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Documents authored by Huber, Christian G.

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Huber, Christian G.

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.05471.1
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.05471.8
Glycosylation Patterns of Proteins Studied by Liquid Chromatography-Mass Spectrometry and Bioinformatic Tools

Authors: Hansjörg Toll, Peter Berger, Andreas Hofmann, Andreas Hildebrandt, Herbert Oberacher, Hans Peter Lenhof, and Christian G. Huber

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

Abstract
Due to their extensive structural heterogeneity, the elucidation of glycosylation patterns in glycoproteins such as the subunits of chorionic gonadotropin (CG), CG-alpha and CG-beta remains one of the most challenging problems in the proteomic analysis of posttranslational modifications. In consequence, glycosylation is usually studied after decomposition of the intact proteins to the proteolytic peptide level. However, by this approach all information about the combination of the different glycopeptides in the intact protein is lost. In this study we have, therefore, attempted to combine the results of glycan identification after tryptic digestion with molecular mass measurements on the intact glycoproteins. Despite the extremely high number of possible combinations of the glycans identified in the tryptic peptides by high-performance liquid chromatography-mass spectrometry (> 1000 for CG-alpha and > 10.000 for CG-beta), the mass spectra of intact CG-alpha and CG-beta revealed only a limited number of glycoforms present in CG preparations from pools of pregnancy urines. Peak annotations for CG-alpha were performed with the help of an algorithm that generates a database containing all possible modifications of the proteins (inclusive possible artificial modifications such as oxidation or truncation) and subsequent searches for combinations fitting the mass difference between the polypeptide backbone and the measured molecular masses. Fourteen different glycoforms of CG-alpha, including methionine-oxidized and N-terminally truncated forms, were readily identified. For CG-beta, however, the relatively high mass accuracy of ± 2 Da was still insufficient to unambiguously assign the possible combinations of posttranslational modifications. Finally, the mass spectrometric fingerprints of the intact molecules were shown to be very useful for the characterization of glycosylation patterns in different CG preparations.
Cite as

Hansjörg Toll, Peter Berger, Andreas Hofmann, Andreas Hildebrandt, Herbert Oberacher, Hans Peter Lenhof, and Christian G. Huber. Glycosylation Patterns of Proteins Studied by Liquid Chromatography-Mass Spectrometry and Bioinformatic Tools. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{toll_et_al:DagSemProc.05471.8,
  author =	{Toll, Hansj\"{o}rg and Berger, Peter and Hofmann, Andreas and Hildebrandt, Andreas and Oberacher, Herbert and Lenhof, Hans Peter and Huber, Christian G.},
  title =	{{Glycosylation Patterns of Proteins Studied by Liquid Chromatography-Mass Spectrometry and Bioinformatic Tools}},
  booktitle =	{Computational Proteomics},
  pages =	{1--6},
  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.05471.8},
  URN =		{urn:nbn:de:0030-drops-5431},
  doi =		{10.4230/DagSemProc.05471.8},
  annote =	{Keywords: Liquid chromatography, mass spectrometry, glycoproteins, glycosylation, peak annotation}
}
Document
DOI: 10.4230/DagSemProc.05471.11
MULTIDIMENSIONAL PEPTIDE/PROTEIN ANALYSIS AND IDENTIFICATION BY SEQUENCE DATABASE SEARCH USING MASS SPECTROMETRIC DATA

Authors: Christian Schley, Matthias Altmeyer, Rolf Müller, and Christian G. Huber

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

Abstract
In order to generate proteomics data that are suitable to validate protein identification in complex mixtures using multidimensional liquid-chromatography-mass spectrometry approaches, we implemented an offline two-dimensional liquid chromatography method combining strong cation-exchange- and ion-pair reversed-phase chromatography followed by electrospray ionization tandem mass spectrormetry (ESI-MS/MS) for the analysis of a bovine serum albumin digest. The fragment ion spectra generated by ESI-MS/MS were subsequently analyzed via MASCOT database search. The obtained identification data were evaluated in terms of quality of protein/peptide identification by means of score values, reproducibility of identification in replicate measurements, distribution of tryptic peptides among different fractions, and overall number of unique identified proteins/peptides. Finally, we improved the trapping conditions in the second dimension by using a more hydrophobic amphiphile in the loading buffer. The improvement was demonstrated by comparison of the obtained identification data, such as number of identified peptides, cumulative mowse scores and reproducibility of identification.
Cite as

Christian Schley, Matthias Altmeyer, Rolf Müller, and Christian G. Huber. MULTIDIMENSIONAL PEPTIDE/PROTEIN ANALYSIS AND IDENTIFICATION BY SEQUENCE DATABASE SEARCH USING MASS SPECTROMETRIC DATA. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{schley_et_al:DagSemProc.05471.11,
  author =	{Schley, Christian and Altmeyer, Matthias and M\"{u}ller, Rolf and Huber, Christian G.},
  title =	{{MULTIDIMENSIONAL PEPTIDE/PROTEIN ANALYSIS AND IDENTIFICATION BY SEQUENCE DATABASE SEARCH USING MASS SPECTROMETRIC DATA}},
  booktitle =	{Computational Proteomics},
  pages =	{1--8},
  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.05471.11},
  URN =		{urn:nbn:de:0030-drops-5389},
  doi =		{10.4230/DagSemProc.05471.11},
  annote =	{Keywords: Multidimensional chromatography, proteome analysis, monolithic column, tandem mass spectrometry}
}

Huber, Christian

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.
Cite as

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}
}
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