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Article

Improving RNA Branching Predictions: Advances and Limitations

1
School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA
2
School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30308, USA
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Björn Voß
Genes 2021, 12(4), 469; https://doi.org/10.3390/genes12040469
Received: 5 February 2021 / Revised: 15 March 2021 / Accepted: 18 March 2021 / Published: 25 March 2021
(This article belongs to the Special Issue Algorithms and Workflows in RNA Bioinformatics)
Minimum free energy prediction of RNA secondary structures is based on the Nearest Neighbor Thermodynamics Model. While such predictions are typically good, the accuracy can vary widely even for short sequences, and the branching thermodynamics are an important factor in this variance. Recently, the simplest model for multiloop energetics—a linear function of the number of branches and unpaired nucleotides—was found to be the best. Subsequently, a parametric analysis demonstrated that per family accuracy can be improved by changing the weightings in this linear function. However, the extent of improvement was not known due to the ad hoc method used to find the new parameters. Here we develop a branch-and-bound algorithm that finds the set of optimal parameters with the highest average accuracy for a given set of sequences. Our analysis shows that the previous ad hoc parameters are nearly optimal for tRNA and 5S rRNA sequences on both training and testing sets. Moreover, cross-family improvement is possible but more difficult because competing parameter regions favor different families. The results also indicate that restricting the unpaired nucleotide penalty to small values is warranted. This reduction makes analyzing longer sequences using the present techniques more feasible. View Full-Text
Keywords: secondary structure; NNTM; multiloops; branching parameters secondary structure; NNTM; multiloops; branching parameters
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MDPI and ACS Style

Poznanović, S.; Wood, C.; Cloer, M.; Heitsch, C. Improving RNA Branching Predictions: Advances and Limitations. Genes 2021, 12, 469. https://doi.org/10.3390/genes12040469

AMA Style

Poznanović S, Wood C, Cloer M, Heitsch C. Improving RNA Branching Predictions: Advances and Limitations. Genes. 2021; 12(4):469. https://doi.org/10.3390/genes12040469

Chicago/Turabian Style

Poznanović, Svetlana, Carson Wood, Michael Cloer, and Christine Heitsch. 2021. "Improving RNA Branching Predictions: Advances and Limitations" Genes 12, no. 4: 469. https://doi.org/10.3390/genes12040469

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