Mining characteristic relations bind to RNA secondary structures

Abstract

The identification of RNA secondary structures has been among the most exciting recent developments in biology and medical science. It has been recognized that there is an abundance of functional structures with frameshifting, regulation of translation, and splicing functions. However, the inherent signal for secondary structures is weak and generally not straightforward due to complex interleaving substrings. This makes it difficult to explore their potential functions from various structure data. Our approach, based on a collection of predicted RNA secondary structures, allows us to efficiently capture interesting characteristic relations in RNA and bring out the top-ranked rules for specified association groups. Our results not only point to a number of interesting associations and include a brief biological interpretation to them. It assists biologists in sorting out the most significant characteristic structure patterns and predicting structurefunction relationships in RNA