Analysis of RNA nearest neighbor parameters reveals interdependencies and quantifies the uncertainty in RNA secondary structure prediction
| Autor: | Iain Mcfadyen, David M. Mauger, Cabral Bj, David H. Mathews, Jeffrey Zuber |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
RNA Folding 030102 biochemistry & molecular biology RNA Perturbation (astronomy) Biology Article k-nearest neighbors algorithm Thermodynamic model 03 medical and health sciences 030104 developmental biology Rna structure prediction Prediction methods Nucleic Acid Conformation Thermodynamics Base Pairing Molecular Biology Algorithm Protein secondary structure Rna secondary structure prediction |
| Zdroj: | RNA. 24:1568-1582 |
| ISSN: | 1469-9001 1355-8382 |
| DOI: | 10.1261/rna.065102.117 |
| Popis: | RNA secondary structure prediction is often used to develop hypotheses about structure-function relationships for newly discovered RNA sequences, to identify unknown functional RNAs, and to design sequences. Secondary structure prediction methods typically use a thermodynamic model that estimates the free energy change of possible structures based on a set of nearest neighbor parameters. These parameters were derived from optical melting experiments of small model oligonucleotides. This work aims to better understand the precision of structure prediction. Here, the experimental errors in optical melting experiments were propagated to errors in the derived nearest neighbor parameter values and then to errors in RNA secondary structure prediction. To perform this analysis, the optical melting experimental values were systematically perturbed within the estimates of experimental error and alternative sets of nearest neighbor parameters were then derived from these error-bounded values. Secondary structure predictions using either the perturbed or reference parameter sets were then compared. This work demonstrated that the precision of RNA secondary structure prediction is more robust than suggested by previous work based on perturbation of the nearest neighbor parameters. This robustness is due to correlations between parameters. Additionally, this work identified weaknesses in the parameter derivation that makes accurate assessment of parameter uncertainty difficult. Considerations for experimental design are provided to mitigate these weaknesses are provided. |
| Databáze: | OpenAIRE |
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