Large-scale frequent stem pattern mining in RNA families

作     者：Chiu, Jimmy Ka Ho Dillon, Tharam S. Chen, Yi-Ping Phoebe 

作者机构：La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3086 Australia Natl Univ Singapore Sch Publ Hlth Singapore Singapore 

出 版 物：《JOURNAL OF THEORETICAL BIOLOGY》 (理论生物学杂志)

年 卷 期：2018年第455卷

页      面：131-139页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 

主　　题：RNA pseudoknot NcRNA Secondary structure Topology Algorithm design and analysis RNA Benchmark testing 

摘      要：Functionally similar non-coding RNAs are expected to be similar in certain regions of their secondary structures. These similar regions are called common structure motifs, and are structurally conserved throughout evolution to maintain their functional roles. Common structure motif identification is one of the critical tasks in RNA secondary structure analysis. Nevertheless, current approaches suffer several limitations, and/or do not scale with both structure size and the number of input secondary structures. In this work, we present a method to transform the conserved base pair stems into transaction items and apply frequent itemset mining to identify common structure motifs existing in a majority of input structures. Our experimental results on telomerase and ribosomal RNA secondary structures report frequent stem patterns that are of biological significance. Moreover, the algorithms utilized in our method are scalable and frequent stem patterns can be identified efficiently among many large structures. (C) 2018 Elsevier Ltd. All rights reserved.