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Documents authored by Gröpl, Clemens

Document
DOI: 10.4230/DagSemProc.05471.13
OpenMS - A Framework for Quantitative HPLC/MS-Based Proteomics

Authors: Knut Reinert, Oliver Kohlbacher, Clemens Gröpl, Eva Lange, Ole Schulz-Trieglaff, Marc Sturm, and Nico Pfeifer

Published in: Dagstuhl Seminar Proceedings, Volume 5471, Computational Proteomics (2006)

Abstract
In the talk we describe the freely available software library OpenMS which is currently under development at the Freie Universität Berlin and the Eberhardt-Karls Universität Tübingen. We give an overview of the goals and problems in differential proteomics with HPLC and then describe in detail the implemented approaches for signal processing, peak detection and data reduction currently employed in OpenMS. After this we describe methods to identify the differential expression of peptides and propose strategies to avoid MS/MS identification of peptides of interest. We give an overview of the capabilities and design principles of OpenMS and demonstrate its ease of use. Finally we describe projects in which OpenMS will be or was already deployed and thereby demonstrate its versatility.
Cite as

Knut Reinert, Oliver Kohlbacher, Clemens Gröpl, Eva Lange, Ole Schulz-Trieglaff, Marc Sturm, and Nico Pfeifer. OpenMS - A Framework for Quantitative HPLC/MS-Based Proteomics. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{reinert_et_al:DagSemProc.05471.13,
  author =	{Reinert, Knut and Kohlbacher, Oliver and Gr\"{o}pl, Clemens and Lange, Eva and Schulz-Trieglaff, Ole and Sturm, Marc and Pfeifer, Nico},
  title =	{{OpenMS - A Framework for Quantitative HPLC/MS-Based Proteomics}},
  booktitle =	{Computational Proteomics},
  pages =	{1--7},
  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/entities/document/10.4230/DagSemProc.05471.13},
  URN =		{urn:nbn:de:0030-drops-5463},
  doi =		{10.4230/DagSemProc.05471.13},
  annote =	{Keywords: Proteomics, C++, Differential expression}
}
Document
DOI: 10.4230/DagSemProc.05471.4
An Algorithm for Feature Finding in LC/MS Raw Data

Authors: Clemens Gröpl

Published in: Dagstuhl Seminar Proceedings, Volume 5471, Computational Proteomics (2006)

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)
Cite as

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)

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@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/entities/document/10.4230/DagSemProc.05471.4},
  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}
}
Document
DOI: 10.4230/DagSemProc.05471.9
High-accuracy peak picking of proteomics data

Authors: Eva Lange, Clemens Gröpl, Oliver Kohlbacher, and Andreas Hildebrandt

Published in: Dagstuhl Seminar Proceedings, Volume 5471, Computational Proteomics (2006)

Abstract
A new peak picking algorithm for the analysis of mass spectrometric (MS) data is presented. It is independent of the underlying machine or ionization method, and is able to resolve highly convoluted and asymmetric signals. The method uses the multiscale nature of spectrometric data by first detecting the mass peaks in the wavelet-transformed signal before a given asymmetric peak function is fitted to the raw data. In an optional third stage, the resulting fit can be further improved using techniques from nonlinear optimization. In contrast to currently established techniques (e.g. SNAP, Apex) our algorithm is able to separate overlapping peaks of multiply charged peptides in ESI-MS data of low resolution. Its improved accuracy with respect to peak positions makes it a valuable preprocessing method for MS-based identification and quantification experiments. The method has been validated on a number of different annotated test cases, where it compares favorably in both runtime and accuracy with currently established techniques. An implementation of the algorithm is freely available in our open source framework OpenMS (www.open-ms.de).
Cite as

Eva Lange, Clemens Gröpl, Oliver Kohlbacher, and Andreas Hildebrandt. High-accuracy peak picking of proteomics data. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{lange_et_al:DagSemProc.05471.9,
  author =	{Lange, Eva and Gr\"{o}pl, Clemens and Kohlbacher, Oliver and Hildebrandt, Andreas},
  title =	{{High-accuracy peak picking of proteomics 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/entities/document/10.4230/DagSemProc.05471.9},
  URN =		{urn:nbn:de:0030-drops-5358},
  doi =		{10.4230/DagSemProc.05471.9},
  annote =	{Keywords: Mass spectrometry, peak detection, peak picking}
}
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