期刊
NATURE COMMUNICATIONS
卷 8, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-017-01188-x
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资金
- Maryland Stem Cell Research Fund [2012-MSCRFE-0135-00]
- National Institutes of Health [R01HG006282, R01HG006841]
- Institute for Data Intensive Engineering and Science of the Johns Hopkins University
We evaluate the feasibility of using a biological sample's transcriptome to predict its genomewide 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.
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