Comparative Transcriptomics of Floral Transition in Arabidopsis thalianaand Ortholog Prediction in Salvia hispanica

The transition from vegetative growth to reproductive development is a critical phase in the plant life cycle, shaping reproductive success and yield through coordinated responses to internal cues and environmental signals. Here, we integrated comparative transcriptomics and unsupervised machine learning to analyze time-series RNA-seq data from Arabidopsis thaliana and to prioritize flowering-time regulators in Salvia hispanica (chia). K-means clustering of Arabidopsis expression profiles recovered clusters enriched in transposable elements (e.g., AT5G28627) and pseudogenes (e.g., AT2G24110) that peaked during the flowering transition (M4–M6), suggesting participation in flowering-associated regulatory programs. Temperature-stratified profiles (Col10, Col16, Col23, Col27) revealed condition-dependent dynamics among canonical flowering regulators. Using cross-species expression correlation, we identified three high-confidence chia candidates for key Arabidopsis flowering genes (FT, FLC, and SOC1), with Pearson correlation coefficients ranging from 0.9943 to 0.9992. Together, these results provide a data-driven shortlist of candidate flowering-time regulators in chia and a reusable computational framework for transferring flowering knowledge from model to non-model plants.