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MS Final Exam – Liang Zhang

ThreshKnot: Thresholded ProbKnot for Improved RNA Secondary Structure Prediction

RNA structure prediction is a challenging problem, especially with pseudoknots. Recently, there has been a general 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 lesser known ProbKnot method, where the latter is a much simpler and faster heuristic that, unlike MEA, can also predict structures with pseudoknots. However, ProbKnot's full potential has been largely overlooked. In particular, it does not have an MEA-like hyperparameter that can balance between 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 three aspects: (1) it can achieve better prediction accuracy than MEA, (2) it can predict pseudoknots that MEA can not, (3) it is much simpler to implement and runs much faster. This suggests that ThreshKnot should replace MEA as the default partition function-based structure prediction algorithm.

Major Advisor: Liang Huang
Committee: Prasad Tadepalli
Committee: Xiaoli Z. Fern
Committee: Weng-Keen Wong
GCR: Leonard Coop

Tuesday, November 26, 2019 at 2:00pm to 4:00pm

Kelley Engineering Center, 1007
110 SW Park Terrace, Corvallis, OR 97331

Event Type

Lecture or Presentation

Event Topic


Electrical Engineering and Computer Science
Contact Name

Calvin Hughes

Contact Email

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