linc2function

linc2function predicts functional potential and annotates long noncoding RNAs (lncRNAs) by applying deep learning to integrated sequence, secondary-structure, interactome, and physicochemical features.

Key Features:

• Comprehensive Feature Evaluation: Evaluates sequence-based attributes, secondary-structure information, interactome data, and physicochemical properties to predict lncRNA potential of transcripts.
• Species-Agnostic Prediction: Operates without relying on reference genome-specific features and uses a minimal set of essential features to maintain performance across diverse species.
• Integration of Structural and Interactome Data: Incorporates secondary-structure information and interactome data reflecting lncRNA interaction sites with DNA, RNA, and protein complexes relevant to epigenetic regulation.
• Deep Learning Models: Employs deep learning methodologies for predictive modeling of lncRNA function and potential.
• High Predictive Performance: Demonstrates Area Under Curve (AUC) values exceeding 0.9 across various species.

Scientific Applications:

• Functional Annotation of lncRNAs: Assigns functional potential and supports annotation of previously uncharacterized lncRNA transcripts.
• Study of Regulatory Roles and Epigenetic Programming: Facilitates analysis of lncRNA-mediated regulatory mechanisms and interactions involved in epigenetic regulation.
• Cross-species Analysis in Non-model Organisms: Enables lncRNA prediction and functional inference in non-model species where reference-genome features may be unavailable.

Methodology:

Applies deep learning models trained on features explicitly including sequence-based attributes, secondary-structure information, interactome data, and physicochemical properties, and evaluates performance using AUC.