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Automated peak alignment for nucleic acid capillary electrophoresis data by dynamic programming

Automated capillary electrophoresis represents a powerful approach for high-throughput analysis of nucleic acid chemical probing experiments. Correcting time variation and measuring similarity of time-series data are major challenges in the automated analysis, however. Here we describe an automated peak alignment algorithm that incorporates a dynamic programming approach to align multiple-peak time series profiles. A new peak similarity function and other algorithmic features make possible rapid peak alignment and highly accurate comparisons of complex time-series datasets.