Genome-wide prediction of DNase i hypersensitivity using gene expression

Weiqiang Zhou, Ben Sherwood, Zhicheng Ji, Yingchao Xue, Fang Du, Jiawei Bai, Mingyao Ying, Hongkai Ji

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number1038
JournalNature communications
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2017

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

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