A peak alignment algorithm with novel improvements in application to electropherogram analysis.

Alignment of peaks in electropherograms or chromatograms obtained from experimental techniques such capillary electrophoresis remains a significant challenge. Accurate alignment is critical for accurate interpretation of various classes of nucleic acid analysis technologies, including conventional DNA sequencing and new RNA structure probing technologies. An automated alignment algorithm was developed based on dynamic programming to align multiple-peak time-series data both globally and locally. This algorithm relies on a new peak similarity measure and other features such as time penalties, global constraints, and minimum-similarity scores and results in rapid, highly accurate comparisons of complex time-series datasets. As a demonstrative case study, the developed algorithm was applied to analysis of capillary electrophoresis data from a Selective 2'-Hydroxyl Acylation analyzed by Primer Extension (SHAPE) evaluation of RNA secondary structure. The algorithm yielded robust analysis of challenging SHAPE probing data. Experimental results show that the peak alignment algorithm corrects retention time variation efficiently due to the presence of fluorescent tags on fragments and differences in capillaries. The tools can be readily adapted for the analysis other biological datasets in which peak retention times vary.