OligoWalk

OligoWalk predicts thermodynamically and sequence-informed siRNA candidates targeting specific mRNA sequences for RNA interference experiments.

Key Features:

• Candidate generation and ranking: Generates lists of candidate siRNA sequences and ranks them by predicted silencing efficiency based on computed metrics.
• Thermodynamic calculations: Calculates free energy changes associated with siRNA–mRNA hybridization while accounting for siRNA and mRNA self-structures.
• Partition function calculation: Employs partition function calculations to estimate ensemble free energies of RNA structures for more precise predictions.
• Alternative structure options: Supports evaluation using lowest free energy structure predictions and suboptimal structure ensembles for comparative analysis.
• Machine learning integration: Applies a support vector machine (SVM) that integrates predicted free energy changes and local sequence features to select siRNA candidates.
• Reported predictive performance: Achieves a reported average effectiveness of 78.6% for selected siRNAs in target silencing.

Scientific Applications:

• RNA interference experiments: Design and selection of siRNAs for RNAi-based gene knockdown studies targeting specific mRNA sequences.
• siRNA efficacy prediction: Prioritization of siRNA candidates using thermodynamic stability and sequence-feature criteria for experimental validation.

Methodology:

Computes hybridization free energies using partition function calculations while considering siRNA and mRNA self-structures, offers lowest-free-energy and suboptimal structure prediction options, and applies a support vector machine that uses predicted free energy changes and local sequence features to select siRNA candidates.