Abstract
Correcting retention time variation and measuring similarity of signal obtained different source, is one of the most popular challenges in the area of bioinformatics. In this study, an automated signal alignment method is proposed by modifying Dynamic Time Warping (DTW) approach to align the time-series data. Preprocessing tools and further optimizations were developed to increase the performance of the algorithm. As a demonstrative case study, the developed algorithm is applied to analysis of capillary electrophoresis data from a Selective 2'-Hydroxyl Acylation analyzed by Primer Extension (SHAPE) evaluation of RNA secondary structure. Accuracy of the algorithm is illustrated with experimental results obtained by applying to different types of data. Experimental results show that the signal alignment algorithm corrects retention time variation efficiently. The developed tools can be readily adapted for the analysis of other biological datasets or time series.