Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model.

ABSTRACT: Transcriptional enhancers commonly work over long genomic distances to precisely regulate spatiotemporal gene expression patterns. Dissecting the promoters physically contacted by these distal regulatory elements is essential for understanding developmental processes as well as the role of disease-associated risk variants. Modern proximity-ligation assays, like HiChIP and ChIA-PET, facilitate the accurate identification of long-range contacts between enhancers and promoters. However, these assays are technically challenging, expensive, and time-consuming, making it difficult to investigate enhancer topologies, especially in uncharacterized cell types. To overcome these shortcomings, we therefore designed LoopPredictor, an ensemble machine learning model, to predict genome topology for cell

SUBMITTER: Tang L 

PROVIDER: S-EPMC7706734 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature