Applying Log-Normal Peak Fitting to Parallel Reaction Monitoring Data Analysis.

Area of Science:

• Proteomics
• Analytical Chemistry
• Computational Biology

Background:

• Chromatographic separation is crucial for mass-spectrometry-based proteomics, providing analytical features like retention time.
• Automated peak picking in chromatographic data analysis can be challenging due to peak shape aberrations, necessitating manual review.

Purpose of the Study:

• To introduce Peakfit, a software tool designed to fit chromatographic data to the log-normal peak equation.
• To provide an automated solution for objective and reproducible chromatographic peak analysis and outlier detection.

Main Methods:

• Developed a software tool named Peakfit using the R programming language.
• Implemented fitting of acquired chromatographic data to the log-normal peak equation.
• Integrated Peakfit with Skyline, a popular software for parallel reaction monitoring applications.

Main Results:

• Peakfit successfully fits chromatographic data to the log-normal peak equation, reporting calculated peak parameters.
• The software can process large datasets (>10,000 peaks) and identify outliers in peak boundary selection.
• Demonstrated objective and reproducible detection and resolution of problematic peak-picking situations using an example dataset (PXD026875).

Conclusions:

• Peakfit offers a robust solution for characterizing chromatographic peaks in proteomic analysis.
• The tool enhances the efficiency and accuracy of quantitative analysis by addressing challenges in automated peak picking.
• Peakfit facilitates objective and reproducible data interpretation, supporting complex proteomic workflows.