cpPredictor

cpPredictor predicts RNA secondary structure by using template-based methods that apply experimentally determined or predicted structures of related RNAs to improve prediction accuracy and assess reliability.

Key Features:

• Template-Based Prediction: Utilizes known RNA structures as templates to predict the secondary structure of query RNA sequences, improving predictions for RNAs that are difficult for conventional computational techniques.
• Conservation Analysis: Identifies conserved and unconserved subsequences and applies the template structure to conserved regions while using de novo predictions for regions with low evolutionary conservation.
• Reliability Assessment: Evaluates biological reliability of generated structures using z-scores as a quantitative confidence measure.
• Prediction of Difficult Structures: Targets RNA secondary structures that are challenging to resolve with existing methods.
• Characterization of Uncharacterized RNAs: Assesses whether uncharacterized RNAs are compatible or incompatible with a given template structure.
• Resolution of Ambiguities: Identifies the most relevant structure among multiple candidate structures for a single RNA.
• Validation and Performance: Validated against experimentally identified structures and demonstrates superior accuracy compared to classical prediction algorithms and constrained prediction methods across diverse heterogeneous RNAs.

Scientific Applications:

• Predicting difficult-to-resolve RNA structures: Provides secondary-structure models for RNAs that are poorly predicted by classical algorithms.
• Annotating uncharacterized RNAs: Determines compatibility with template structures to aid characterization and classification of novel RNAs.
• Disambiguating alternative structures: Selects the most relevant structure among multiple candidates to resolve prediction ambiguities.

Methodology:

Template-based prediction uses related RNA structures (experimentally determined or predicted); conservation analysis identifies conserved and unconserved subsequences and applies templates to conserved regions while performing de novo predictions for unconserved regions; reliability of generated structures is assessed using z-scores.