TY - JOUR
T1 - Genome-wide prediction of DNase i hypersensitivity using gene expression
AU - Zhou, Weiqiang
AU - Sherwood, Ben
AU - Ji, Zhicheng
AU - Xue, Yingchao
AU - Du, Fang
AU - Bai, Jiawei
AU - Ying, Mingyao
AU - Ji, Hongkai
N1 - Publisher Copyright:
© 2017 The Author(s).
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s41467-017-01188-x
DO - 10.1038/s41467-017-01188-x
M3 - Article
C2 - 29051481
AN - SCOPUS:85031899178
SN - 2041-1723
VL - 8
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 1038
ER -