Graduate Thesis Or Dissertation


ThreshKnot: Thresholded ProbKnot for Improved RNA Secondary Structure Prediction Public Deposited

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  • RNA structure prediction is a challenging problem, especially with pseudoknots. Recently, there has been a shift from the classical minimum free energy-based methods (MFE) to partition function-based ones that assemble structures based on base-pairing probabilities. Two typical examples of the latter group are the popular maximum expected accuracy (MEA) method and the ProbKnot method. ProbKnot is fast heuristic that pairs nucleotides that are reciprocally most probable pairing partners, and unlike MEA, can also predict structures with pseudoknots. However, ProbKnot’s full potential has been largely overlooked. In particular, when introduced, it did not have an MEA-like hyperparameter that can balance between positive predictive value (PPV) and sensitivity. We show that a simple thresholded version of ProbKnot, which we call ThreshKnot, leads to more accurate overall predictions by filtering out unlikely pairs whose probability falls under a given threshold. We also show that on three widely-used folding engines (RNAstructure, Vienna RNAfold, and CONTRAfold), ThreshKnot always outperforms the much more involved MEA algorithm in structure prediction accuracy, in its capability to predict pseudoknots, and in its faster running time. This suggests that ThreshKnot should replace MEA as the default partition function-based structure prediction algorithm.
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  • Ongoing Research
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  • 2019-12-13 to 2021-01-19



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