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Comparing machine learning methods predicting transcriptome from epigenome with applications to association studies.

Fatemeh Behjati Ardakani1, Shamim Ashrafiyan1, Laura Rumpf1

  • 1Institute for Computational Genomic Medicine, Goethe University Frankfurt, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Hesse, Germany.

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Summary

This study benchmarks machine learning models to predict gene expression from epigenome data, revealing key factors influencing accuracy and identifying disease-associated regulatory regions for B-cell leukemia.

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Area of Science:

  • Genomics
  • Computational Biology
  • Epigenetics

Background:

  • Epigenome variation significantly impacts gene expression in development and disease.
  • Cell type-specific regulatory regions present challenges for gene expression analysis.
  • Machine learning models are emerging tools for predicting gene expression from epigenomic data.

Purpose of the Study:

  • To benchmark state-of-the-art linear and nonlinear machine learning approaches for predicting gene expression from epigenomic data.
  • To create an inferred regulatory catalog of gene models for over 28,000 human genes.
  • To investigate the impact of epigenetic variation on gene expression, particularly in the context of B-cell leukemia.

Main Methods:

  • Benchmarking of linear and nonlinear machine learning models using the IHEC EpiATLAS dataset.
  • Optimization of models for over 28,000 human genes.
  • Evaluation of model performance using CRISPRi and eQTL validation data.
  • Histone-acetylation association studies to link epigenetic variation with gene expression.

Main Results:

  • Gene characteristics and locus epigenomic complexity influence the accuracy of epigenome-to-transcriptome association prediction.
  • Model performance was validated using CRISPRi and eQTL data.
  • Systematic analysis identified genes and regulatory regions associated with B-cell leukemia in patient data.
  • Disease-related functions were linked to identified regulatory regions.

Conclusions:

  • The study provides a foundation for linking epigenome variation to gene expression in human cells.
  • Benchmarking methods on a per-gene basis offers a robust approach for analysis.
  • The developed models and disease context analysis are valuable resources for the scientific community.