Motivation: Understanding how nucleic acids interact is essential for understanding their function. Controlling these interactions, for example, can allow us to detect diseases and create new therapeutics. During quantitative reverse-transcription polymerase chain reaction (qRT-PCR) testing, having nucleic acids interact as designed is essential for ensuring accurate test results. Accurate testing 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 introduced 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 RNAcofold and highlighted an example to showcase DinoKnot’s ability to predict pseudoknotted 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 pre- dicted 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 microRNA (miRNA) target site prediction, by adding structural considerations to the interaction to elicit functional information. / Graduate