| 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. |
