Database compiles 7,139 TF-olfactory gene interactions, facilitating studies in sensory biology and genomics.
Introduction: Olfaction is a complex process governed by genes that are tightly regulated by Transcription Factors (TFs). Although a large number of human Olfactory Receptor (OR) genes have been identified, the regulatory mechanisms controlling their expression and function remain largely unknown. Understanding these regulatory networks is critical for studying human health, sensory perception, and disease mechanisms. Methods: We developed the Olfactory Receptor Transcription Regulation Database (ORTRD), a manually curated resource compiling 7,139 causal TF-olfactory gene interactions. Data were integrated from multiple sources, including TF binding information, gene expression profiles, and functional annotations. Each interaction was categorized according to its regulatory effect (activation or repression), using experimental evidence and computational inference. The database is stored in MySQL and designed for periodic updates as new validated data becomes available. Results: ORTRD contains detailed information on human OR genes, associated TFs, regulatory signs, log2 Fold Changes (log2FC), and TF binding motifs. Interactions can be visualized as signed directed graphs that represent activation or repression relationships within Gene Regulatory Networks (GRNs). Users can query the database to retrieve and download specific data subsets for customized analyses. Discussion: By combining experimentally validated interactions with functional annotations, ORTRD provides a high-coverage resource for interpreting GRNs in human olfaction. It allows researchers to explore transcriptional regulation with confidence and supports studies in genomics, physiology, and sensory biology. Conclusion: ORTRD is a comprehensive, user-friendly resource for studying transcriptional regulation of human olfactory receptors. It is publicly accessible at https://ortrd.iiita.ac.in/ and will be updated periodically to incorporate new validated data.
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Dubey et al. (2026) studied this question.
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