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Predicting dynamic expression patterns in budding yeast with a fungal DNA language model.

Kuan-Hao Chao^1,2, Majed Mohamed Magzoub³, Emily Stoops³

¹Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.

Biorxiv : the Preprint Server for Biology

|September 26, 2025

Summary

We developed Shorkie, a novel DNA language model, to predict gene expression from DNA sequences. This model significantly enhances gene expression prediction and variant effect analysis, advancing our understanding of gene regulation.

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Predicting gene expression from DNA is complex due to intricate regulatory mechanisms.
Understanding gene regulation is crucial for deciphering noncoding variant effects and biological networks.

Purpose of the Study:

To develop a novel DNA language model for improved gene expression prediction.
To capture conserved regulatory grammar across related species for enhanced model performance.
To accurately predict the functional impact of DNA sequence variants on gene expression.

Main Methods:

Pretraining a masked DNA language model on 165 fungal genomes.
Fine-tuning the language model using yeast RNA-seq data and time-course induction experiments.
Evaluating model performance on gene expression prediction, cis-eQTL classification, and massively parallel reporter assays.

Main Results:

The Shorkie model significantly improved gene expression prediction compared to baseline models.
Shorkie identified transcription factor binding motifs and tracked their usage during induction.
The model accurately predicted variant effects, outperforming existing sequence-to-expression models.

Conclusions:

Evolutionary-scale pretraining combined with transfer learning enhances the decoding of gene regulation from sequence.
Shorkie provides valuable insights into promoter dynamics, splicing, and regulatory motif activity.
This framework advances the study of noncoding variants and complex regulatory networks.