Correcting the retention time variation and measuring the similarity of time series is one of the most popular challenges in the area of analyzing capillary electrophoresis (CE) data. In this study, an automated signal alignment method is proposed by modifying the 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 the analysis of CE data from a selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) evaluation of the RNA secondary structure. The time-shift problem is one of the main components in the analysis of the SHAPE data. The accuracy and execution time of the algorithm are illustrated with experimental results obtained by applying to different types of data. The experimental results show that the signal alignment algorithm efficiently corrects the retention time variation. The developed tools can be readily adapted for the analysis of other biological datasets or time series.