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PeerJ. 2017 Sep 28;5:e3742. doi: 10.7717/peerj.3742. eCollection 2017.

Predicting stimulation-dependent enhancer-promoter interactions from ChIP-Seq time course data.

Author information

1
Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.
2
Chemical Biology Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany.
3
Department of Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, Netherlands.
4
Genomatix GmbH, Munich, Germany.
5
Helsinki Institute for InformationTechnology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland.
6
Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
7
Department of Public Health, University of Helsinki, Helsinki, Finland.

Abstract

We have developed a machine learning approach to predict stimulation-dependent enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ERα), RNA polymerase (Pol II) and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ERα binding. We use the method to identify a genome-wide confident set of ERα target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ERα binding proximity alone.

KEYWORDS:

Bayesian classifier; ChIP-Seq; Enhancer-promoter interaction; Estrogen receptor; Machine learning

Conflict of interest statement

The authors declare there are no competing interests. Korbinain Grote is employed by Genomatix Software GmbH.

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