The application of nucleic acid interaction structure prediction
| dc.contributor.author | Newman, Tara | |
| dc.contributor.supervisor | Jabbari, Hosna | |
| dc.date.accessioned | 2022-08-26T22:54:22Z | |
| dc.date.available | 2022-08-26T22:54:22Z | |
| dc.date.copyright | 2022 | en_US |
| dc.date.issued | 2022-08-26 | |
| dc.degree.department | Department of Computer Science | en_US |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | 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. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.bibliographicCitation | Newman, T., Chang, H.F.K., Jabbari, H. In silico prediction of COVID-19 test efficiency with DinoKnot. IEEE 9th International Conference on Healthcare Informatics (ICHI), 2021, pp. 470-479, doi: 10.1109/ICHI52183.2021.00082. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/14142 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Nucleic acid structure | en_US |
| dc.subject | Pseudoknot | en_US |
| dc.subject | DNA/RNA interaction | en_US |
| dc.subject | RNA/RNA interaction | en_US |
| dc.subject | RNA/DNA hetero-dimers | en_US |
| dc.subject | homo-dimers | en_US |
| dc.title | The application of nucleic acid interaction structure prediction | en_US |
| dc.type | Thesis | en_US |