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Refining sequence-to-expression modelling with chromatin accessibility.

Orsolya Lapohos1,2,3, Gregory J Fonseca2,4, Amin Emad1,2,3,5,6

  • 1Department of Quantitative Life Sciences, McGill University, Montreal, Quebec, H3A 0G4, Canada.

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Summary
This summary is machine-generated.

Incorporating chromatin accessibility into sequence-to-expression models significantly improves gene expression prediction accuracy. This enhanced model better predicts gene expression across cell types and identifies key regulatory DNA sequences.

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Gene regulation is complex and influenced by DNA sequence and chromatin accessibility.
  • Current sequence-to-expression models often overlook chromatin accessibility, a critical regulatory factor.
  • This study investigates the impact of integrating chromatin accessibility into predictive models.

Purpose of the Study:

  • To develop and evaluate sequence-to-expression models augmented with chromatin accessibility data.
  • To determine if incorporating accessibility improves the prediction of gene expression, especially for highly variable genes and across different cell types.
  • To analyze how accessibility influences the model's identification of important DNA sequence patterns.

Main Methods:

  • Developed sequence-to-expression models incorporating chromatin accessibility as an input feature.
  • Compared the performance of augmented models against sequence-only and accessibility-only models.
  • Utilized attribution scores to interpret the model's focus on DNA sequences.
  • Investigated the effect of fine-tuning pre-trained models with both sequence and accessibility data.
  • Assessed the influence of sequencing depth on prediction performance.

Main Results:

  • Augmented models significantly outperformed sequence-only or accessibility-only models.
  • Improved prediction accuracy for highly variable genes and gene expression in different cell types.
  • Attribution scores in augmented models correlated with chromatin accessibility, revealing cell type-specific sequence patterns.
  • Fine-tuning pre-trained models with accessibility data further enhanced performance.
  • Sequencing depth was identified as a crucial factor for accurate prediction.

Conclusions:

  • Chromatin accessibility is a vital feature for improving sequence-to-expression model performance.
  • Augmented models provide a more accurate and nuanced understanding of gene regulation.
  • The findings highlight the potential of integrating multi-modal genomic data for predictive modeling.