KnotAli

KnotAli predicts pseudoknotted RNA secondary structures by integrating covariation analysis with thermodynamic minimum free energy calculations to improve structural inference from multiple sequence alignments.

Key Features:

• Hybrid Methodology: Combines covariation analysis with thermodynamic energy minimization (minimum free energy, MFE) to balance sequence conservation and energetic feasibility.
• Alignment Integration: Accepts multiple RNA sequence alignments as input and uses structural covariation from those alignments to inform predictions.
• Robustness to Alignment Quality: Demonstrates reduced dependency on initial alignment quality, maintaining predictive performance with suboptimal alignments.
• Comparative Performance: In studies across 10 RNA families with pseudoknotted and non-pseudoknotted reference structures, it outperformed three leading alignment-based programs in six families using MUSCLE alignments and seven families using MAFFT alignments.

Scientific Applications:

• Pseudoknotted RNA characterization: Enables analysis of pseudoknotted structures in ribosomal RNAs, viral genomes, and regulatory RNAs to inform RNA function, stability, and interactions.

Methodology:

Operates on multiple sequence alignments using covariation analysis to detect co-evolutionary signals and thermodynamic energy minimization (MFE) to predict stable secondary structure configurations.