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Incorporating chromatin accessibility data into sequence-to-expression modeling.

Pei-Chen Peng1, Md Abul Hassan Samee1, Saurabh Sinha2

  • 1Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois.

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Summary
This summary is machine-generated.

This study enhances gene expression prediction by incorporating chromatin accessibility into a thermodynamics-based model (GEMSTAT-A). The improved model, GEMSTAT-A, better predicts expression levels by considering how DNA accessibility affects transcription factor binding.

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Area of Science:

  • Genomics
  • Computational Biology
  • Developmental Biology

Background:

  • Predicting gene expression from DNA sequences is a key challenge in genomics.
  • Thermodynamics-based models like GEMSTAT predict expression from enhancers and transcription factor levels.
  • Existing models do not account for chromatin accessibility's impact on transcription factor binding and gene expression.

Purpose of the Study:

  • To extend the GEMSTAT model by incorporating chromatin accessibility data.
  • To quantify the effect of chromatin accessibility on the accuracy of gene expression prediction.
  • To improve sequence-to-expression models using whole-genome DNA accessibility measurements.

Main Methods:

  • Modified the GEMSTAT model to create GEMSTAT-A, integrating chromatin accessibility data.
  • Assumed accessibility at a binding site influences transcription factor binding strength.
  • Applied the GEMSTAT-A model to a dataset of Drosophila embryo enhancers.

Main Results:

  • GEMSTAT-A demonstrated significantly improved fits for predicting gene expression from enhancer sequences.
  • The improved accuracy stemmed from variations in accessibility within enhancers, not overall elevated accessibility.
  • The study validated the utility of chromatin accessibility data for sequence-to-expression models.

Conclusions:

  • Incorporating chromatin accessibility significantly enhances the accuracy of thermodynamics-based gene expression models.
  • Variations in DNA accessibility within regulatory regions are crucial for accurate expression prediction.
  • Future research should focus on modeling accessibility from sequence and regulatory context for precise perturbation effect predictions.