A novel method for the identification of conserved structural patterns in RNA: From small scale to high-throughput applications

• Published in: Nucleic acids research Vol. 44; no. 18; pp. 8600 - 8609
• Main Authors: Pietrosanto, Marco, Mattei, Eugenio, Helmer-Citterich, Manuela, Ferrè, Fabrizio
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 14.10.2016
• Subjects: Algorithms; Animals; Biophysics - methods; Computational Biology; Databases, Nucleic Acid; ELAV-Like Protein 1 - metabolism; High-Throughput Nucleotide Sequencing - methods; Iron - pharmacology; Mice; Nucleic Acid Conformation; Nucleotide Motifs - genetics; Protein Binding - drug effects; Response Elements - genetics; RNA - chemistry; Sequence Analysis, RNA
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkw750

Functional RNA regions are often related to recurrent secondary structure patterns (or motifs), which can exert their role in several different ways, particularly in dictating the interaction with RNA-binding proteins, and acting in the regulation of a large number of cellular processes. Among the available motif-finding tools, the majority focuses on sequence patterns, sometimes including secondary structure as additional constraints to improve their performance. Nonetheless, secondary structures motifs may be concurrent to their sequence counterparts or even encode a stronger functional signal. Current methods for searching structural motifs generally require long pipelines and/or high computational efforts or previously aligned sequences. Here, we present BEAM (BEAr Motif finder), a novel method for structural motif discovery from a set of unaligned RNAs, taking advantage of a recently developed encoding for RNA secondary structure named BEAR (Brand nEw Alphabet for RNAs) and of evolutionary substitution rates of secondary structure elements. Tested in a varied set of scenarios, from small- to large-scale, BEAM is successful in retrieving structural motifs even in highly noisy data sets, such as those that can arise in CLIP-Seq or other high-throughput experiments.