BIOINFORMATICS INVESTIGATION OF RNA PSEUDOKNOTS

Pseudoknots are a special kind of RNA structures that play functional roles in a wide variety of biological processes. Pseudoknots are best known for their involvement in the −1 programed ribosomal frameshifting (−1 PRF) and stop codon readthrough translational recoding events as the stimulatory structures. In this dissertation, three large scale bioinformatics investigations were carried out on the roles of pseudoknots in the −1 PRF, as well as stop codon readthrough, recoding mechanisms in viral and human mRNAs. To meet the specific needs of the bioinformatics investigations, a new algorithm and method for the detection of RNA pseudoknots has been developed. The new approach differs from all existing pseudoknot detection programs in that it is capable of identifying all potential pseudoknots in any given RNA sequence with no length limitation, in a time efficient manner. This capability is essential for large scale applications in which large datasets of long RNA sequences are analyzed. The algorithm and method have been implemented, with different flavors, in three large scale sequence analysis investigations. The three datasets of mRNA sequences are: 1) full-length genomic mRNA sequences of all animal viruses known or expected to use the −1 PRF and stop codon readthrough recoding mechanisms for viral protein production; 2) full-length genomic mRNA sequences of 4000 plus different strains of human immunodeficiency virus type-1 (HIV-1); 3) 34,000 plus full-length human mRNA sequences. Results from systematic sequence analysis on these three datasets prove the usefulness and robustness of the newly developed pseudoknot detection approach. A large number of previously unknown potential pseudoknots were detected in the viral and human mRNA sequences under investigation. Post detection analysis leads to new mechanistic insights and hypotheses of pseudoknot dependent translational recoding. Some unifying themes of RNA pseudoknot structures in general are also uncovered. The results provide solid basis for further experimental and bioinformatics studies in the future.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Campus Only Dissertation

Discipline thesis:degree_discipline

Computer Science

Year

2017

Author dc:creator

• Huang, Xiaolan

Contributors dc:contributor

• Cheng, Qiang

• animal virus
• bioinformatics
• computational algorithm
• HIV-1
• human mRNA
• RNA pseudonots

Repository record dc:identifier

https://opensiuc.lib.siu.edu/dissertations/1463

OAI identifier oai:identifier

oai:opensiuc.lib.siu.edu:dissertations-2467