Akademik Veri Yönetim Sistemi
KSU TARIM VE DOGA DERGISI-KSU JOURNAL OF AGRICULTURE AND NATURE, cilt.29, sa.6, ss.1411-1418, 2026 (ESCI, TRDizin)
Deciphering the functional architecture of the transcriptome is essential for understanding molecular regulation and biological complexity, particularly in non-model plant species. Most transcriptomic studies rely primarily on expression-level comparisons, which may overlook higher-order functional organization. Here, we aim to identify functionally coherent transcript modules in Cinnamomum species using an annotation-driven unsupervised clustering framework. Transcripts were characterized using Gene Ontology (GO) terms, KEGG pathways, protein domains, and BRITE classifications, followed by principal component analysis (PCA) for dimensionality reduction and K-means clustering (k = 4). The resulting clusters exhibited distinct functional profiles, including modules enriched in stress response, signal transduction, primary metabolism, and secondary metabolite biosynthesis. KEGG pathway enrichment analysis further supported the biological relevance of these modules, highlighting metabolic specialization and adaptive strategies. Overall, this study demonstrates that annotation-based unsupervised clustering provides an effective alternative to expression-only analyses for revealing biologically meaningful transcript organization in Cinnamomum, with implications for plant functional genomics and chemical biology.