Seminar: Global prediction of gene regulatory landscape

Hongkai Ji, PhD, is a professor and director of the graduate program of biostatistics in the Johns Hopkins University Bloomberg School of Public Health. He will share a talk with the Gillings School titled, “Global prediction of gene regulatory landscape using gene expression.”

The seminar will cover how to evaluate the feasibility of using a biological sample’s transcriptome to predict its genome-wide regulatory element activities measured by DNase I hypersensitivity (DH). It is possible to develop BIRD (Big Data Regression for predicting DH) to handle this high-dimensional problem. Applying BIRD to the Encyclopedia of DNA Elements (ENCODE) data, Hongkai and colleagues found that, to a large extent, gene expression predicts DH, and information useful for prediction is contained in the whole transcriptome rather than limited to a regulatory element’s neighboring genes. Hongkai’s work also shows applications of BIRD-predicted DH in predicting transcription factor-binding sites (TFBSs), turning publicly available gene expression samples in Gene Expression Omnibus (GEO) into a regulome database, predicting differential regulatory element activities, and predicting regulatory landscape of single cells and samples with small number of cells. Besides improving understanding of the regulome-transcriptome relationship, this study suggests that transcriptome-based prediction can provide a useful new approach for regulome mapping.