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Documents authored by Kohlbacher, Oliver

Document
DOI: 10.4230/DagRep.5.8.9
Computational Mass Spectrometry (Dagstuhl Seminar 15351)

Authors: Rudolf Aebersold, Oliver Kohlbacher, and Olga Vitek

Published in: Dagstuhl Reports, Volume 5, Issue 8 (2016)

Abstract
Following in the steps of high-throughput sequencing, mass spectrometry (MS) has become established as a key analytical technique for large-scale studies of complex biological mixtures. MS-based experiments generate datasets of increasing complexity and size, and the rate of production of these datasets has exceeded Moore’s law. In recent years we have witnessed the growth of computational approaches to coping with this data deluge. The seminar 'Computational Mass Spectrometry' brought together mass spectrometrists, statisticians, computer scientists and biologists to discuss where the next set of computational and statistical challenges lie. The participants discussed emerging areas of research such as how to investigate questions in systems biology with the design and analysis of datasets both large in memory usage and number of features and include measurements from multiple ‘omics technologies.
Cite as

Rudolf Aebersold, Oliver Kohlbacher, and Olga Vitek. Computational Mass Spectrometry (Dagstuhl Seminar 15351). In Dagstuhl Reports, Volume 5, Issue 8, pp. 9-33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@Article{aebersold_et_al:DagRep.5.8.9,
  author =	{Aebersold, Rudolf and Kohlbacher, Oliver and Vitek, Olga},
  title =	{{Computational Mass Spectrometry (Dagstuhl Seminar 15351)}},
  pages =	{9--33},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2016},
  volume =	{5},
  number =	{8},
  editor =	{Aebersold, Rudolf and Kohlbacher, Oliver and Vitek, Olga},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.5.8.9},
  URN =		{urn:nbn:de:0030-drops-56765},
  doi =		{10.4230/DagRep.5.8.9},
  annote =	{Keywords: computational mass spectrometry, proteomics, metabolomics, bioinformatics}
}
Document
DOI: 10.4230/DagRep.3.12.1
Computational Mass Spectrometry (Dagstuhl Seminar 13491)

Authors: Ruedi Aebersbold, Oliver Kohlbacher, and Olga Vitek

Published in: Dagstuhl Reports, Volume 3, Issue 12 (2014)

Abstract
The last decade has brought tremendous technological advances in mass spectrometry, which in turn have enabled new applications of mass spectrometry in the life sciences. Proteomics, metabolomics, lipidomics, glycomics and related fields have gotten a massive boost, which also resulted in vastly increased amount of data produced and increased complexity of these data sets. An efficient and accurate analysis of these data sets has become the key bottleneck in the field. The seminar 'Computational Mass Spectrometry' brought together scientist from mass spetrometry and bioinformatics, from industry and academia to discuss the state of the art in computational mass spectrometry. The participants discussed a number of current topics, for example new and upcoming technologies, the challenges posed by new types of experiments, the challenges of the growing data volume ('big data'), or challenges for education in several working groups. The seminar reviewed the state of the art in computational mass spectrometry and summarized the upcoming challenges. The seminar also led to the creation of structures to support the computational mass spectrometry community (the formation of an ISCB Community of Interest and a HUPO subgroup on computational mass spectrometry). This community will also carry on with some of the efforts initiated during the seminar, in particular with the establishment of a computational mass spectrometry curriculum that was drafted in Dagstuhl.
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Ruedi Aebersbold, Oliver Kohlbacher, and Olga Vitek. Computational Mass Spectrometry (Dagstuhl Seminar 13491). In Dagstuhl Reports, Volume 3, Issue 12, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@Article{aebersbold_et_al:DagRep.3.12.1,
  author =	{Aebersbold, Ruedi and Kohlbacher, Oliver and Vitek, Olga},
  title =	{{Computational Mass Spectrometry (Dagstuhl Seminar 13491)}},
  pages =	{1--16},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2014},
  volume =	{3},
  number =	{12},
  editor =	{Aebersbold, Ruedi and Kohlbacher, Oliver and Vitek, Olga},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.3.12.1},
  URN =		{urn:nbn:de:0030-drops-45053},
  doi =		{10.4230/DagRep.3.12.1},
  annote =	{Keywords: computational mass spectrometry, proteomics, metabolomics, bioinformatics}
}
Document
DOI: 10.4230/DagSemProc.08101.1
08101 Abstracts Collection – Computational Proteomics

Authors: Knut Reinert, Christian Huber, Kathrin Marcus, Michal Linial, and Oliver Kohlbacher

Published in: Dagstuhl Seminar Proceedings, Volume 8101, Computational Proteomics (2008)

Abstract
The second Dagstuhl Seminar on emph{Computational Proteomics} took place from March 3rd to 7th, 2008 in Schloss Dagstuhl--Leibniz Center for Informatics. This highly international meeting brought together researchers from computer science and from proteomics to discuss the state of the art and future developments at the interface between experiment and theory. This interdisciplinary exchange covered a wide range of topics, from new experimental methods resulting in more complex data we will have to expect in the future to purely theoretical studies of what level of experimental accuracy is required in order to solve certain problems. A particular focus was also on the application side, where the participants discussed more complex experimental methodologies that are enabled by more sophisticated computational techniques. Quantitative aspects of protein expression analysis as well as posttranslational modifications in the context of disease development and diagnosis were discussed. The seminar sparked a number of new ideas and collaborations and has resulted in several joint grant applications and paper submissions. This paper describes the seminar topics, its goals and results. The executive summary is followed by the abstracts of the presentations given. Links to extended abstracts or full papers are provided, if available.
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Knut Reinert, Christian Huber, Kathrin Marcus, Michal Linial, and Oliver Kohlbacher. 08101 Abstracts Collection – Computational Proteomics. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 8101, pp. 1-34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{reinert_et_al:DagSemProc.08101.1,
  author =	{Reinert, Knut and Huber, Christian and Marcus, Kathrin and Linial, Michal and Kohlbacher, Oliver},
  title =	{{08101 Abstracts  Collection – Computational Proteomics}},
  booktitle =	{Computational Proteomics},
  pages =	{1--34},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8101},
  editor =	{Christian Huber and Oliver Kohlbacher and Michal Linial and Katrin Marcus 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.08101.1},
  URN =		{urn:nbn:de:0030-drops-17840},
  doi =		{10.4230/DagSemProc.08101.1},
  annote =	{Keywords: Bioinformatics, biomedicine, proteomics, analytical chemistry}
}
Document
DOI: 10.4230/DagSemProc.08191.1
08191 Abstracts Collection – Graph Drawing with Applications to Bioinformatics and Social Sciences

Authors: Stephen Borgatti, Stephen Kobourov, Oliver Kohlbacher, and Petra Mutzel

Published in: Dagstuhl Seminar Proceedings, Volume 8191, Graph Drawing with Applications to Bioinformatics and Social Sciences (2008)

Abstract
From May 4 to May 9, 2008, the Dagstuhl Seminar 08191 ``Graph Drawing with Applications to Bioinformatics and Social Sciences'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Stephen Borgatti, Stephen Kobourov, Oliver Kohlbacher, and Petra Mutzel. 08191 Abstracts Collection – Graph Drawing with Applications to Bioinformatics and Social Sciences. In Graph Drawing with Applications to Bioinformatics and Social Sciences. Dagstuhl Seminar Proceedings, Volume 8191, pp. 1-10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{borgatti_et_al:DagSemProc.08191.1,
  author =	{Borgatti, Stephen and Kobourov, Stephen and Kohlbacher, Oliver and Mutzel, Petra},
  title =	{{08191 Abstracts Collection – Graph Drawing with Applications to Bioinformatics and Social Sciences}},
  booktitle =	{Graph Drawing with Applications to Bioinformatics and Social Sciences},
  pages =	{1--10},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8191},
  editor =	{Stephen P. Borgatti and Stephen Kobourov and Oliver Kohlbacher and Petra Mutzel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08191.1},
  URN =		{urn:nbn:de:0030-drops-15547},
  doi =		{10.4230/DagSemProc.08191.1},
  annote =	{Keywords: Graph drawing, visualization, social sciences, bioinformatics}
}
Document
DOI: 10.4230/DagSemProc.08191.2
08191 Executive Summary – Graph Drawing with Applications to Bioinformatics and Social Sciences

Authors: Stephen Borgatti, Stephen Kobourov, Oliver Kohlbacher, and Petra Mutzel

Published in: Dagstuhl Seminar Proceedings, Volume 8191, Graph Drawing with Applications to Bioinformatics and Social Sciences (2008)

Abstract
Graph drawing deals with the problem of communicating the structure of relational data through diagrams, or drawings. The ability to represent relational information in a graphical form is a powerful tool which allows to perform analysis through visual exploration to find important patterns, trends, and correlations. Real-world applications such as bioinformatics and sociology pose challenges to the relational visualization because, e.g., semantic information carried by the diagram has to be used for obtaining meaningful layouts and application-specific drawing conventions need to be fulfilled. Moreover, the underlying data often stems from huge data bases, but only a small fraction shall be displayed at a time; the user interactively selects the data to be displayed and explores the graph by expanding interesting and collapsing irrelevant parts. This requires powerful graph exploration tools with navigation capabilities that allow dynamic adaption of the graph layout in real time. In this seminar we focused on the application of graph drawing in two important application domains: bioinformatics and social sciences. We brought together theoreticians and practitioners from these areas and focused on problems concerning interaction with and navigation in large and dynamic networks arising in these application areas; During the seminar, we identified and defined open graph drawing problems that are motivated by practical applications in the targeted application areas, tackled selected open problems, formulated the findings as a first step to the solution, and defined further research directions.
Cite as

Stephen Borgatti, Stephen Kobourov, Oliver Kohlbacher, and Petra Mutzel. 08191 Executive Summary – Graph Drawing with Applications to Bioinformatics and Social Sciences. In Graph Drawing with Applications to Bioinformatics and Social Sciences. Dagstuhl Seminar Proceedings, Volume 8191, pp. 1-3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{borgatti_et_al:DagSemProc.08191.2,
  author =	{Borgatti, Stephen and Kobourov, Stephen and Kohlbacher, Oliver and Mutzel, Petra},
  title =	{{08191 Executive Summary – Graph Drawing with Applications to Bioinformatics and Social Sciences}},
  booktitle =	{Graph Drawing with Applications to Bioinformatics and Social Sciences},
  pages =	{1--3},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8191},
  editor =	{Stephen P. Borgatti and Stephen Kobourov and Oliver Kohlbacher and Petra Mutzel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08191.2},
  URN =		{urn:nbn:de:0030-drops-15523},
  doi =		{10.4230/DagSemProc.08191.2},
  annote =	{Keywords: Graph drawing, visualization, social sciences, bioinformatics}
}
Document
DOI: 10.4230/DagSemProc.08191.6
08191 Working Group Summary – Visually Comparing a Set of Graphs

Authors: Mario Albrecht, Alejandro Estrella-Balderrama, Markus Geyer, Carsten Gutwenger, Karsten Klein, Oliver Kohlbacher, and Michael Schulz

Published in: Dagstuhl Seminar Proceedings, Volume 8191, Graph Drawing with Applications to Bioinformatics and Social Sciences (2008)

Abstract
We consider methods to visually compare graphs, more to focus on the differences of the graphs than on the similarities. Our two-level approach constructs a meaningful overview of the given graphs combined with a detailed view focusing on a local area of change. The actual layout of these graphs has to be evaluated depending on the specific type of biological network to be visualized in each case. We look into different variants and propose properties to be optimized in our visualizations.
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Mario Albrecht, Alejandro Estrella-Balderrama, Markus Geyer, Carsten Gutwenger, Karsten Klein, Oliver Kohlbacher, and Michael Schulz. 08191 Working Group Summary – Visually Comparing a Set of Graphs. In Graph Drawing with Applications to Bioinformatics and Social Sciences. Dagstuhl Seminar Proceedings, Volume 8191, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{albrecht_et_al:DagSemProc.08191.6,
  author =	{Albrecht, Mario and Estrella-Balderrama, Alejandro and Geyer, Markus and Gutwenger, Carsten and Klein, Karsten and Kohlbacher, Oliver and Schulz, Michael},
  title =	{{08191 Working Group Summary – Visually Comparing a Set of Graphs}},
  booktitle =	{Graph Drawing with Applications to Bioinformatics and Social Sciences},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8191},
  editor =	{Stephen P. Borgatti and Stephen Kobourov and Oliver Kohlbacher and Petra Mutzel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08191.6},
  URN =		{urn:nbn:de:0030-drops-15536},
  doi =		{10.4230/DagSemProc.08191.6},
  annote =	{Keywords: Graph drawing, visual graph comparison}
}
Document
DOI: 10.4230/DagSemProc.05471.3
A machine learning approach for prediction of DNA and peptide HPLC retention times

Authors: Marc Sturm, Sascha Quinten, Christian G. Huber, and Oliver Kohlbacher

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

Abstract
High performance liquid chromatography (HPLC) has become one of the most efficient methods for the separation of biomolecules. It is an important tool in DNA purification after synthesis as well as DNA quantification. In both cases the separability of different oligonucleotides is essential. The prediction of oligonucleotide retention times prior to the experiment may detect superimposed nucleotides and thereby help to avoid futile experiments. In 2002 Gilar et al. proposed a simple mathematical model for the prediction of DNA retention times, that reliably works at high temperatures only (at least 70°C). To cover a wider temperature rang we incorporated DNA secondary structure information in addition to base composition and length. We used support vector regression (SVR) for the model generation and retention time prediction. A similar problem arises in shotgun proteomics. Here HPLC coupled to a mass spectrometer (MS) is used to analyze complex peptide mixtures (thousands of peptides). Predicting peptide retention times can be used to validate tandem-MS peptide identifications made by search engines like SEQUEST. Recently several methods including multiple linear regression and artificial neural networks were proposed, but SVR has not been used so far.
Cite as

Marc Sturm, Sascha Quinten, Christian G. Huber, and Oliver Kohlbacher. A machine learning approach for prediction of DNA and peptide HPLC retention times. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{sturm_et_al:DagSemProc.05471.3,
  author =	{Sturm, Marc and Quinten, Sascha and Huber, Christian G. and Kohlbacher, Oliver},
  title =	{{A machine learning approach for prediction of DNA and peptide HPLC retention times}},
  booktitle =	{Computational Proteomics},
  pages =	{1--5},
  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.05471.3},
  URN =		{urn:nbn:de:0030-drops-5484},
  doi =		{10.4230/DagSemProc.05471.3},
  annote =	{Keywords: High performance liquid chromatography, mass spectrometry, retention time, prediction, peptide, DNA, support vector regression}
}
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.05471.1
05471 Abstract Collection – Computational Proteomics

Authors: Christian G. Huber, Oliver Kohlbacher, and Knut Reinert

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

Abstract
From 20.11.05 to 25.11.05, the Dagstuhl Seminar 05471 ``Computational Proteomics'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Christian G. Huber, Oliver Kohlbacher, and Knut Reinert. 05471 Abstract Collection – Computational Proteomics. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{huber_et_al:DagSemProc.05471.1,
  author =	{Huber, Christian G. and Kohlbacher, Oliver and Reinert, Knut},
  title =	{{05471 Abstract Collection – Computational Proteomics}},
  booktitle =	{Computational Proteomics},
  pages =	{1--15},
  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.05471.1},
  URN =		{urn:nbn:de:0030-drops-5569},
  doi =		{10.4230/DagSemProc.05471.1},
  annote =	{Keywords: Proteomics, mass spectrometry, MALDI, HPLC-MS, differential expression, clinical proteomics, quantitation, identification}
}
Document
DOI: 10.4230/DagSemProc.05471.2
05471 Executive Summary – Computational Proteomics

Authors: Christian G. Huber, Oliver Kohlbacher, and Knut Reinert

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

Abstract
The Dagstuhl Seminar on Computational Proteomics brought together researchers from computer science and from proteomics to discuss the state of the art and future developments at the interface between experiment and theory. This interdisciplinary exchange covered a wide range of topics, from new experimental methods resulting in more complex data we will have to expect in the future to purely theoretical studies of what level of experimental accuracy is required in order to solve certain problems. A particular focus was also on the application side, where the participants discussed more complex experimental methodologies that are enabled by more sophisticated computational techniques. Quantitative aspects of protein expression analysis as well as posttranslational modifications in the context of disease development and diagnosis were discussed. The seminar sparked a number of new ideas and collaborations and resulted in joint grant applications and publications.
Cite as

Christian G. Huber, Oliver Kohlbacher, and Knut Reinert. 05471 Executive Summary – Computational Proteomics. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{huber_et_al:DagSemProc.05471.2,
  author =	{Huber, Christian G. and Kohlbacher, Oliver and Reinert, Knut},
  title =	{{05471 Executive Summary – Computational Proteomics}},
  booktitle =	{Computational Proteomics},
  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.05471.2},
  URN =		{urn:nbn:de:0030-drops-5406},
  doi =		{10.4230/DagSemProc.05471.2},
  annote =	{Keywords: Proteomics, mass spectrometry, MALDI, HPLC-MS, differential expression, clinical proteomics, quantitation, identification}
}
Document
DOI: 10.4230/DagSemProc.05471.6
Evaluation of LC-MS data for the absolute quantitative analysis of marker proteins

Authors: Nathanaël Delmotte, Bettina Mayr, Andreas Leinenbach, Knut Reinert, Oliver Kohlbacher, Christoph Klein, and Christian G. Huber

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

Abstract
The serum complexity makes the absolute quantitative analysis of medium to low-abundant proteins very challenging. Tens of thousands proteins are present in human serum and dispersed over an extremely wide dynamic range. The reliable identification and quantitation of proteins, which are potential biomarkers of disease, in serum or plasma as matrix still represents one of the most difficult analytical challenges. The difficulties arise from the presence of a few, but highly abundant proteins in serum and from the non-availability of isotope-labeled proteins, which serve to calibrate the method and to account for losses during sample preparation. For the absolute quantitation of serum proteins, we have developed an analytical scheme based on first-dimension separation of the intact proteins by anion-exchange high-performance liquid chromatography (HPLC), followed by proteolytic digestion and second-dimension separation of the tryptic peptides by reversed-phase HPLC in combination with electrospray ionization mass spectrometry (ESI-MS). The potential of mass spectrometric peptide identification in complex mixtures by means of peptide mass fingerprinting (PMF) and peptide fragment fingerprinting (PFF) was evaluated and compared utilizing synthetic mixtures of commercially available proteins and electrospray-ion trap- or electrospray time-of-flight mass spectrometers. While identification of peptides by PFF is fully supported by automated spectrum interpretation and database search routines, reliable identification by PMF still requires substantial efforts of manual calibration and careful data evaluation in order to avoid false positives. Quantitation of the identified peptides, however, is preferentially performed utilizing full-scan mass spectral data typical of PMF. Algorithmic solutions for PMF that incorporate both recalibration and automated feature finding on the basis of peak elution profiles and isotopic patterns are therefore highly desirable in order to speed up the process of data evaluation and calculation of quantitative results. Calibration for quantitative analysis of serum proteins was performed upon addition of known amounts of authentic protein to the serum sample. This was essential for the analysis of human serum samples, for which isotope-labeled protein standards are usually not available. We present the application of multidimensional HPLC-ESI-MS to the absolute quantitative analysis of myoglobin in human serum, a very sensitive biomarker for myocardial infarction. It was possible to determine myoglobin concentrations in human serum down to 100-500 ng/mL. Calibration graphs were linear over at least one order of magnitude and the relative standard deviation of the method ranged from 7-15%.
Cite as

Nathanaël Delmotte, Bettina Mayr, Andreas Leinenbach, Knut Reinert, Oliver Kohlbacher, Christoph Klein, and Christian G. Huber. Evaluation of LC-MS data for the absolute quantitative analysis of marker proteins. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{delmotte_et_al:DagSemProc.05471.6,
  author =	{Delmotte, Nathana\"{e}l and Mayr, Bettina and Leinenbach, Andreas and Reinert, Knut and Kohlbacher, Oliver and Klein, Christoph and Huber, Christian G.},
  title =	{{Evaluation of LC-MS data for the absolute quantitative analysis of marker proteins}},
  booktitle =	{Computational Proteomics},
  pages =	{1--5},
  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.05471.6},
  URN =		{urn:nbn:de:0030-drops-5397},
  doi =		{10.4230/DagSemProc.05471.6},
  annote =	{Keywords: RP-HPLC, monolith, Mascot, Myoglobin, Absolute quantitation, Serum}
}
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)

Copy BibTex To Clipboard

@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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