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An Algorithm for Feature Finding in LC/MS Raw Data

Author Clemens Gröpl



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Clemens Gröpl

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Clemens Gröpl. An Algorithm for Feature Finding in LC/MS Raw Data. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-9, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2006)
https://doi.org/10.4230/DagSemProc.05471.4

Abstract

Liquid chromatography coupled with mass spectrometry is an established method in shotgun proteomics. A key step in the data processing pipeline is to transform the raw data acquired by the mass spectrometer into a list of features. In this context, a emph{feature} is defined as the two-dimensional integration with respect to retention time (RT) and mass-over-charge (m/z) of the eluting signal belonging to a single charge variant of a measurand (e.g., a peptide). Features are characterized by attributes like average mass-to-charge ratio, centroid retention time, intensity, and quality. We present a new algorithm for feature finding which has been developed as a part of a combined experimental and algorithmic approach to absolutely quantify proteins from complex samples with unprecedented precision. The method was applied to the analysis of myoglobin in human blood serum, which is an important diagnostic marker for myocardial infarction. Our approach was able to determine the absolute amount of myoglobin in a serum sample through a series of standard addition experiments with a relative error of 2.5\%. It compares favorably to a manual analysis of the same data set since we could improve the precision and conduct the whole analysis pipeline in a small fraction of the time. We anticipate that our automatic quantitation method will facilitate further absolute or relative quantitation of even more complex peptide samples. The algorithm was implemented in the publicly available software framework OpenMS (www.OpenMS.de)
Keywords
  • Computational Proteomics
  • Quantitative Analysis
  • Liquid Chromatography
  • Mass Spectrometry
  • Algorithm
  • Software

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