Genome-wide prediction of DNase I hypersensitivity using gene expression. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Zhou, Weiqiang; Sherwood, Ben; Ji, Zhicheng; Xue, Yingchao; Du, Fang; Bai, Jiawei; Ying, Mingyao; Ji, Hongkai
• Is Part Of: Nature communications. Volume 8:Issue 1(2017)
• Page Start: 1
• Page End: 17
• Publication Date: 2017-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-017-01188-x
• Languages: English
• ISSNs: 2041-1723

Abstract

Abstract We evaluate the feasibility of using a biological sample's transcriptome to predict its genome-wide regulatory element activities measured by DNase I hypersensitivity (DH). We develop BIRD, Big Data Regression for predicting DH, to handle this high-dimensional problem. Applying BIRD to the Encyclopedia of DNA Elements (ENCODE) data, we 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. We show 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 facilitating regulome data analyses by serving as pseudo-replicates. Besides improving our understanding of the regulome–transcriptome relationship, this study suggests that transcriptome-based prediction can provide a useful new approach for regulome mapping. A map of the activities of all genomic regulatory elements across cell types and conditions would be a tremendous resource. The computational method introduced here predicts genome-wide accessible sites from gene expression data and allows the authors to build a database of regulatory element activities using publicly available transcriptome data.