-7091 (Linking)
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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.
| Item Type: | Paper |
|---|---|
| Subjects: | bioinformatics bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > transcription bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene expression |
| CSHL Authors: | |
| Communities: | CSHL Cancer Center Program > Cancer Genetics CSHL labs > Siepel lab |
| Depositing User: | Matt Covey |
| Date: | May 2015 |
| Date Deposited: | 19 May 2015 16:37 |
| Last Modified: | 15 Jul 2021 18:57 |
| PMCID: | PMC4507281 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/31506 |
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