Cofolga: a genetic algorithm for finding the common folding of two RNAs

Area of Science:

• Computational Biology
• Bioinformatics
• Molecular Biology

Background:

• Accurate prediction of non-coding RNA secondary structures is essential for predicting RNA genes and functions from genomic sequences.
• Existing single-sequence folding programs like mfold have limitations, necessitating novel approaches for improved prediction performance.

Purpose of the Study:

• To propose and evaluate Cofolga, a novel secondary structure prediction method for RNA based on a genetic algorithm.
• To assess Cofolga's performance in simultaneously folding and aligning two homologous RNAs.

Main Methods:

• Development of Cofolga, a genetic algorithm-based program for simultaneous folding and alignment of two homologous RNAs.
• Testing Cofolga on diverse RNA datasets including tRNAs, 5S rRNAs, RNase P RNAs, and SRP RNAs.
• Comparison of Cofolga's prediction accuracy against single-sequence folding methods based on free-energy minimization.

Main Results:

• Cofolga achieved average prediction accuracies of 83.6% for tRNAs, 81.8% for 5S rRNAs, 73.5% for RNase P RNAs, and 67.7% for SRP RNAs.
• These accuracies significantly outperformed single-sequence folding methods, which yielded average accuracies of 52.4%, 47.4%, 57.7%, and 52.3% for the respective RNA types.
• Cofolga incorporates a post-processing step for single-sequence folding after common structure fixation, enabling prediction of structures unique to one RNA.

Conclusions:

• Cofolga demonstrates superior performance in predicting RNA secondary structures compared to traditional free-energy minimization methods.
• The simultaneous folding and alignment approach, coupled with post-processing, enhances prediction accuracy for homologous RNAs.
• Cofolga offers a valuable tool for RNA gene and function prediction, with executable files available for multiple platforms.