Global or local? Predicting secondary structure and accessibility in mRNAs

Determining the structural properties of mRNA is key to understanding vital post-transcriptional processes. As experimental data on mRNA struc-ture are scarce, accurate structure prediction is required to characterize RNA regulatory mechan-isms. Although various structure prediction approaches are available, it is often unclear which to choose and how to set their parameters. Furthermore, no standard measure to compare pre-dictions of local structure exists. We assessed the performance of different methods using two types of data: transcriptome-wide enzymatic probing information and a large, curated set of cis-regula-tory elements. To compare the approaches, we introduced structure accuracy, a measure that is applicable to both global and local methods. Our results showed that local folding was more accurate than the classic global approach. We investigated how the locality parameters, maximum base pair span and window size, influe-nced the prediction performance. A span of 150 provided a reasonable balance between maximizing the number of accurately predicted base pairs, while minimizing effects of incorrect long-range predic-tions. We characterized the error at artificial sequence ends, which we reduced by setting the window size sufficiently greater than the max-imum span. Our method, LocalFold, diminished all border effects and produced the most robust performance.