title: Exploring Consensus RNA Substructural Patterns Using Subgraph Mining.
abstract: Frequently recurring RNA structural motifs play important roles in RNA folding process and interaction with other molecules. Traditional index-based and shape-based schemas are useful in modeling RNA secondary structures but ignore the structural discrepancy of individual RNA family member. Further, the in-depth analysis of underlying substructure pattern is insufficient due to varied and unnormalized substructure data. This prevents us from understanding RNAs functions and their inherent synergistic regulation networks. This article thus proposes a novel labeled graph-based algorithm RnaGraph to uncover frequently RNA substructure patterns. Attribute data and graph data are combined to characterize diverse substructures and their correlations, respectively. Further, a top-k graph pattern mining algorithm is developed to extract interesting substructure motifs by integrating frequency and similarity. The experimental results show that our methods assist in not only modelling complex RNA secondary structures but also identifying hidden but interesting RNA substructure patterns.
authors.forename: Qingfeng
authors.initials: Q
authors.lastname: Chen
authors.forename: Chaowang
authors.initials: C
authors.lastname: Lan
authors.forename: Baoshan
authors.initials: B
authors.lastname: Chen
authors.forename: Lusheng
authors.initials: L
authors.lastname: Wang
authors.forename: Jinyan
authors.initials: J
authors.lastname: Li
authors.forename: Chengqi
authors.initials: C
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D016384:Consensus Sequence; D057225:Data Mining; D006801:Humans; D009690:Nucleic Acid Conformation; D010363:Pattern Recognition, Automated / Q000379:methods*; D012313:RNA / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D012313:RNA