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

Christoph Stingl, Theo M. Luider*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)


Chromatographic separation is often an important part of mass-spectrometry-based proteomic analysis. It reduces the complexity of the initial samples before they are introduced to mass-spectrometric detection and chromatographic characteristics (such as retention time) add analytical features to the analyte. The acquisition and analysis of chromatographic data are thus of great importance, and specialized software is used for the extraction of quantitative information in an efficient and optimized manner. However, occasionally, automatic peak picking and correct peak boundary setting is challenged by, for instance, aberration of peak shape, peak truncation, and peak tailing, and a manual review of a large number of peaks is frequently required. To support this part of the analysis, we present here a software tool, Peakfit, that fits acquired chromatographic data to the log-normal peak equation and reports the calculated peak parameters. The program is written in R and can easily be integrated into Skyline, a popular software packages that is frequently used for proteomic parallel reaction monitoring applications. The program is capable of processing large data sets (>10 000 peaks) and detecting sporadic outliers in peak boundary selection performed, for instance, in Skyline. In an example data set, available via ProteomeXchange with identifier PXD026875, we demonstrated the capability of the program to characterize chromatographic peaks and showed an example of its ability to objectively and reproducibly detect and solve problematic peak-picking situations.

Original languageEnglish
Pages (from-to)4186-4192
Number of pages7
JournalJournal of Proteome Research
Issue number8
Early online date14 Jul 2021
Publication statusPublished - 6 Aug 2021

Bibliographical note

Funding Information:
This work was financially supported by the Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek, Perspectief program P12-04 (13541).

Publisher Copyright:
© 2021 American Chemical Society.


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