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The functions of a non-protein-coding RNA are often determined by its structure. Since experimental determination of RNA structure is time-consuming and expensive, its computational prediction is of great interest, and efficient solutions based on thermodynamic parameters are known. Frequently, however, the predicted minimum free energy structures are not the native ones, leading to the necessity of generating suboptimal solutions. While this can be accomplished by a number of programs, the user is often confronted with large outputs of similar structures, although he or she is interested in structures with more fundamental differences, or, in other words, with different abstract shapes. Here, we formalize the concept of abstract shapes and introduce their efficient computation. Each shape of an RNA molecule comprises a class of similar structures and has a representative structure of minimal free energy within the class. Shape analysis is implemented in the program RNAshapes. We applied RNAshapes to the prediction of optimal and suboptimal abstract shapes of several RNAs. For a given energy range, the number of shapes is considerably smaller than the number of structures, and in all cases, the native structures were among the top shape representatives.
A statistical model for transcription binding sites (TBS) in Caenorhabditis elegans was developed. The model is based on a position weight matrix (PWM), which has been used extensively in the discovery of new TBS. A TBS is a short stretch of DNA that can potentially act as a binding site. The model allows the researcher to search for TBS in the entire genome of C. elegans and sort the TBS by statistical preference and similarity. The model is available for download at: http://elegans.bcgsc.ca/tbs/ 827ec27edc