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Faculty of Graduate Studies

Tara Newman

  • BSc (University of Victoria, 2021)
Notice of the Final Oral Examination for the Degree of Master of Science

Topic

The application of nucleic acid interaction structure prediction

Computer Science

Date & location

  • Tuesday, August 16, 2022

  • 8:30 A.M.

  • Virtual Defence

Reviewers

Supervisory Committee

  • Dr. Hosna Jabbari, Department of Computer Science, University of Victoria (Supervisor)

  • Dr. Ulrike Stege, Department of Computer Science, UVic (Member) 

External Examiner

  • Dr. Heather Buckley, Department of Civil Engineering, UVic 

Chair of Oral Examination

  • Dr. Lynneth Stuart-Hill, School of Exercise Science, Physical and Health Education, UVic

Abstract

Motivation: Understanding how nucleic acids interact is essential for understanding their function. Furthermore, controlling these interactions can allow us to detect diseases, create new therapeutics, and much more. During quantitative reverse-transcription polymerase chain reaction (qRT-PCR) testing it is essential for nucleic acids to interact as designed to ensure accurate test results. This is an important consideration during the detection of COVID-19, the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Results: I introduce the program DinoKnot (Duplex Interaction of Nucleic acids with pseudoKnots) that follows the hierarchical folding hypothesis to predict the secondary structure of two interacting nucleic acid strands (DNA/RNA) of similar or different type. DinoKnot is the first program that utilizes stable stems in both strands as a guide to find the structure of their interaction. Using DinoKnot, I predicted the interaction structure between the SARS-CoV-2 genome and nine reverse primers from qRT-PCR primer-probe sets. I compared these results to an existing tool and highlighted an example to showcase DinoKnot’s ability to predict pseudo-knotted structures. I investigated how mutations to the SARS-CoV-2 genome may affect the primer interaction and predicted three mutations that may prevent primer binding, reducing the ability for SARS-CoV-2 detection. Interaction structure results predicted by DinoKnot that showed disruption of primer binding were consistent with a clinical example showing detection issues due to mutations. DinoKnot has the potential to screen new SARS-CoV-2 variants for possible detection issues and support existing applications involving DNA/RNA interactions, such as miRNA target site prediction, by adding structural considerations to the interaction to elicit functional information.

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