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

Area of Science:

• Bioinformatics
• Molecular Biology
• Analytical Chemistry

Background:

• Peak alignment in electropherograms and chromatograms is a significant challenge.
• Accurate alignment is critical for interpreting nucleic acid analysis data, including DNA sequencing and RNA structure probing.

Purpose of the Study:

• To develop an automated alignment algorithm for multiple-peak time-series data.
• To improve the accuracy and speed of comparing complex time-series datasets.

Main Methods:

• Developed an automated alignment algorithm using dynamic programming.
• Incorporated a novel peak similarity measure, time penalties, global constraints, and minimum-similarity scores.
• Applied the algorithm to capillary electrophoresis data from Selective 2'-Hydroxyl Acylation analyzed by Primer Extension (SHAPE) experiments.

Main Results:

• The algorithm provides rapid and highly accurate global and local alignment of time-series data.
• Successfully applied to challenging SHAPE probing data for RNA secondary structure analysis.
• Demonstrated efficient correction of retention time variations caused by fluorescent tags and capillary differences.

Conclusions:

• The developed dynamic programming algorithm effectively aligns peaks in complex biological datasets.
• This tool offers robust analysis for techniques like SHAPE probing and can be adapted for other variable retention time datasets.