12 Search Results for "Böcker, Sebastian"

Volume

OASIcs, Volume 26

German Conference on Bioinformatics 2012

GCB 2012, September 20-22, 2012, Jena, Germany

Editors: Sebastian Böcker, Franziska Hufsky, Kerstin Scheubert, Jana Schleicher, and Stefan Schuster

Document
DOI: 10.4230/DagRep.11.6.1
Computational Proteomics (Dagstuhl Seminar 21271)

Authors: Sebastian Böcker, Rebekah Gundry, Lennart Martens, and Magnus Palmblad

Published in: Dagstuhl Reports, Volume 11, Issue 6 (2021)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 21271 "Computational Proteomics". The Seminar, which took place in a hybrid fashion with both local as well as online participation due to the COVID pandemic, was built around three topics: the rapid uptake of advanced machine learning in proteomics; computational challenges across the various rapidlly evolving approaches for structural and top-down proteomics; and the computational analysis of glycoproteomics data. These three topics were the focus of three corresponding breakout sessions, which ran in parallel throughout the seminar. A fourth breakout session was created during the seminar, on the specific topic of creating a Kaggle competition based on proteomics data. The abstracts presented here first describe the three introduction talks, one for each topic. These talk abstracts are then followed by one abstract each per breakout session, documenting that breakout’s discussion and outcomes. An Executive Summary is also provided, which details the overall seminar structure alongside the most important conclusions for the three topic-derived breakouts.
Cite as

Sebastian Böcker, Rebekah Gundry, Lennart Martens, and Magnus Palmblad. Computational Proteomics (Dagstuhl Seminar 21271). In Dagstuhl Reports, Volume 11, Issue 6, pp. 1-13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@Article{bocker_et_al:DagRep.11.6.1,
  author =	{B\"{o}cker, Sebastian and Gundry, Rebekah and Martens, Lennart and Palmblad, Magnus},
  title =	{{Computational Proteomics (Dagstuhl Seminar 21271)}},
  pages =	{1--13},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2021},
  volume =	{11},
  number =	{6},
  editor =	{B\"{o}cker, Sebastian and Gundry, Rebekah and Martens, Lennart and Palmblad, Magnus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.11.6.1},
  URN =		{urn:nbn:de:0030-drops-155775},
  doi =		{10.4230/DagRep.11.6.1},
  annote =	{Keywords: bioinformatics, computational mass spectrometry, machine learning, proteomics}
}
Document
DOI: 10.4230/DagRep.10.1.144
Computational Metabolomics: From Cheminformatics to Machine Learning (Dagstuhl Seminar 20051)

Authors: Sebastian Böcker, Corey Broeckling, Emma Schymanski, and Nicola Zamboni

Published in: Dagstuhl Reports, Volume 10, Issue 1 (2020)

Abstract
Dagstuhl Seminar 20051 on Computational Metabolomics is the third edition of seminars on this topic and focused on Cheminformatics and Machine Learning. With the advent of higher precision instrumentation, application of metabolomics to a wider variety of small molecules, and ever increasing amounts of raw and processed data available, developments in cheminformatics and machine learning are sorely needed to facilitate interoperability and leverage further insights from these data. Following on from Seminars 17491 and 15492, this edition convened both experimental and computational experts, many of whom had attended the previous sessions and brought much-valued perspective to the week’s proceedings and discussions. Throughout the week, participants first debated on what topics to discuss in detail, before dispersing into smaller, focused working groups for more in-depth discussions. This dynamic format was found to be most productive and ensured active engagement amongst the participants. The abstracts in this report reflect these working group discussions, in addition to summarising several informal evening sessions. Action points to follow-up on after the seminar were also discussed, including future workshops and possibly another Dagstuhl seminar in late 2021 or 2022.
Cite as

Sebastian Böcker, Corey Broeckling, Emma Schymanski, and Nicola Zamboni. Computational Metabolomics: From Cheminformatics to Machine Learning (Dagstuhl Seminar 20051). In Dagstuhl Reports, Volume 10, Issue 1, pp. 144-159, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@Article{bocker_et_al:DagRep.10.1.144,
  author =	{B\"{o}cker, Sebastian and Broeckling, Corey and Schymanski, Emma and Zamboni, Nicola},
  title =	{{Computational Metabolomics: From Cheminformatics to Machine Learning (Dagstuhl Seminar 20051)}},
  pages =	{144--159},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{10},
  number =	{1},
  editor =	{B\"{o}cker, Sebastian and Broeckling, Corey and Schymanski, Emma and Zamboni, Nicola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.10.1.144},
  URN =		{urn:nbn:de:0030-drops-124036},
  doi =		{10.4230/DagRep.10.1.144},
  annote =	{Keywords: bioinformatics, chemoinformatics, computational mass spectrometry, computational metabolomics, machine learning}
}
Document
DOI: 10.4230/LIPIcs.WABI.2018.23
Heuristic Algorithms for the Maximum Colorful Subtree Problem

Authors: Kai Dührkop, Marie A. Lataretu, W. Timothy J. White, and Sebastian Böcker

Published in: LIPIcs, Volume 113, 18th International Workshop on Algorithms in Bioinformatics (WABI 2018)

Abstract
In metabolomics, small molecules are structurally elucidated using tandem mass spectrometry (MS/MS); this computational task can be formulated as the Maximum Colorful Subtree problem, which is NP-hard. Unfortunately, data from a single metabolite requires us to solve hundreds or thousands of instances of this problem - and in a single Liquid Chromatography MS/MS run, hundreds or thousands of metabolites are measured. Here, we comprehensively evaluate the performance of several heuristic algorithms for the problem. Unfortunately, as is often the case in bioinformatics, the structure of the (chemically) true solution is not known to us; therefore we can only evaluate against the optimal solution of an instance. Evaluating the quality of a heuristic based on scores can be misleading: Even a slightly suboptimal solution can be structurally very different from the optimal solution, but it is the structure of a solution and not its score that is relevant for the downstream analysis. To this end, we propose a different evaluation setup: Given a set of candidate instances of which exactly one is known to be correct, the heuristic in question solves each instance to the best of its ability, producing a score for each instance, which is then used to rank the instances. We then evaluate whether the correct instance is ranked highly by the heuristic. We find that one particular heuristic consistently ranks the correct instance in a top position. We also find that the scores of the best heuristic solutions are very close to the optimal score; in contrast, the structure of the solutions can deviate significantly from the optimal structures. Integrating the heuristic allowed us to speed up computations in practice by a factor of 100-fold.
Cite as

Kai Dührkop, Marie A. Lataretu, W. Timothy J. White, and Sebastian Böcker. Heuristic Algorithms for the Maximum Colorful Subtree Problem. In 18th International Workshop on Algorithms in Bioinformatics (WABI 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 113, pp. 23:1-23:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{duhrkop_et_al:LIPIcs.WABI.2018.23,
  author =	{D\"{u}hrkop, Kai and Lataretu, Marie A. and White, W. Timothy J. and B\"{o}cker, Sebastian},
  title =	{{Heuristic Algorithms for the Maximum Colorful Subtree Problem}},
  booktitle =	{18th International Workshop on Algorithms in Bioinformatics (WABI 2018)},
  pages =	{23:1--23:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-082-8},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{113},
  editor =	{Parida, Laxmi and Ukkonen, Esko},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2018.23},
  URN =		{urn:nbn:de:0030-drops-93256},
  doi =		{10.4230/LIPIcs.WABI.2018.23},
  annote =	{Keywords: Fragmentation trees, Computational mass spectrometry}
}
Document
DOI: 10.4230/DagRep.7.12.1
Computational Metabolomics: Identification, Interpretation, Imaging (Dagstuhl Seminar 17491)

Authors: Theodore Alexandrov, Sebastian Böcker, Pieter Dorrestein, and Emma Schymanski

Published in: Dagstuhl Reports, Volume 7, Issue 12 (2018)

Abstract
Metabolites are key players in almost all biological processes, and play various functional roles providing energy, building blocks, signaling, communication, and defense. Metabolites serve as clinical biomarkers for detecting medical conditions such as cancer; small molecule drugs account for 90% of prescribed therapeutics. Complete understanding of biological systems requires detecting and interpreting the metabolome in time and space. 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 metabolite mixtures. MS-based experiments generate datasets of increasing complexity and size. The Dagstuhl Seminar on Computational Metabolomics brought together leading experts from the experimental (analytical chemistry and biology) and the computational (computer science and bioinformatics) side, to foster the exchange of expertise needed to advance computational metabolomics. The focus was on a dynamic schedule with overview talks followed by break-out sessions, selected by the participants, covering the whole experimental-computational continuum in mass spectrometry. Particular focus in this seminar was given to imaging mass spectrometry techniques that integrate a spacial component into the analysis, ranging in scale from single cells to organs and organisms.
Cite as

Theodore Alexandrov, Sebastian Böcker, Pieter Dorrestein, and Emma Schymanski. Computational Metabolomics: Identification, Interpretation, Imaging (Dagstuhl Seminar 17491). In Dagstuhl Reports, Volume 7, Issue 12, pp. 1-17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{alexandrov_et_al:DagRep.7.12.1,
  author =	{Alexandrov, Theodore and B\"{o}cker, Sebastian and Dorrestein, Pieter and Schymanski, Emma},
  title =	{{Computational Metabolomics: Identification, Interpretation, Imaging (Dagstuhl Seminar 17491)}},
  pages =	{1--17},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2018},
  volume =	{7},
  number =	{12},
  editor =	{Alexandrov, Theodore and B\"{o}cker, Sebastian and Dorrestein, Pieter and Schymanski, Emma},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.7.12.1},
  URN =		{urn:nbn:de:0030-drops-86740},
  doi =		{10.4230/DagRep.7.12.1},
  annote =	{Keywords: algorithms, bioinformatics, cheminformatics, computational mass spectrometry, computational metabolomics, databases, imaging mass spectrometry}
}
Document
DOI: 10.4230/DagRep.5.11.180
Computational Metabolomics (Dagstuhl Seminar 15492)

Authors: Sebastian Böcker, Juho Rousu, and Emma Schymanski

Published in: Dagstuhl Reports, Volume 5, Issue 11 (2016)

Abstract
he Dagstuhl Seminar 15492 on Computational Metabolomics brought together leading experimental (analytical chemistry and biology) and computational (computer science and bioinformatics) experts with the aim to foster the exchange of expertise needed to advance computational metabolomics. The focus was on a dynamic schedule with overview talks followed by breakout sessions, selected by the participants, covering the whole experimental-computational continuum in mass spectrometry, as well as the use of metabolomics data in applications. A general observation was that metabolomics is in the state that genomics was 20 years ago and that while the availability of data is holding back progress, several good initiatives are present. The importance of small molecules to life should be communicated properly to assist initiating a global metabolomics initiative, such as the Human Genome project. Several follow-ups were discussed, including workshops, hackathons, joint paper(s) and a new Dagstuhl Seminar in two years to follow up on this one.
Cite as

Sebastian Böcker, Juho Rousu, and Emma Schymanski. Computational Metabolomics (Dagstuhl Seminar 15492). In Dagstuhl Reports, Volume 5, Issue 11, pp. 180-192, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@Article{bocker_et_al:DagRep.5.11.180,
  author =	{B\"{o}cker, Sebastian and Rousu, Juho and Schymanski, Emma},
  title =	{{Computational Metabolomics (Dagstuhl Seminar 15492)}},
  pages =	{180--192},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2016},
  volume =	{5},
  number =	{11},
  editor =	{B\"{o}cker, Sebastian and Rousu, Juho and Schymanski, Emma},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.5.11.180},
  URN =		{urn:nbn:de:0030-drops-58016},
  doi =		{10.4230/DagRep.5.11.180},
  annote =	{Keywords: algorithms, bioinformatics, cheminformatics, computational mass spectrometry, computational metabolomics, databases}
}
Document
Complete Volume
DOI: 10.4230/OASIcs.GCB.2012
OASIcs, Volume 26, GCB'12, Complete Volume

Authors: Sebastian Böcker, Franziska Hufsky, Kerstin Scheubert, Jana Schleicher, and Stefan Schuster

Published in: OASIcs, Volume 26, German Conference on Bioinformatics 2012

Abstract
OASIcs, Volume 26, GCB'12, Complete Volume
Cite as

German Conference on Bioinformatics 2012. Open Access Series in Informatics (OASIcs), Volume 26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@Proceedings{bocker_et_al:OASIcs.GCB.2012,
  title =	{{OASIcs, Volume 26, GCB'12, Complete Volume}},
  booktitle =	{German Conference on Bioinformatics 2012},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-44-6},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{26},
  editor =	{B\"{o}cker, Sebastian and Hufsky, Franziska and Scheubert, Kerstin and Schleicher, Jana and Schuster, Stefan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2012},
  URN =		{urn:nbn:de:0030-drops-37269},
  doi =		{10.4230/OASIcs.GCB.2012},
  annote =	{Keywords: Life and Medical Sciences}
}
Document
Front Matter
DOI: 10.4230/OASIcs.GCB.2012.i
Frontmatter, Table of Contents, Preface, Programm Committee, Supportes and Sponsors, Index of Authors

Authors: Sebastian Böcker, Franziska Hufsky, Kerstin Scheubert, Jana Schleicher, and Stefan Schuster

Published in: OASIcs, Volume 26, German Conference on Bioinformatics 2012

Abstract
Frontmatter, Table of Contents, Preface, Programm Committee, Supportes and Sponsors, Index of Authors
Cite as

German Conference on Bioinformatics 2012. Open Access Series in Informatics (OASIcs), Volume 26, pp. i-xiv, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InProceedings{bocker_et_al:OASIcs.GCB.2012.i,
  author =	{B\"{o}cker, Sebastian and Hufsky, Franziska and Scheubert, Kerstin and Schleicher, Jana and Schuster, Stefan},
  title =	{{Frontmatter, Table of Contents, Preface, Programm Committee, Supportes and Sponsors, Index of Authors}},
  booktitle =	{German Conference on Bioinformatics 2012},
  pages =	{i--xiv},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-44-6},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{26},
  editor =	{B\"{o}cker, Sebastian and Hufsky, Franziska and Scheubert, Kerstin and Schleicher, Jana and Schuster, Stefan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2012.i},
  URN =		{urn:nbn:de:0030-drops-37121},
  doi =		{10.4230/OASIcs.GCB.2012.i},
  annote =	{Keywords: Frontmatter, Table of Contents, Preface, Programm Committee, Supportes and Sponsors, Index of Authors}
}
Document
DOI: 10.4230/OASIcs.GCB.2012.12
Comparing Fragmentation Trees from Electron Impact Mass Spectra with Annotated Fragmentation Pathways

Authors: Franziska Hufsky and Sebastian Böcker

Published in: OASIcs, Volume 26, German Conference on Bioinformatics 2012

Abstract
Electron impact ionization (EI) is the most common form of ionization for GC-MS analysis of small molecules. This ionization method results in a mass spectrum not necessarily containing the molecular ion peak. The fragmentation of small compounds during EI is well understood, but manual interpretation of mass spectra is tedious and time-consuming. Methods for automated analysis are highly sought, but currently limited to database searching and rule-based approaches. With the computation of hypothetical fragmentation trees from high mass GC-MS data the high-throughput interpretation of such spectra may become feasible. We compare these trees with annotated fragmentation pathways. We find that fragmentation trees explain the origin of the ions found in the mass spectra in accordance to the literature. No peak is annotated with an incorrect fragment formula and 78.7% of the fragmentation processes are correctly reconstructed.
Cite as

Franziska Hufsky and Sebastian Böcker. Comparing Fragmentation Trees from Electron Impact Mass Spectra with Annotated Fragmentation Pathways. In German Conference on Bioinformatics 2012. Open Access Series in Informatics (OASIcs), Volume 26, pp. 12-22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InProceedings{hufsky_et_al:OASIcs.GCB.2012.12,
  author =	{Hufsky, Franziska and B\"{o}cker, Sebastian},
  title =	{{Comparing Fragmentation Trees from Electron Impact Mass Spectra with Annotated Fragmentation Pathways}},
  booktitle =	{German Conference on Bioinformatics 2012},
  pages =	{12--22},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-44-6},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{26},
  editor =	{B\"{o}cker, Sebastian and Hufsky, Franziska and Scheubert, Kerstin and Schleicher, Jana and Schuster, Stefan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2012.12},
  URN =		{urn:nbn:de:0030-drops-37146},
  doi =		{10.4230/OASIcs.GCB.2012.12},
  annote =	{Keywords: metabolomics, GC-MS, computational mass spectrometry, fragmentation trees}
}
Document
DOI: 10.4230/OASIcs.GCB.2012.23
Finding Characteristic Substructures for Metabolite Classes

Authors: Marcus Ludwig, Franziska Hufsky, Samy Elshamy, and Sebastian Böcker

Published in: OASIcs, Volume 26, German Conference on Bioinformatics 2012

Abstract
We introduce a method for finding a characteristic substructure for a set of molecular structures. Different from common approaches, such as computing the maximum common subgraph, the resulting substructure does not have to be contained in its exact form in all input molecules. Our approach is part of the identification pipeline for unknown metabolites using fragmentation trees. Searching databases using fragmentation tree alignment results in hit lists containing compounds with large structural similarity to the unknown metabolite. The characteristic substructure of the molecules in the hit list may be a key structural element of the unknown compound and might be used as starting point for structure elucidation. We evaluate our method on different data sets and find that it retrieves essential substructures if the input lists are not too heterogeneous. We apply our method to predict structural elements for five unknown samples from Icelandic poppy.
Cite as

Marcus Ludwig, Franziska Hufsky, Samy Elshamy, and Sebastian Böcker. Finding Characteristic Substructures for Metabolite Classes. In German Conference on Bioinformatics 2012. Open Access Series in Informatics (OASIcs), Volume 26, pp. 23-38, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InProceedings{ludwig_et_al:OASIcs.GCB.2012.23,
  author =	{Ludwig, Marcus and Hufsky, Franziska and Elshamy, Samy and B\"{o}cker, Sebastian},
  title =	{{Finding Characteristic Substructures for Metabolite Classes}},
  booktitle =	{German Conference on Bioinformatics 2012},
  pages =	{23--38},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-44-6},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{26},
  editor =	{B\"{o}cker, Sebastian and Hufsky, Franziska and Scheubert, Kerstin and Schleicher, Jana and Schuster, Stefan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2012.23},
  URN =		{urn:nbn:de:0030-drops-37157},
  doi =		{10.4230/OASIcs.GCB.2012.23},
  annote =	{Keywords: metabolites, substructure prediction, mass spectrometry, FT-BLAST}
}
Document
DOI: 10.4230/DagSemProc.08101.2
Towards de novo identification of metabolites by analyzing tandem mass spectra

Authors: Sebastian Böcker and Florian Rasche

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

Abstract
Mass spectrometry is among the most widely used technologies in proteomics and metabolomics. For metabolites, de novo interpretation of spectra is even more important than for protein data, because metabolite spectra databases cover only a small fraction of naturally occurring metabolites. In this work, we analyze a method for fully automated de novo identification of metabolites from tandem mass spectra. Mass spectrometry data is usually assumed to be insufficient for identification of molecular structures, so we want to estimate the molecular formula of the unknown metabolite, a crucial step for its identification. This is achieved by calculating the possible formulas of the fragment peaks and then reconstructing the most likely fragmentation tree from this information. We present tests on real mass spectra showing that our algorithms solve the reconstruction problem suitably fast and provide excellent results: For all 32 test compounds the correct solution was among the top five suggestions, for 26 compounds the first suggestion of the exact algorithm was correct.
Cite as

Sebastian Böcker and Florian Rasche. Towards de novo identification of metabolites by analyzing tandem mass spectra. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 8101, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{bocker_et_al:DagSemProc.08101.2,
  author =	{B\"{o}cker, Sebastian and Rasche, Florian},
  title =	{{Towards de novo identification of metabolites by analyzing tandem mass spectra}},
  booktitle =	{Computational Proteomics},
  pages =	{1--5},
  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.08101.2},
  URN =		{urn:nbn:de:0030-drops-17839},
  doi =		{10.4230/DagSemProc.08101.2},
  annote =	{Keywords: Tandem mass spectrometry, metabolomics, de novo interpretation}
}
Document
DOI: 10.4230/DagSemProc.05471.5
Combinatorial Approaches for Mass Spectra Recalibration

Authors: Sebastian Böcker and Veli Mäkinen

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

Abstract
Mass spectrometry has become one of the most popular analysis techniques in Proteomics and Systems Biology. With the creation of larger datasets, the automated recalibration of mass spectra becomes important to ensure that every peak in the sample spectrum is correctly assigned to some peptide and protein. Algorithms for recalibrating mass spectra have to be robust with respect to wrongly assigned peaks, as well as efficient due to the amount of mass spectrometry data. The recalibration of mass spectra leads us to the problem of finding an optimal matching between mass spectra under measurement errors. We have developed two deterministic methods that allow robust computation of such a matching: The first approach uses a computational geometry interpretation of the problem, and tries to find two parallel lines with constant distance that stab a maximal number of points in the plane. The second approach is based on finding a maximal common approximate subsequence, and improves existing algorithms by one order of magnitude exploiting the sequential nature of the matching problem. We compare our results to a computational geometry algorithm using a topological line-sweep.
Cite as

Sebastian Böcker and Veli Mäkinen. Combinatorial Approaches for Mass Spectra Recalibration. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{bocker_et_al:DagSemProc.05471.5,
  author =	{B\"{o}cker, Sebastian and M\"{a}kinen, Veli},
  title =	{{Combinatorial Approaches for Mass Spectra Recalibration}},
  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-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.5},
  URN =		{urn:nbn:de:0030-drops-5455},
  doi =		{10.4230/DagSemProc.05471.5},
  annote =	{Keywords: Mass spectrometry recalibration computational geometry}
}
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