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Genome-wide prediction of DNase I hypersensitivity using gene expression.


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.

SUBMITTER: Zhou W 

PROVIDER: S-EPMC5715040 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Genome-wide prediction of DNase I hypersensitivity using gene expression.

Zhou Weiqiang W   Sherwood Ben B   Ji Zhicheng Z   Xue Yingchao Y   Du Fang F   Bai Jiawei J   Ying Mingyao M   Ji Hongkai H  

Nature communications 20171019 1


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 r  ...[more]

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