Annotation-Based Clustering Reveals Functional Transcript Modules Involved in Stress Response and Metabolic Pathways in Cinnamomum
Abstract
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.
Keywords
Kaynakça
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Ayrıntılar
Birincil Dil
İngilizce
Konular
Bitki Biyoteknolojisi
Bölüm
Araştırma Makalesi
Erken Görünüm Tarihi
11 Nisan 2026
Yayımlanma Tarihi
-
Gönderilme Tarihi
17 Eylül 2025
Kabul Tarihi
26 Ocak 2026
Yayımlandığı Sayı
Yıl 2026 Sayı: Advanced Online Publication
