RNAalifoldWS

RNAalifoldWS predicts consensus RNA secondary structures from aligned RNA sequences using minimum free energy calculations to support comparative and functional analyses of RNA.

Key Features:

• Enhanced Gap Handling: Introduces a rational approach to managing alignment gaps to improve structure prediction accuracy.
• Sophisticated Scoring Matrices: Replaces simplistic covariance scoring with RIBOSUM-like scoring matrices for a more nuanced evaluation of sequence covariation.
• Maintained Computational Efficiency: Preserves computational efficiency suitable for handling large datasets.
• Improved Predictive Accuracy: Empirical evaluations report higher accuracy than the original RNAalifold and favorable performance relative to stochastic context-free grammars (SCFGs), maximum expected accuracy methods, and hierarchical nearest neighbor classifiers.

Scientific Applications:

• Comparative Genomics: Infers conserved secondary structures across aligned RNA sequences for comparative analyses.
• Evolutionary Studies: Assesses structural conservation and covariation to inform evolutionary relationships.
• Functional Annotation of non-coding RNAs: Supports annotation by predicting consensus secondary structures of non-coding RNAs.

Methodology:

Computes minimum free energy consensus secondary structures for aligned RNA sequences using an updated algorithmic framework that integrates improved gap handling techniques and RIBOSUM-like scoring matrices.