License
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.05471.4
URN: urn:nbn:de:0030-drops-5341
URL: https://drops.dagstuhl.de/opus/volltexte/2006/534/
Go to the corresponding Portal


Gröpl, Clemens

An Algorithm for Feature Finding in LC/MS Raw Data

pdf-format:
05471.GroeplClemens.Paper.534.pdf (0.5 MB)


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)

BibTeX - Entry

@InProceedings{gropl:DagSemProc.05471.4,
  author =	{Gr\"{o}pl, Clemens},
  title =	{{An Algorithm for Feature Finding in LC/MS Raw Data}},
  booktitle =	{Computational Proteomics},
  pages =	{1--9},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2006/534},
  URN =		{urn:nbn:de:0030-drops-5341},
  doi =		{10.4230/DagSemProc.05471.4},
  annote =	{Keywords: Computational Proteomics, Quantitative Analysis, Liquid Chromatography, Mass Spectrometry, Algorithm, Software}
}

Keywords: Computational Proteomics, Quantitative Analysis, Liquid Chromatography, Mass Spectrometry, Algorithm, Software
Collection: 05471 - Computational Proteomics
Issue Date: 2006
Date of publication: 03.05.2006


DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI