18 Search Results for "Morgenstern, Burkhard"

Volume

OASIcs, Volume 34

German Conference on Bioinformatics 2013

GCB 2013, September 10-13, 2013, Göttingen, Germany

Editors: Tim Beißbarth, Martin Kollmar, Andreas Leha, Burkhard Morgenstern, Anne-Kathrin Schultz, Stephan Waack, and Edgar Wingender

Document
DOI: 10.4230/LIPIcs.WABI.2025.20
A k-mer-Based Estimator of the Substitution Rate Between Repetitive Sequences

Authors: Haonan Wu, Antonio Blanca, and Paul Medvedev

Published in: LIPIcs, Volume 344, 25th International Conference on Algorithms for Bioinformatics (WABI 2025)

Abstract
K-mer-based analysis of genomic data is ubiquitous, but the presence of repetitive k-mers continues to pose problems for the accuracy of many methods. For example, the Mash tool (Ondov et al. 2016) can accurately estimate the substitution rate between two low-repetitive sequences from their k-mer sketches; however, it is inaccurate on repetitive sequences such as the centromere of a human chromosome. Follow-up work by Blanca et al. (2021) has attempted to model how mutations affect k-mer sets based on strong assumptions that the sequence is non-repetitive and that mutations do not create spurious k-mer matches. However, the theoretical foundations for extending an estimator like Mash to work in the presence of repeat sequences have been lacking. In this work, we relax the non-repetitive assumption and propose a novel estimator for the mutation rate. We derive theoretical bounds on our estimator’s bias. Our experiments show that it remains accurate for repetitive genomic sequences, such as the alpha satellite higher order repeats in centromeres. We demonstrate our estimator’s robustness across diverse datasets and various ranges of the substitution rate and k-mer size. Finally, we show how sketching can be used to avoid dealing with large k-mer sets while retaining accuracy. Our software is available at https://github.com/medvedevgroup/Repeat-Aware_Substitution_Rate_Estimator.
Cite as

Haonan Wu, Antonio Blanca, and Paul Medvedev. A k-mer-Based Estimator of the Substitution Rate Between Repetitive Sequences. In 25th International Conference on Algorithms for Bioinformatics (WABI 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 344, pp. 20:1-20:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{wu_et_al:LIPIcs.WABI.2025.20,
  author =	{Wu, Haonan and Blanca, Antonio and Medvedev, Paul},
  title =	{{A k-mer-Based Estimator of the Substitution Rate Between Repetitive Sequences}},
  booktitle =	{25th International Conference on Algorithms for Bioinformatics (WABI 2025)},
  pages =	{20:1--20:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-386-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{344},
  editor =	{Brejov\'{a}, Bro\v{n}a and Patro, Rob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2025.20},
  URN =		{urn:nbn:de:0030-drops-239465},
  doi =		{10.4230/LIPIcs.WABI.2025.20},
  annote =	{Keywords: k-mers, sketching, mutation rates}
}
Document
DOI: 10.4230/LIPIcs.WABI.2025.16
Estimation of Substitution and Indel Rates via k-mer Statistics

Authors: Mahmudur Rahman Hera, Paul Medvedev, David Koslicki, and Antonio Blanca

Published in: LIPIcs, Volume 344, 25th International Conference on Algorithms for Bioinformatics (WABI 2025)

Abstract
Methods utilizing k-mers are widely used in bioinformatics, yet our understanding of their statistical properties under realistic mutation models remains incomplete. Previously, substitution-only mutation models have been considered to derive precise expectations and variances for mutated k-mers and intervals of mutated and non-mutated sequences. In this work, we consider a mutation model that incorporates insertions and deletions in addition to single-nucleotide substitutions. Within this framework, we derive closed-form k-mer-based estimators for the three fundamental mutation parameters: substitution, deletion rate, and insertion rates. We provide theoretical guarantees in the form of concentration inequalities, ensuring accuracy of our estimators under reasonable model assumptions. Empirical evaluations on simulated evolution of genomic sequences confirm our theoretical findings, demonstrating that accounting for insertions and deletions signals allows for accurate estimation of mutation rates and improves upon the results obtained by considering a substitution-only model. An implementation of estimating the mutation parameters from a pair of fasta files is available here: https://github.com/KoslickiLab/estimate_rates_using_mutation_model.git. The results presented in this manuscript can be reproduced using the code available here: https://github.com/KoslickiLab/est_rates_experiments.git.
Cite as

Mahmudur Rahman Hera, Paul Medvedev, David Koslicki, and Antonio Blanca. Estimation of Substitution and Indel Rates via k-mer Statistics. In 25th International Conference on Algorithms for Bioinformatics (WABI 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 344, pp. 16:1-16:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{rahmanhera_et_al:LIPIcs.WABI.2025.16,
  author =	{Rahman Hera, Mahmudur and Medvedev, Paul and Koslicki, David and Blanca, Antonio},
  title =	{{Estimation of Substitution and Indel Rates via k-mer Statistics}},
  booktitle =	{25th International Conference on Algorithms for Bioinformatics (WABI 2025)},
  pages =	{16:1--16:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-386-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{344},
  editor =	{Brejov\'{a}, Bro\v{n}a and Patro, Rob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2025.16},
  URN =		{urn:nbn:de:0030-drops-239422},
  doi =		{10.4230/LIPIcs.WABI.2025.16},
  annote =	{Keywords: k-mers, mutation rate, indel, alignment-free, estimation, substitution, insertion, deletion}
}
Document
Research
DOI: 10.4230/OASIcs.Grossi.20
Subsequence-Based Indices for Genome Sequence Analysis

Authors: Giovanni Buzzega, Alessio Conte, Veronica Guerrini, Giulia Punzi, Giovanna Rosone, and Lorenzo Tattini

Published in: OASIcs, Volume 132, From Strings to Graphs, and Back Again: A Festschrift for Roberto Grossi's 60th Birthday (2025)

Abstract
Compact indices are a fundamental tool in string analysis, even more so in bioinformatics, where genomic sequences can reach billions in length. This paper presents some recent results in which Roberto Grossi has been involved, showing how some of these indices do more than just efficiently represent data, but rather are able to bring out salient information within it, which can be exploited for their downstream analysis. Specifically, we first review a recently-introduced method [Guerrini et al., 2023] that employs the Burrows-Wheeler Transform to build reasonably accurate phylogenetic trees in an assembly-free scenario. We then describe a recent practical tool [Buzzega et al., 2025] for indexing Maximal Common Subsequences between strings, which can enable analysis of genomic sequence similarity. Experimentally, we show that the results produced by the one index are consistent with the expectations about the results of the other index.
Cite as

Giovanni Buzzega, Alessio Conte, Veronica Guerrini, Giulia Punzi, Giovanna Rosone, and Lorenzo Tattini. Subsequence-Based Indices for Genome Sequence Analysis. In From Strings to Graphs, and Back Again: A Festschrift for Roberto Grossi's 60th Birthday. Open Access Series in Informatics (OASIcs), Volume 132, pp. 20:1-20:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{buzzega_et_al:OASIcs.Grossi.20,
  author =	{Buzzega, Giovanni and Conte, Alessio and Guerrini, Veronica and Punzi, Giulia and Rosone, Giovanna and Tattini, Lorenzo},
  title =	{{Subsequence-Based Indices for Genome Sequence Analysis}},
  booktitle =	{From Strings to Graphs, and Back Again: A Festschrift for Roberto Grossi's 60th Birthday},
  pages =	{20:1--20:21},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-391-1},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{132},
  editor =	{Conte, Alessio and Marino, Andrea and Rosone, Giovanna and Vitter, Jeffrey Scott},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Grossi.20},
  URN =		{urn:nbn:de:0030-drops-238199},
  doi =		{10.4230/OASIcs.Grossi.20},
  annote =	{Keywords: String Indices, Burrows-Wheeler Transform, Maximal Common Subsequences, Sequence Analysis, Phylogeny}
}
Document
Front Matter
DOI: 10.4230/OASIcs.GCB.2013.i
Frontmatter, Table of Contents, Preface, Conference Organization

Authors: Tim Beißbarth, Martin Kollmar, Andreas Leha, Burkhard Morgenstern, Anne-Kathrin Schultz, Stephan Waack, and Edgar Wingender

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

Abstract
Frontmatter, Table of Contents, Preface, Conference Organization
Cite as

German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. i-xiii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

Copy BibTex To Clipboard

@InProceedings{beibarth_et_al:OASIcs.GCB.2013.i,
  author =	{Bei{\ss}barth, Tim and Kollmar, Martin and Leha, Andreas and Morgenstern, Burkhard and Schultz, Anne-Kathrin and Waack, Stephan and Wingender, Edgar},
  title =	{{Frontmatter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{i--xiii},
  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.i},
  URN =		{urn:nbn:de:0030-drops-42265},
  doi =		{10.4230/OASIcs.GCB.2013.i},
  annote =	{Keywords: Frontmatter, Table of Contents, Preface, Conference Organization}
}
Document
DOI: 10.4230/OASIcs.GCB.2013.1
On the estimation of metabolic profiles in metagenomics

Authors: Kathrin Petra Aßhauer and Peter Meinicke

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

Abstract
Metagenomics enables the characterization of the specific metabolic potential of a microbial community. The common approach towards a quantitative representation of this potential is to count the number of metagenomic sequence fragments that can be assigned to metabolic pathways by means of predicted gene functions. The resulting pathway abundances make up the metabolic profile of the metagenome and several different schemes for computing these profiles have been used. So far, none of the existing approaches actually estimates the proportion of sequences that can be assigned to a particular pathway. In most publications of metagenomic studies, the utilized abundance scores lack a clear statistical meaning and usually cannot be compared across different studies. Here, we introduce a mixture model-based approach to the estimation of pathway abundances that provides a basis for statistical interpretation and fast computation of metabolic profiles. Using the KEGG database our results on a large-scale analysis of data from the Human Microbiome Project show a good representation of metabolic differences between different body sites. Further, the results indicate that our mixture model even provides a better representation than the dedicated HUMAnN tool which has been developed for metabolic analysis of human microbiome data.
Cite as

Kathrin Petra Aßhauer and Peter Meinicke. On the estimation of metabolic profiles in metagenomics. In German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. 1-13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

Copy BibTex To Clipboard

@InProceedings{ahauer_et_al:OASIcs.GCB.2013.1,
  author =	{A{\ss}hauer, Kathrin Petra and Meinicke, Peter},
  title =	{{On the estimation of metabolic profiles in metagenomics}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{1--13},
  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.2013.1},
  URN =		{urn:nbn:de:0030-drops-42380},
  doi =		{10.4230/OASIcs.GCB.2013.1},
  annote =	{Keywords: metagenomics, metabolic profiling, taxonomic profiling, abundance estimation, mixture modeling}
}
Document
DOI: 10.4230/OASIcs.GCB.2013.14
On Weighting Schemes for Gene Order Analysis

Authors: Matthias Bernt, Nicolas Wieseke, and Martin Middendorf

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

Abstract
Gene order analysis aims at extracting phylogenetic information from the comparison of the order and orientation of the genes on the genomes of different species. This can be achieved by computing parsimonious rearrangement scenarios, i.e. to determine a sequence of rearrangements events that transforms one given gene order into another such that the sum of weights of the included rearrangement events is minimal. In this sequence only certain types of rearrangements, given by the rearrangement model, are admissible and weights are assigned with respect to the rearrangement type. The choice of a suitable rearrangement model and corresponding weights for the included rearrangement types is important for the meaningful reconstruction. So far the analysis of weighting schemes for gene order analysis has not been considered sufficiently. In this paper weighting schemes for gene order analysis are considered for two rearrangement models: 1) inversions, transpositions, and inverse transpositions; 2) inversions, block interchanges, and inverse transpositions. For both rearrangement models we determined properties of the weighting functions that exclude certain types of rearrangements from parsimonious rearrangement scenarios.
Cite as

Matthias Bernt, Nicolas Wieseke, and Martin Middendorf. On Weighting Schemes for Gene Order Analysis. In German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. 14-23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

Copy BibTex To Clipboard

@InProceedings{bernt_et_al:OASIcs.GCB.2013.14,
  author =	{Bernt, Matthias and Wieseke, Nicolas and Middendorf, Martin},
  title =	{{On Weighting Schemes for Gene Order Analysis}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{14--23},
  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.2013.14},
  URN =		{urn:nbn:de:0030-drops-42354},
  doi =		{10.4230/OASIcs.GCB.2013.14},
  annote =	{Keywords: Gene order analysis, maximum parsimony, weighting}
}
Document
DOI: 10.4230/OASIcs.GCB.2013.24
Alignment-free sequence comparison with spaced k-mers

Authors: Marcus Boden, Martin Schöneich, Sebastian Horwege, Sebastian Lindner, Chris Leimeister, and Burkhard Morgenstern

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

Abstract
Alignment-free methods are increasingly used for genome analysis and phylogeny reconstruction since they circumvent various difficulties of traditional approaches that rely on multiple sequence alignments. In particular, they are much faster than alignment-based methods. Most alignment-free approaches work by analyzing the k-mer composition of sequences. In this paper, we propose to use 'spaced k-mers', i.e. patterns of deterministic and 'don't care' positions instead of contiguous k-mers. Using simulated and real-world sequence data, we demonstrate that this approach produces better phylogenetic trees than alignment-free methods that rely on contiguous k-mers. In addition, distances calculated with spaced k-mers appear to be statistically more stable than distances based on contiguous k-mers.
Cite as

Marcus Boden, Martin Schöneich, Sebastian Horwege, Sebastian Lindner, Chris Leimeister, and Burkhard Morgenstern. Alignment-free sequence comparison with spaced k-mers. In German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. 24-34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

Copy BibTex To Clipboard

@InProceedings{boden_et_al:OASIcs.GCB.2013.24,
  author =	{Boden, Marcus and Sch\"{o}neich, Martin and Horwege, Sebastian and Lindner, Sebastian and Leimeister, Chris and Morgenstern, Burkhard},
  title =	{{Alignment-free sequence comparison with spaced k-mers}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{24--34},
  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.2013.24},
  URN =		{urn:nbn:de:0030-drops-42334},
  doi =		{10.4230/OASIcs.GCB.2013.24},
  annote =	{Keywords: Alignment-free sequence comparison, phylogeny reconstruction}
}
Document
DOI: 10.4230/OASIcs.GCB.2013.35
PanCake: A Data Structure for Pangenomes

Authors: Corinna Ernst and Sven Rahmann

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

Abstract
We present a pangenome data structure ("PanCake") for sets of related genomes, based on bundling similar sequence regions into shared features, which are derived from genome-wide pairwise sequence alignments. We discuss the design of the data structure, basic operations on it and methods to predict core genomes and singleton regions. In contrast to many other pangenome analysis tools, like EDGAR or PGAT, PanCake is independent of gene annotations. Nevertheless, comparison of identified core and singleton regions shows good agreements. The PanCake data structure requires significantly less space than the sum of individual sequence files.
Cite as

Corinna Ernst and Sven Rahmann. PanCake: A Data Structure for Pangenomes. In German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. 35-45, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

Copy BibTex To Clipboard

@InProceedings{ernst_et_al:OASIcs.GCB.2013.35,
  author =	{Ernst, Corinna and Rahmann, Sven},
  title =	{{PanCake: A Data Structure for Pangenomes}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{35--45},
  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.2013.35},
  URN =		{urn:nbn:de:0030-drops-42314},
  doi =		{10.4230/OASIcs.GCB.2013.35},
  annote =	{Keywords: pangenome, data structure, core genome, comparative genomics}
}
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)

Copy BibTex To Clipboard

@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}
}
Document
DOI: 10.4230/OASIcs.GCB.2013.56
Efficient Interpretation of Tandem Mass Tags in Top-Down Proteomics

Authors: Anna Katharina Hildebrandt, Ernst Althaus, Hans-Peter Lenhof, Chien-Wen Hung, Andreas Tholey, and Andreas Hildebrandt

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

Abstract
Mass spectrometry is the major analytical tool for the identification and quantification of proteins in biological samples. In so-called top-down proteomics, separation and mass spectrometric analysis is performed at the level of intact proteins, without preparatory digestion steps. It has been shown that the tandem mass tag (TMT) labeling technology, which is often used for quantification based on digested proteins (bottom-up studies), can be applied in top-down proteomics as well. This, however, leads to a complex interpretation problem, where we need to annotate measured peaks with their respective generating protein, the number of charges, and the a priori unknown number of TMT-groups attached to this protein. In this work, we give an algorithm for the efficient enumeration of all valid annotations that fulfill available experimental constraints. Applying the algorithm to real-world data, we show that the annotation problem can indeed be efficiently solved. However, our experiments also demonstrate that reliable annotation in complex mixtures requires at least partial sequence information and high mass accuracy and resolution to go beyond the proof-of-concept stage.
Cite as

Anna Katharina Hildebrandt, Ernst Althaus, Hans-Peter Lenhof, Chien-Wen Hung, Andreas Tholey, and Andreas Hildebrandt. Efficient Interpretation of Tandem Mass Tags in Top-Down Proteomics. In German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. 56-67, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

Copy BibTex To Clipboard

@InProceedings{hildebrandt_et_al:OASIcs.GCB.2013.56,
  author =	{Hildebrandt, Anna Katharina and Althaus, Ernst and Lenhof, Hans-Peter and Hung, Chien-Wen and Tholey, Andreas and Hildebrandt, Andreas},
  title =	{{Efficient Interpretation of Tandem Mass Tags in Top-Down Proteomics}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{56--67},
  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.2013.56},
  URN =		{urn:nbn:de:0030-drops-42304},
  doi =		{10.4230/OASIcs.GCB.2013.56},
  annote =	{Keywords: Mass spectrometry, TMT labeling, Top-down Proteomics}
}
Document
DOI: 10.4230/OASIcs.GCB.2013.68
GEDEVO: An Evolutionary Graph Edit Distance Algorithm for Biological Network Alignment

Authors: Rashid Ibragimov, Maximilian Malek, Jiong Guo, and Jan Baumbach

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

Abstract
Introduction: With the so-called OMICS technology the scientific community has generated huge amounts of data that allow us to reconstruct the interplay of all kinds of biological entities. The emerging interaction networks are usually modeled as graphs with thousands of nodes and tens of thousands of edges between them. In addition to sequence alignment, the comparison of biological networks has proven great potential to infer the biological function of proteins and genes. However, the corresponding network alignment problem is computationally hard and theoretically intractable for real world instances. Results: We therefore developed GEDEVO, a novel tool for efficient graph comparison dedicated to real-world size biological networks. Underlying our approach is the so-called Graph Edit Distance (GED) model, where one graph is to be transferred into another one, with a minimal number of (or more general: minimal costs for) edge insertions and deletions. We present a novel evolutionary algorithm aiming to minimize the GED, and we compare our implementation against state of the art tools: SPINAL, GHOST, \CGRAAL, and \MIGRAAL. On a set of protein-protein interaction networks from different organisms we demonstrate that GEDEVO outperforms the current methods. It thus refines the previously suggested alignments based on topological information only. Conclusion: With GEDEVO, we account for the constantly exploding number and size of available biological networks. The software as well as all used data sets are publicly available at http://gedevo.mpi-inf.mpg.de.
Cite as

Rashid Ibragimov, Maximilian Malek, Jiong Guo, and Jan Baumbach. GEDEVO: An Evolutionary Graph Edit Distance Algorithm for Biological Network Alignment. In German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. 68-79, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

Copy BibTex To Clipboard

@InProceedings{ibragimov_et_al:OASIcs.GCB.2013.68,
  author =	{Ibragimov, Rashid and Malek, Maximilian and Guo, Jiong and Baumbach, Jan},
  title =	{{GEDEVO: An Evolutionary Graph Edit Distance Algorithm for Biological Network Alignment}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{68--79},
  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.2013.68},
  URN =		{urn:nbn:de:0030-drops-42298},
  doi =		{10.4230/OASIcs.GCB.2013.68},
  annote =	{Keywords: Network Alignment, Graph Edit Distance, Evolutionary Algorithm, Protein-Protein Interactions}
}
Document
DOI: 10.4230/OASIcs.GCB.2013.80
Dinucleotide distance histograms for fast detection of rRNA in metatranscriptomic sequences

Authors: Heiner Klingenberg, Robin Martinjak, Frank Oliver Glöckner, Rolf Daniel, Thomas Lingner, and Peter Meinicke

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

Abstract
With the advent of metatranscriptomics it has now become possible to study the dynamics of microbial communities. The analysis of environmental RNA-Seq data implies several challenges for the development of efficient tools in bioinformatics. One of the first steps in the computational analysis of metatranscriptomic sequencing reads requires the separation of rRNA and mRNA fragments to ensure that only protein coding sequences are actually used in a subsequent functional analysis. In the context of the rRNA filtering task it is desirable to have a broad spectrum of different methods in order to find a suitable trade-off between speed and accuracy for a particular dataset. We introduce a machine learning approach for the detection of rRNA in metatranscriptomic sequencing reads that is based on support vector machines in combination with dinucleotide distance histograms for feature representation. The results show that our SVM-based approach is at least one order of magnitude faster than any of the existing tools with only a slight degradation of the detection performance when compared to state-of-the-art alignment-based methods.
Cite as

Heiner Klingenberg, Robin Martinjak, Frank Oliver Glöckner, Rolf Daniel, Thomas Lingner, and Peter Meinicke. Dinucleotide distance histograms for fast detection of rRNA in metatranscriptomic sequences. In German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. 80-89, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

Copy BibTex To Clipboard

@InProceedings{klingenberg_et_al:OASIcs.GCB.2013.80,
  author =	{Klingenberg, Heiner and Martinjak, Robin and Gl\"{o}ckner, Frank Oliver and Daniel, Rolf and Lingner, Thomas and Meinicke, Peter},
  title =	{{Dinucleotide distance histograms for fast detection of rRNA in metatranscriptomic sequences}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{80--89},
  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.2013.80},
  URN =		{urn:nbn:de:0030-drops-42324},
  doi =		{10.4230/OASIcs.GCB.2013.80},
  annote =	{Keywords: Metatranscriptomics, metagenomics, rRNA detection, distance histograms}
}
Document
DOI: 10.4230/OASIcs.GCB.2013.90
Utilization of ordinal response structures in classification with high-dimensional expression data

Authors: Andreas Leha, Klaus Jung, and Tim Beißbarth

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

Abstract
Molecular diagnosis or prediction of clinical treatment outcome based on high-throughput genomics data is a modern application of machine learning techniques for clinical problems. In practice, clinical parameters, such as patient health status or toxic reaction to therapy, are often measured on an ordinal scale (e.g. good, fair, poor). Commonly, the prediction of ordinal end-points is treated as a multi-class classification problem, disregarding the ordering information contained in the response. This may result in a loss of prediction accuracy. Classical approaches to model ordinal response directly, including for instance the cumulative logit model, are typically not applicable to high-dimensional data. We present hierarchical twoing (hi2), a novel algorithm for classification of high-dimensional data into ordered categories. hi2 combines the power of well-understood binary classification with ordinal response prediction. A comparison of several approaches for ordinal classification on real world data as well as simulated data shows that classification algorithms especially designed to handle ordered categories fail to improve upon state-of-the-art non-ordinal classification algorithms. In general, the classification performance of an algorithm is dominated by its ability to deal with the high-dimensionality of the data. Only hi2 outperforms its competitors in the case of moderate effects.
Cite as

Andreas Leha, Klaus Jung, and Tim Beißbarth. Utilization of ordinal response structures in classification with high-dimensional expression data. In German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. 90-100, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

Copy BibTex To Clipboard

@InProceedings{leha_et_al:OASIcs.GCB.2013.90,
  author =	{Leha, Andreas and Jung, Klaus and Bei{\ss}barth, Tim},
  title =	{{Utilization of ordinal response structures in classification with high-dimensional expression data}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{90--100},
  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.2013.90},
  URN =		{urn:nbn:de:0030-drops-42340},
  doi =		{10.4230/OASIcs.GCB.2013.90},
  annote =	{Keywords: Classification, High-Dimensional Data, Ordinal Response, Expression Data}
}
Document
DOI: 10.4230/OASIcs.GCB.2013.101
Extended Sunflower Hidden Markov Models for the recognition of homotypic cis-regulatory modules}

Authors: Ioana M. Lemnian, Ralf Eggeling, and Ivo Grosse

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

Abstract
The transcription of genes is often regulated not only by transcription factors binding at single sites per promoter, but by the interplay of multiple copies of one or more transcription factors binding at multiple sites forming a cis-regulatory module. The computational recognition of cis-regulatory modules from ChIP-seq or other high-throughput data is crucial in modern life and medical sciences. A common type of cis-regulatory modules are homotypic clusters of binding sites, i.e., clusters of binding sites of one transcription factor. For their recognition the homotypic Sunflower Hidden Markov Model is a promising statistical model. However, this model neglects statistical dependences among nucleotides within binding sites and flanking regions, which makes it not well suited for de-novo motif discovery. Here, we propose an extension of this model that allows statistical dependences within binding sites, their reverse complements, and flanking regions. We study the efficacy of this extended homotypic Sunflower Hidden Markov Model based on ChIP-seq data from the Human ENCODE Project and find that it often outperforms the traditional homotypic Sunflower Hidden Markov Model.
Cite as

Ioana M. Lemnian, Ralf Eggeling, and Ivo Grosse. Extended Sunflower Hidden Markov Models for the recognition of homotypic cis-regulatory modules}. In German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. 101-109, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

Copy BibTex To Clipboard

@InProceedings{lemnian_et_al:OASIcs.GCB.2013.101,
  author =	{Lemnian, Ioana M. and Eggeling, Ralf and Grosse, Ivo},
  title =	{{Extended Sunflower Hidden Markov Models for the recognition of homotypic cis-regulatory modules\}}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{101--109},
  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.2013.101},
  URN =		{urn:nbn:de:0030-drops-42361},
  doi =		{10.4230/OASIcs.GCB.2013.101},
  annote =	{Keywords: Hidden Markov Models, cis-regulatory modules, de-novo motif discovery}
}
  • Refine by Type
  • 17 Document/PDF
  • 3 Document/HTML
  • 1 Volume

  • Refine by Publication Year
  • 3 2025
  • 15 2013

  • Refine by Author
  • 3 Beißbarth, Tim
  • 3 Leha, Andreas
  • 3 Morgenstern, Burkhard
  • 2 Blanca, Antonio
  • 2 Kollmar, Martin
  • Show More...

  • Refine by Series/Journal
  • 2 LIPIcs
  • 15 OASIcs

  • Refine by Classification
  • 2 Applied computing → Computational biology
  • 1 Applied computing → Bioinformatics
  • 1 Mathematics of computing → Probabilistic inference problems
  • 1 Theory of computation → Data structures design and analysis
  • 1 Theory of computation → Design and analysis of algorithms
  • Show More...

  • Refine by Keyword
  • 2 k-mers
  • 2 metagenomics
  • 1 Alignment-free sequence comparison
  • 1 Bayesian Networks
  • 1 Burrows-Wheeler Transform
  • Show More...

Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact