Apr 13, 2020

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

BioRxiv : the Preprint Server for Biology
L. TangMin Li

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 types which lack long-range contact maps. To enrich for functional enhancer-promoter loops over common structural genomic contacts, we trained LoopPredictor with both H3K27ac and YY1 HiChIP data. What's more, the integration of several related multi-omics features facilitated identifying and annotating the predicted loops. LoopPredictor is able to efficiently identify cell type-specific enhancer mediated loops, and promoter-promoter interacti...Continue Reading

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Mentioned in this Paper

Computer Software
Study
Chromosome Structures
Genome
Enzymes, antithrombotic
Three-dimensional
Regulation of Biological Process
Systolic Blood Pressure Measurement
Transcription, Genetic
Gene Expression

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