Predicting transcriptional responses to cold stress across plant species
Although genome sequence assemblies are available for a growing number of plant species, gene expression responses to stimuli have been catalogued for only a subset of these species. Many genes show altered transcription patterns in response to abiotic stresses. However, orthologous genes in related species often exhibit different responses to a given stress. Accordingly, data on the regulation of gene expression in one species are not reliable predictors of orthologous gene responses in a related species. Here, we trained a supervised classification algorithm to identify genes that transcriptionally respond to cold stress. A model trained with only features calculated directly from genome assemblies exhibited only modest decreases in performance relative to models trained using genomic, chromatin, and evolution/diversity features. Models trained with data from one species successfully predicted which genes would respond to cold stress in other related species. Cross-species predictions remained accurate when training was performed in cold-sensitive species and predictions were performed in cold-tolerant species and vice versa. Models trained with data on gene expression in multiple species outperformed models trained with data from any single species. These results suggest that classifiers trained on stress data from well-studied species may suffice for predicting gene expression patterns in related, less-studied species with sequenced genomes.