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Ribofsm: Frequent Subgraph Mining For the Discovery of RNA Structures and Interactions

Resource type
Date created
2014
Authors/Contributors
Abstract
Frequent subgraph mining is a useful method for extracting meaningful 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 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, which has a unique RNA editing mechanism. The most significant patterns contain two stem-loops, indicative of gRNA, and represent interactions of these structures with target mRNA.
Document
Published as
Gawronski and Turcotte
BMC Bioinformatics
2014,
15
(Suppl 13):S2
http://www.biomedcentral.com/1471-2105/15/S13/S2
Publication title
BMC Bioinformatics
Document title
Ribofsm: Frequent Subgraph Mining For the Discovery of RNA Structures and Interactions
Date
2014
Volume
15
Copyright statement
Copyright is held by the author(s).
Scholarly level
Peer reviewed?
Yes
Language
English
Member of collection
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1471-2105-15-S13-S2.pdf 943.03 KB

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