New Algorithm for Aligning Biological Data

Abstract

The change in data processing conditions obtained from biological experiments, in particular, SHAPE (Selective 2′-Hydroxyl Acylation analyzed by the Extension Primer) technique, results in the time shift of the data which are in the form of signals. In this study, a SHAPE data alignment algorithm is proposed using a new pattern recognition approach based on the discrete-to-continuous transition of entities. The advantage of our algorithm lies in the ability to process the information concerned with a logarithmic complexity, therefore, powerful results have been obtained.