Abstract folding space analysis based on helices

RNA has many pivotal functions especially in the regulation of gene expression by ncRNAs. Identification of their structure is an important requirement for understanding their function. Structure prediction alone is often insufficient for this task, due to algorithmic problems, parameter inaccuracies, and biological peculiarities. Among the latter, there are base modifications, cotranscriptional folding leading to folding traps, and conformational switching as in the case of riboswitches. All these require more in-depth analysis of the folding space. The major drawback, which all methods have to cope with, is the exponential growth of the folding space. Therefore, methods are often limited in the sequence length they can analyze, or they make use of heuristics, sampling, or abstraction. Our approach adopts the abstraction strategy and remedies some problems of existing methods. We introduce a position-specific abstraction based on helices that we term helix index shapes, or hishapes for short. Utilizing a dynamic programming framework, we have implemented this abstraction in the program RNAHeliCes. Furthermore, we developed two hishape-based methods, one for energy barrier estimation, called HiPath, and one for abstract structure comparison, termed HiTed. We demonstrate the superior performance of HiPath compared to other existing methods and the competitive accuracy of HiTed. RNAHeliCes, together with HiPath and HiTed, are available for download at http://www.cyanolab.de/software/RNAHeliCes.htm.