Interval-Based Similarity for Classifying Conserved RNA Secondary Structures

Abstract

The structure of a molecule is critical to determine its function in a biological context. Intelligent strategies are required to group structures with high similarity in an intuitive way. Most previous approaches focus on addressing sequence similarity and gene expression, whereas the techniques for comparative analysis of conserved structure data are underdeveloped because various secondary structures are complex, and most existing distance metrics have limitations. This article proposes a novel classification schema in terms of interval-based and weighted similarity functions that considers the intersection, non-intersection, and inclusion between two intervals of sequence size for conserved structures. The secondary structures are characterized by distance vectors. This assists in classifying structures under specific structure patterns that are expected to be correlated by functional or structural importance.