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Afaf Saaidi, Alain Laederach, Christine E. Heitsch (School of Mathematics, Georgia Institute of Technology)
The structure of RNA is a key to understanding many biological mechanisms, several methods aiming to predict the secondary structure of RNA using probing data have emerged but they are almost all relying on thermodynamic models. Probing mutation data contains a signal at many structural levels. We seek to go beyond searching for correlated base pairs to find correlated tuples belonging to the neighborhood of a possibly formed helix. We have developed ReDMaxH, a new method based on the use of DMS-MaP experimental data, resulting from sequencing, to directly deduce experimentally supported helices. Our method evaluates structural profiling by calculating the relative mutation differences on the Moore neighborhood of maximal helices. Given that mutations are context-dependent, Moore neighborhood seems to be a good approximation to capture correlated mutations that may figure beyond plausible canonical base pairs. It is therefore hoped that experimentally supported helices can be used to build native and above all alternative structures, and contribute to bypassing the shortcomings of thermodynamic models.