F3UTER

F3UTER predicts previously unannotated 3' untranslated regions (3'UTRs) in the human genome to improve 3'UTR annotation and reveal RBP-mediated regulatory elements, with emphasis on synaptic genes and brain tissues.

Key Features:

• 3'UTR discovery: Identifies previously unannotated 3' untranslated regions (3'UTRs) in the human genome.
• Machine learning framework: Uses a machine learning-based framework to predict unannotated 3'UTRs.
• Feature integration: Integrates genomic and tissue-specific transcriptomic features for prediction.
• Empirical scope: Has identified unannotated 3'UTRs associated with 1,513 genes across 39 human tissues, with highest abundance in brain tissues.
• RBP motif analysis: Detects significant enrichment of RNA binding protein (RBP) motifs in newly discovered 3'UTRs, including motifs for TARDBP and RBFOX1.
• Synaptic gene associations: Links identified unannotated 3'UTRs to synaptic genes and neuronal functions relevant to neurological and neurodevelopmental diseases.

Scientific Applications:

• 3'UTR annotation expansion: Expands annotation of the human 3'UTR landscape by adding previously unannotated regions.
• mRNA–RBP interactome mapping: Reveals RBP motif enrichment patterns to inform studies of mRNA–RBP interactions, particularly in the brain.
• Neuronal regulatory mechanism investigation: Supports investigation of post-transcriptional regulatory mechanisms in synaptic genes and brain-related disorders.

Methodology:

Applies a machine learning-based framework that integrates genomic and tissue-specific transcriptomic features to predict unannotated 3'UTRs and performs motif enrichment analysis to detect enriched RBP motifs such as TARDBP and RBFOX1.