RNALfoldz: efficient prediction of thermodynamically stable, local secondary structures

Abstract

The search for local RNA secondary structures and the annotation of unusually stable folding regions in genomic sequences are two well motivated bioinformatic problems. In this contribution we introduce RNALfoldz an efficient solution two tackle both tasks. It is an extension of the RNALfold algorithm augmented by support vector regression for efficient calculation of a structure's thermodynamic stability. We demonstrate the applicability of this approach on the genome of E. coli and investigate a potential strategy to determine z-score cutoffs given a predefined false discovery rate.