SwiSpot: modeling riboswitches by spotting out switching sequences

• Published in: Bioinformatics (Oxford, England) Vol. 32; no. 21; pp. 3252 - 3259
• Main Authors: Barsacchi, Marco, Novoa, Eva Maria, Kellis, Manolis, Bechini, Alessio
• Format: Journal Article
• Language: English
• Published: England 01.11.2016
• Subjects: Bacteria; Ligands; Models, Genetic; Riboswitch; Sequence Analysis, RNA; Software
• DOI: 10.1093/bioinformatics/btw401
• ISSN: 1367-4803

Riboswitches are cis-regulatory elements in mRNA, mostly found in Bacteria, which exhibit two main secondary structure conformations. Although one of them prevents the gene from being expressed, the other conformation allows its expression, and this switching process is typically driven by the presence of a specific ligand. Although there are a handful of known riboswitches, our knowledge in this field has been greatly limited due to our inability to identify their alternate structures from their sequences. Indeed, current methods are not able to predict the presence of the two functionally distinct conformations just from the knowledge of the plain RNA nucleotide sequence. Whether this would be possible, for which cases, and what prediction accuracy can be achieved, are currently open questions. Here we show that the two alternate secondary structures of riboswitches can be accurately predicted once the 'switching sequence' of the riboswitch has been properly identified. The proposed SwiSpot approach is capable of identifying the switching sequence inside a putative, complete riboswitch sequence, on the basis of pairing behaviors, which are evaluated on proper sets of configurations. Moreover, it is able to model the switching behavior of riboswitches whose generated ensemble covers both alternate configurations. Beyond structural predictions, the approach can also be paired to homology-based riboswitch searches. SwiSpot software, along with the reference dataset files, is available at: http://www.iet.unipi.it/a.bechini/swispot/Supplementary information: Supplementary data are available at Bioinformatics online. a.bechini@ing.unipi.it.