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Updated: Jan 10, 2026

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

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Published on: June 14, 2013

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

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

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

Research Square

|November 24, 2025

Summary

This summary is machine-generated.

We developed Shorkie, a novel DNA language model, to predict gene expression from DNA sequences. This model significantly enhances gene expression prediction and regulatory network analysis by leveraging evolutionary pretraining and transfer learning.

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

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

Purpose of the Study:

To develop a model that improves gene expression prediction from DNA sequence.
To identify and analyze regulatory grammar and motif usage in gene regulation.
To accurately predict the effects of genetic variants on gene expression.

Main Methods:

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

Main Results:

Shorkie substantially improved gene expression prediction compared to baseline models.
The model identified conserved transcription factor binding motifs and their usage dynamics.
Shorkie 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 noncoding variants and regulatory network dynamics.
The framework offers a powerful approach for understanding promoter dynamics, splicing, and motif usage.