Sequence Characteristics Distinguish Transcribed Enhancers from Promoters and Predict Their Breadth of Activity. Issue 4 (29th January 2019)

• Record Type: Journal Article
• Authors: Colbran, Laura L; Chen, Ling; Capra, John A
• Is Part Of: Genetics. Volume 211:Issue 4(2019)
• Page Start: 1205
• Page End: 1217
• Publication Date: 2019-01-29
• Subjects: enhancers -- promoters -- gene regulation -- machine learning -- sequence analysis; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.118.301895
• Languages: English
• ISSNs: 0016-6731

Abstract

Abstract: Enhancers and promoters both regulate gene expression by recruiting transcription factors (TFs); however, the degree to which enhancer vs. promoter activity is due to differences in their sequences or to genomic context is the subject of ongoing debate. We examined this question by analyzing the sequences of thousands of transcribed enhancers and promoters from hundreds of cellular contexts previously identified by cap analysis of gene expression. Support vector machine classifiers trained on counts of all possible 6-bp-long sequences (6-mers) were able to accurately distinguish promoters from enhancers and distinguish their breadth of activity across tissues. Classifiers trained to predict enhancer activity also performed well when applied to promoter prediction tasks, but promoter-trained classifiers performed poorly on enhancers. This suggests that the learned sequence patterns predictive of enhancer activity generalize to promoters, but not vice versa. Our classifiers also indicate that there are functionally relevant differences in enhancer and promoter GC content beyond the influence of CpG islands. Furthermore, sequences characteristic of broad promoter or broad enhancer activity matched different TFs, with predicted ETS- and RFX-binding sites indicative of promoters, and AP-1 sites indicative of enhancers. Finally, we evaluated the ability of our models to distinguish enhancers and promoters defined by histone modifications. Separating these classes was substantially more difficult, and this difference may contribute to ongoing debates about the similarity of enhancers and promoters. In summary, our results suggest that high-confidence transcribed enhancers and promoters can largely be distinguished based on biologically relevant sequence properties.