DotAligner: identification and clustering of RNA structure motifs

Genome Biology

Stefan E. Seemann¹  Martin A. Smith²  John S. Mattick²  Xiu Cheng Quek² 

[1] Center for non-coding RNA in Technology and Health (RTH), University of Copenhagen;RNA Biology and Plasticity Group, Garvan Institute of Medical Research;

关键词: Functions of RNA structures;    RNA structure clustering;    Machine learning;    RNA–protein interactions;    Functional genome annotation;    Regulation by non-coding RNAs;   

DOI  :  10.1186/s13059-017-1371-3

来源: DOAJ

【 摘 要 】

Abstract The diversity of processed transcripts in eukaryotic genomes poses a challenge for the classification of their biological functions. Sparse sequence conservation in non-coding sequences and the unreliable nature of RNA structure predictions further exacerbate this conundrum. Here, we describe a computational method, DotAligner, for the unsupervised discovery and classification of homologous RNA structure motifs from a set of sequences of interest. Our approach outperforms comparable algorithms at clustering known RNA structure families, both in speed and accuracy. It identifies clusters of known and novel structure motifs from ENCODE immunoprecipitation data for 44 RNA-binding proteins.

【 授权许可】

Unknown