Genome-wide prediction of DNase I hypersensitivity using gene expression

Weiqiang Zhou, Ben Sherwood, Zhicheng Ji, Yingchao Xue, Fang Du, Jiawei Bai, Mingyao Ying and Hongkai Ji ()
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Weiqiang Zhou: Johns Hopkins University Bloomberg School of Public Health
Ben Sherwood: Johns Hopkins University Bloomberg School of Public Health
Zhicheng Ji: Johns Hopkins University Bloomberg School of Public Health
Yingchao Xue: Hugo W. Moser Research Institute at Kennedy Krieger and Johns Hopkins University School of Medicine
Fang Du: Johns Hopkins University Bloomberg School of Public Health
Jiawei Bai: Johns Hopkins University Bloomberg School of Public Health
Mingyao Ying: Hugo W. Moser Research Institute at Kennedy Krieger and Johns Hopkins University School of Medicine
Hongkai Ji: Johns Hopkins University Bloomberg School of Public Health

Nature Communications, 2017, vol. 8, issue 1, 1-17

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.

Date: 2017
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DOI: 10.1038/s41467-017-01188-x

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