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Documents authored by Reinert, Knut

Document
Complete Volume
DOI: 10.4230/LIPIcs.WABI.2017
LIPIcs, Volume 88, WABI'17, Complete Volume

Authors: Russell Schwartz and Knut Reinert

Published in: LIPIcs, Volume 88, 17th International Workshop on Algorithms in Bioinformatics (WABI 2017)

Abstract
LIPIcs, Volume 88, WABI'17, Complete Volume
Cite as

17th International Workshop on Algorithms in Bioinformatics (WABI 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 88, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@Proceedings{schwartz_et_al:LIPIcs.WABI.2017,
  title =	{{LIPIcs, Volume 88, WABI'17, Complete Volume}},
  booktitle =	{17th International Workshop on Algorithms in Bioinformatics (WABI 2017)},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-050-7},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{88},
  editor =	{Schwartz, Russell and Reinert, Knut},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2017},
  URN =		{urn:nbn:de:0030-drops-78120},
  doi =		{10.4230/LIPIcs.WABI.2017},
  annote =	{Keywords: Nonnumerical Algorithms and Problems, Pattern Matching, Algorithms, Life and Medical Sciences}
}
Document
Front Matter
DOI: 10.4230/LIPIcs.WABI.2017.0
Front Matter, Table of Contents, Preface, List of Authors

Authors: Russell Schwartz and Knut Reinert

Published in: LIPIcs, Volume 88, 17th International Workshop on Algorithms in Bioinformatics (WABI 2017)

Abstract
Front Matter, Table of Contents, Preface, List of Authors
Cite as

17th International Workshop on Algorithms in Bioinformatics (WABI 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 88, pp. 0:i-0:xiv, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{schwartz_et_al:LIPIcs.WABI.2017.0,
  author =	{Schwartz, Russell and Reinert, Knut},
  title =	{{Front Matter, Table of Contents, Preface, List of Authors}},
  booktitle =	{17th International Workshop on Algorithms in Bioinformatics (WABI 2017)},
  pages =	{0:i--0:xiv},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-050-7},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{88},
  editor =	{Schwartz, Russell and Reinert, Knut},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2017.0},
  URN =		{urn:nbn:de:0030-drops-76348},
  doi =		{10.4230/LIPIcs.WABI.2017.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, List of Authors}
}
Document
DOI: 10.4230/LIPIcs.WABI.2017.13
Vaquita: Fast and Accurate Identification of Structural Variation Using Combined Evidence

Authors: Jongkyu Kim and Knut Reinert

Published in: LIPIcs, Volume 88, 17th International Workshop on Algorithms in Bioinformatics (WABI 2017)

Abstract
Motivation: Comprehensive identification of structural variations (SVs) is a crucial task for studying genetic diversity and diseases. However, it remains challenging. There is only a marginal consensus between different methods, and our understanding of SVs is substantially limited.In general, integration of multiple pieces of evidence including split-read, read-pair, soft-clip, and read-depth yields the best result regarding accuracy. However, doing this step by step is usually cumbersome and computationally expensive. Result: We present Vaquita, an accurate and fast tool for the identification of structural variations, which leverages all four types of evidence in a single program. After merging SVs from split-reads and discordant read-pairs, Vaquita realigns the soft-clipped reads to the selected regions using a fast bit-vector algorithm. Furthermore, it also considers the discrepancy of depth distribution around breakpoints using Kullback-Leibler divergence. Finally, Vaquita provides an additional metric for candidate selection based on voting, and also provides robust prioritization based on rank aggregation. We show that Vaquita is robust in terms of sequencing coverage, insertion size of the library, and read length, and is comparable or even better for the identification of deletions, inversions, duplications, and translocations than state-of-the-art tools, using both simulated and real datasets. In addition, Vaquita is more than eight times faster than any other tools in comparison. Availability: Vaquita is implemented in C++ using the SeqAn library. The source code is distributed under the BSD license and can be downloaded at http://github.com/seqan/vaquita
Cite as

Jongkyu Kim and Knut Reinert. Vaquita: Fast and Accurate Identification of Structural Variation Using Combined Evidence. In 17th International Workshop on Algorithms in Bioinformatics (WABI 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 88, pp. 13:1-13:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{kim_et_al:LIPIcs.WABI.2017.13,
  author =	{Kim, Jongkyu and Reinert, Knut},
  title =	{{Vaquita: Fast and Accurate Identification of Structural Variation Using Combined Evidence}},
  booktitle =	{17th International Workshop on Algorithms in Bioinformatics (WABI 2017)},
  pages =	{13:1--13:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-050-7},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{88},
  editor =	{Schwartz, Russell and Reinert, Knut},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2017.13},
  URN =		{urn:nbn:de:0030-drops-76352},
  doi =		{10.4230/LIPIcs.WABI.2017.13},
  annote =	{Keywords: Structural variation}
}
Document
DOI: 10.4230/DagRep.6.8.91
Next Generation Sequencing (Dagstuhl Seminar 16351)

Authors: Gene Myers, Mihai Pop, Knut Reinert, and Tandy Warnow

Published in: Dagstuhl Reports, Volume 6, Issue 8 (2017)

Abstract
Next Generation Sequencing (NGS) data have begun to appear in many applications that are clinically relevant, such as resequencing of cancer patients, disease-gene discovery and diagnostics for rare diseases, microbiome analyses, and gene expression profiling. The analysis of sequencing data is demanding because of the enormous data volume and the need for fast turnaround time, accuracy, reproducibility, and data security. This Dagstuhl Seminar aimed at a free and deep exchange of ideas and needs between the communities of algorithmicists and theoreticians and practitioners from the biomedical field. It identified several relevant fields such as data structures and algorithms for large data sets, hardware acceleration, new problems in the upcoming age of genomes, etc. which were discussed in breakout groups.
Cite as

Gene Myers, Mihai Pop, Knut Reinert, and Tandy Warnow. Next Generation Sequencing (Dagstuhl Seminar 16351). In Dagstuhl Reports, Volume 6, Issue 8, pp. 91-130, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@Article{myers_et_al:DagRep.6.8.91,
  author =	{Myers, Gene and Pop, Mihai and Reinert, Knut and Warnow, Tandy},
  title =	{{Next Generation Sequencing (Dagstuhl Seminar 16351)}},
  pages =	{91--130},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2017},
  volume =	{6},
  number =	{8},
  editor =	{Myers, Gene and Pop, Mihai and Reinert, Knut and Warnow, Tandy},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.6.8.91},
  URN =		{urn:nbn:de:0030-drops-68395},
  doi =		{10.4230/DagRep.6.8.91},
  annote =	{Keywords: Cancer, DNA Sequence Assembly, Expression Profiles, Next Generation Sequencing, Sequence analysis, Software Engineering (Tools \& Libraries)}
}
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-dev.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.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-dev.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-dev.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-dev.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-dev.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}
}
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