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Documents authored by Klau, Gunnar W.

Document
DOI: 10.4230/LIPIcs.WABI.2020.6
The Longest Run Subsequence Problem

Authors: Sven Schrinner, Manish Goel, Michael Wulfert, Philipp Spohr, Korbinian Schneeberger, and Gunnar W. Klau

Published in: LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

Abstract
Genome assembly is one of the most important problems in computational genomics. Here, we suggest addressing the scaffolding phase, in which contigs need to be linked and ordered to obtain larger pseudo-chromosomes, by means of a second incomplete assembly of a related species. The idea is to use alignments of binned regions in one contig to find the most homologous contig in the other assembly. We show that ordering the contigs of the other assembly can be expressed by a new string problem, the longest run subsequence problem (LRS). We show that LRS is NP-hard and present reduction rules and two algorithmic approaches that, together, are able to solve large instances of LRS to provable optimality. In particular, they can solve realistic instances resulting from partial Arabidopsis thaliana assemblies in short computation time. Our source code and all data used in the experiments are freely available.
Cite as

Sven Schrinner, Manish Goel, Michael Wulfert, Philipp Spohr, Korbinian Schneeberger, and Gunnar W. Klau. The Longest Run Subsequence Problem. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 6:1-6:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{schrinner_et_al:LIPIcs.WABI.2020.6,
  author =	{Schrinner, Sven and Goel, Manish and Wulfert, Michael and Spohr, Philipp and Schneeberger, Korbinian and Klau, Gunnar W.},
  title =	{{The Longest Run Subsequence Problem}},
  booktitle =	{20th International Workshop on Algorithms in Bioinformatics (WABI 2020)},
  pages =	{6:1--6:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-161-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{172},
  editor =	{Kingsford, Carl and Pisanti, Nadia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020.6},
  URN =		{urn:nbn:de:0030-drops-127951},
  doi =		{10.4230/LIPIcs.WABI.2020.6},
  annote =	{Keywords: alignments, assembly, string algorithm, longest subsequence}
}
Document
DOI: 10.4230/LIPIcs.WABI.2018.16
Multiple-Choice Knapsack for Assigning Partial Atomic Charges in Drug-Like Molecules

Authors: Martin S. Engler, Bertrand Caron, Lourens Veen, Daan P. Geerke, Alan E. Mark, and Gunnar W. Klau

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

Abstract
A key factor in computational drug design is the consistency and reliability with which intermolecular interactions between a wide variety of molecules can be described. Here we present a procedure to efficiently, reliably and automatically assign partial atomic charges to atoms based on known distributions. We formally introduce the molecular charge assignment problem, where the task is to select a charge from a set of candidate charges for every atom of a given query molecule. Charges are accompanied by a score that depends on their observed frequency in similar neighbourhoods (chemical environments) in a database of previously parameterised molecules. The aim is to assign the charges such that the total charge equals a known target charge within a margin of error while maximizing the sum of the charge scores. We show that the problem is a variant of the well-studied multiple-choice knapsack problem and thus weakly NP-complete. We propose solutions based on Integer Linear Programming and a pseudo-polynomial time Dynamic Programming algorithm. We show that the results obtained for novel molecules not included in the database are comparable to the ones obtained performing explicit charge calculations while decreasing the time to determine partial charges for a molecule by several orders of magnitude, that is, from hours or even days to below a second. Our software is openly available at https://github.com/enitram/charge_assign.
Cite as

Martin S. Engler, Bertrand Caron, Lourens Veen, Daan P. Geerke, Alan E. Mark, and Gunnar W. Klau. Multiple-Choice Knapsack for Assigning Partial Atomic Charges in Drug-Like Molecules. In 18th International Workshop on Algorithms in Bioinformatics (WABI 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 113, pp. 16:1-16:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{engler_et_al:LIPIcs.WABI.2018.16,
  author =	{Engler, Martin S. and Caron, Bertrand and Veen, Lourens and Geerke, Daan P. and Mark, Alan E. and Klau, Gunnar W.},
  title =	{{Multiple-Choice Knapsack for Assigning Partial Atomic Charges in Drug-Like Molecules}},
  booktitle =	{18th International Workshop on Algorithms in Bioinformatics (WABI 2018)},
  pages =	{16:1--16:13},
  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.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2018.16},
  URN =		{urn:nbn:de:0030-drops-93187},
  doi =		{10.4230/LIPIcs.WABI.2018.16},
  annote =	{Keywords: Multiple-choice knapsack, integer linear programming, pseudo-polynomial dynamic programming, partial charge assignment, molecular dynamics simulations}
}
Document
DOI: 10.4230/OASIcs.GCB.2013.46
Reconstructing Consensus Bayesian Network Structures with Application to Learning Molecular Interaction Networks

Authors: Holger Fröhlich and Gunnar W. Klau

Published in: OASIcs, Volume 34, German Conference on Bioinformatics 2013

Abstract
Bayesian Networks are an established computational approach for data driven network inference. However, experimental data is limited in its availability and corrupted by noise. This leads to an unavoidable uncertainty about the correct network structure. Thus sampling or bootstrap based strategies are applied to obtain edge frequencies. In a more general sense edge frequencies can also result from integrating networks learned on different datasets or via different inference algorithms. Subsequently one typically wants to derive a biological interpretation from the results in terms of a consensus network. We here propose a log odds based edge score on the basis of the expected false positive rate and thus avoid the selection of a subjective edge frequency cutoff. Computing a score optimal consensus network in our new model amounts to solving the maximum weight acyclic subdigraph problem. We use a branch-and-cut algorithm based on integer linear programming for this task. Our empirical studies on simulated and real data demonstrate a consistently improved network reconstruction accuracy compared to two threshold based strategies.
Cite as

Holger Fröhlich and Gunnar W. Klau. Reconstructing Consensus Bayesian Network Structures with Application to Learning Molecular Interaction Networks. In German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. 46-55, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@InProceedings{frohlich_et_al:OASIcs.GCB.2013.46,
  author =	{Fr\"{o}hlich, Holger and Klau, Gunnar W.},
  title =	{{Reconstructing Consensus Bayesian Network Structures with Application to Learning Molecular Interaction Networks}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{46--55},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-59-0},
  ISSN =	{2190-6807},
  year =	{2013},
  volume =	{34},
  editor =	{Bei{\ss}barth, Tim and Kollmar, Martin and Leha, Andreas and Morgenstern, Burkhard and Schultz, Anne-Kathrin and Waack, Stephan and Wingender, Edgar},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2013.46},
  URN =		{urn:nbn:de:0030-drops-42273},
  doi =		{10.4230/OASIcs.GCB.2013.46},
  annote =	{Keywords: Bayesian Networks, Network Reverse Engineering, Minimum Feedback Arc Set, Maximum Acyclic Subgraph, Molecular Interaction Networks}
}
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