Secondary Structure Predict Ion for Circular Rnas

RNAs play an important role in bioinformatic applications. Their ability to serve not only as information carrier, but also to develop catalytic properties highlights them in the set of organic macromolecules notably. As these catalytic properties are closely related to the three-dimensional configuration (tertiary structure) of the RNA molecule, the formation and prediction of this tertiary structure a process called folding is a crucial bioinformatic problem. RNA folding is considered as a hierarchical process, where a secondary structure precedes the tertiary structure, whereas tertiary interactions are energetically weaker than those yielded by the secondary structure. Generally, the secondary structure does not change when tertiary interactions are formed. Because there are efficient methods for predicting the secondary structure of an RNA molecule under certain conditions, but none for the tertiary structure, the secondary structure is used as a first step for the prediction of functional properties of the RNA molecule. However, most methods for the analysis of secondary structure(s) are designed for linear RNAs exclusively. As catalytic active circular RNA molecules occur in nature too, it is necessary to extend these methods. Based on the scheme for a memory efficient extension of previously existing methods for linear RNAs, suggested by the group of Ivo Hofacker, four basic algorithms are introduced, extended and therefore made accessible for circular RNA molecules within this work.