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FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi
Ananthan Nambiar1,2, Veronika Dubinkina1,2,3, Simon Liu2,4
1Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, United States of America.
Plos Computational Biology
|November 16, 2023
Summary
We developed FUN-PROSE, a deep learning model predicting gene expression changes in fungi using promoter sequences and transcription factor levels. This advances metabolic engineering by revealing condition-specific gene regulation.
Area of Science:
- Genomics
- Systems Biology
- Computational Biology
Background:
- Understanding cellular states requires analyzing genome-wide mRNA levels under various conditions.
- Metabolic engineering of fungi for chemical production necessitates accurate prediction of condition-specific gene expression.
- Previous deep learning models often predict average gene expression, neglecting condition-specific variations.
Purpose of the Study:
- To develop a deep learning model, FUN-PROSE, for predicting differential gene expression in fungi.
- To utilize promoter sequences and transcription factor levels as inputs for predicting condition-specific gene expression.
- To interpret the model for discovering novel regulatory elements and transcription factor-gene interactions.
Main Methods:
- Developed FUN-PROSE, a deep learning model.
- Trained and tested the model on three fungal species.
- Interpreted the model using sequence motif extraction and feature importance analysis.
Main Results:
- Achieved high correlation (up to 0.85) between predicted and observed condition-specific gene expression.
- Identified promoter sequence motifs associated with variable gene expression.
- Linked specific transcription factors to their gene targets, uncovering known and novel interactions.
Conclusions:
- FUN-PROSE accurately predicts condition-specific gene expression in fungi.
- Model interpretation reveals biologically relevant sequence motifs and transcription factor-gene interactions.
- The findings support advancements in fungal metabolic engineering and regulatory network discovery.
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