Frequent subgraph mining is a useful method for extracting biologically relevant patterns from a set of graphs or a single large graph. Here, the graph represents all possible RNA structures and interactions. Patterns that are significantly more frequent in this graph over a random graph are extracted. We hypothesize that these patterns are most likely to represent a biological mechanisms. The graph representation used is a directed dual graph, extended to handle intermolecular interactions. The graph is sampled for subgraphs, which are labeled using a canonical labeling method and counted. The resulting patterns are compared to those created from a randomized dataset and scored. The algorithm was applied to the mitochondrial genome of the kinetoplastid species Trypanosoma brucei. This species has a unique RNA editing mechanism that has been well studied, making it a good model organism to test RiboFSM. The most significant patterns contain two stem-loops, indicative of gRNA, and represent interactions of these structures with target mRNA.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/26296 |
| Date | January 2013 |
| Creators | Gawronski, Alexander |
| Contributors | Turcotte, Marcel |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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