Work

Can deep learning predict inducibility of stress-responsive gene expression from DNA sequence in Arabidopsis thaliana?

Year: 2026

Type: article

Abstract: Noncoding regions mediate transcriptional adaptation to stress in plants, yet the genomic determinants driving gene expression responses remain poorly understood. Here, we evaluate the ability of two deep learning approaches, a convolutional neural network (CNN) and a transformer-based genomic language model pre-trained on plant genomes to predict changes in gene expression under various abiotic and biotic stress conditions. Using RNA-seq time-series data from Arabidopsis thaliana, we explored different strategies for summarizing expression dynamics to capture treatment-specific transcriptional changes relative to control conditions. Both models achieved low to moderate predictive performance, with pattern-triggered-immunity related treatments showing the strongest sequence-based predictability. While extending promoter regions upstream had a limited impact, including coding sequences significantly improved performance. Model interpretation revealed that the CNN recovered sequence features comparable to those identified by simple 6-mer based linear models, suggesting limited gains in regulatory insight from increased model complexity. These findings underscore both the promise and limitations of sequence-based models in uncovering the regulatory logic of induced plant stress responses.

Source: in silico Plants

DOI: 10.1093/insilicoplants/diag012

Authors: Jordi Alonso Esteve, Niels Aerts, Saskia C M van Wees, Dick de Ridder, Aalt D J van Dijk

Institutions: University of Amsterdam, Utrecht University, Wageningen University & Research

Language: English

Topic: Genomics and Chromatin Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $2,297