Evaluating our ability to predict the structural disruption of RNA by SNPs

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Single Nucleotide Polymorphisms (SNPs) in non-coding genomic regions can affect cellular regulation by altering transcribed Ribonucleic Acid (RNA) structure.
• These structural changes in RNA can lead to disease by disrupting normal cellular processes.

Purpose of the Study:

• To investigate the impact of Single Nucleotide Polymorphisms (SNPs) on Ribonucleic Acid (RNA) secondary structure.
• To benchmark computational algorithms for predicting SNPs that significantly alter RNA structure and evaluate metrics for ranking these structural changes.

Main Methods:

• A large-scale meta-analysis of Selective 2 eal-prime-Hydroxyl Acylation analyzed by Primer Extension (SHAPE) data was performed.
• The study benchmarked popular RNA structure prediction algorithms and evaluated metrics for quantifying structural changes caused by mutations.

Main Results:

• Several single point mutations were identified that significantly disrupt the secondary structure of five analyzed RNA transcripts.
• While no single algorithm/metric combination was dramatically superior, certain approaches showed better agreement with experimental data (SHAPE).
• The findings confirm that multiple single point mutations across RNA transcripts can significantly disrupt structure, aligning with computational predictions.

Conclusions:

• Every transcribed Ribonucleic Acid (RNA) has the potential to be a "RiboSNitch," where a Single Nucleotide Polymorphism (SNP) induces a large conformational change impacting regulatory function.
• Predicting the SNPs with the greatest effect on RNA structure remains a computational challenge, though progress has been made in algorithm performance and metric evaluation.