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A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells
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Predicting stimulation-dependent enhancer-promoter interactions from ChIP-Seq time course data.

Tomasz Dzida1, Mudassar Iqbal1, Iryna Charapitsa2

  • 1Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.

Peerj
|October 4, 2017
PubMed
Summary
This summary is machine-generated.

We developed a machine learning model to predict enhancer-promoter interactions by analyzing changes in protein binding over time. This approach accurately identifies estrogen receptor alpha (ERα) target genes and their regulatory elements, improving upon existing methods.

Keywords:
Bayesian classifierChIP-SeqEnhancer-promoter interactionEstrogen receptorMachine learning

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Area of Science:

  • Genomics
  • Molecular Biology
  • Computational Biology

Background:

  • Enhancer-promoter interactions are crucial for gene regulation.
  • Understanding these interactions, particularly in response to stimuli like estrogen, is vital for deciphering cellular mechanisms.
  • Estrogen receptor alpha (ERα) plays a significant role in various cellular processes.

Purpose of the Study:

  • To develop a novel machine learning approach for predicting stimulation-dependent enhancer-promoter interactions.
  • To identify a genome-wide set of ERα target genes and their regulatory enhancers.
  • To improve the accuracy of predicting gene regulatory elements compared to existing methods.

Main Methods:

  • Utilized ChIP-Seq data to measure the temporal occupancy of ERα, RNA polymerase II (Pol II), and histone marks (H2AZ, H3K4me3) in MCF7 cells stimulated with estrogen.
  • Developed a Bayesian classifier incorporating temporal binding pattern correlations and genomic proximity to predict enhancer-promoter interactions.
  • Trained and validated the model using experimentally determined interactions and publicly available GRO-Seq data.

Main Results:

  • The developed machine learning method achieved higher precision in predicting enhancer-promoter interactions than methods relying solely on genomic proximity.
  • Identified a genome-wide set of confident ERα target genes and their associated regulatory enhancers.
  • Predicted targets showed a significantly higher likelihood of early nascent transcription compared to predictions based on proximity alone.

Conclusions:

  • The machine learning approach effectively predicts dynamic enhancer-promoter interactions based on temporal protein occupancy changes.
  • This method provides a more accurate and comprehensive way to identify gene regulatory networks, particularly for hormone-responsive genes.
  • The findings offer valuable insights into estrogen-mediated gene regulation and can be applied to broader genomic studies.