Volume
OASIcs, Volume 26
German Conference on Bioinformatics 2012
- Part of: Series: Open Access Series in Informatics (OASIcs)
Event
GCB 2012, September 20-22, 2012, Jena, GermanyEditors
Publication Details
- published at: 2012-09-13
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- ISBN: 978-3-939897-44-6
- DBLP: db/conf/gcb/gcb2012
Access Numbers
- Detailed Access Statistics available here
-
Total Document Accesses (updated on a weekly basis):
0PDF Downloads
Documents
No documents found matching your filter selection.
Document
Complete Volume
OASIcs, Volume 26, GCB'12, Complete Volume
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)
Copy BibTex To Clipboard
@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.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
Frontmatter, Table of Contents, Preface, Programm Committee, Supportes and Sponsors, Index of Authors
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)
Copy BibTex To Clipboard
@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.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
ModeScore: A Method to Infer Changed Activity of Metabolic Function from Transcript Profiles
Abstract
Genome-wide transcript profiles are often the only available quantitative data for a particular perturbation of a cellular system and their interpretation with respect to the metabolism is a major challenge in systems biology, especially beyond on/off distinction of genes.
We present a method that predicts activity changes of metabolic functions by scoring reference flux distributions based on relative transcript profiles, providing a ranked list of most regulated functions. Then, for each metabolic function, the involved genes are ranked upon how much they represent a specific regulation pattern. Compared with the naïve pathway-based approach, the reference modes can be chosen freely, and they represent full metabolic functions, thus, directly provide testable hypotheses for the metabolic study.
In conclusion, the novel method provides promising functions for subsequent experimental elucidation together with outstanding associated genes, solely based on transcript profiles.
Cite as
Andreas Hoppe and Hermann-Georg Holzhütter. ModeScore: A Method to Infer Changed Activity of Metabolic Function from Transcript Profiles. In German Conference on Bioinformatics 2012. Open Access Series in Informatics (OASIcs), Volume 26, pp. 1-11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)
Copy BibTex To Clipboard
@InProceedings{hoppe_et_al:OASIcs.GCB.2012.1,
author = {Hoppe, Andreas and Holzh\"{u}tter, Hermann-Georg},
title = {{ModeScore: A Method to Infer Changed Activity of Metabolic Function from Transcript Profiles}},
booktitle = {German Conference on Bioinformatics 2012},
pages = {1--11},
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.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2012.1},
URN = {urn:nbn:de:0030-drops-37134},
doi = {10.4230/OASIcs.GCB.2012.1},
annote = {Keywords: Metabolic network, expression profile, metabolic function}
}
Document
Comparing Fragmentation Trees from Electron Impact Mass Spectra with Annotated Fragmentation Pathways
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)
Copy BibTex To Clipboard
@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.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
Finding Characteristic Substructures for Metabolite Classes
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)
Copy BibTex To Clipboard
@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.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
A Two-Step Soft Segmentation Procedure for MALDI Imaging Mass Spectrometry Data
Abstract
We propose a new method for soft spatial segmentation of matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) data which is based on probabilistic clustering with subsequent smoothing. Clustering of spectra is done with the Latent Dirichlet Allocation (LDA) model. Then, clustering results are smoothed with a Markov random field (MRF) resulting in a soft probabilistic segmentation map. We show several extensions of the basic MRF model specifically tuned for MALDI-IMS data segmentation. We describe a highly parallel implementation of the smoothing algorithm based on GraphLab framework and show experimental results.
Cite as
Ilya Chernyavsky, Theodore Alexandrov, Peter Maass, and Sergey I. Nikolenko. A Two-Step Soft Segmentation Procedure for MALDI Imaging Mass Spectrometry Data. In German Conference on Bioinformatics 2012. Open Access Series in Informatics (OASIcs), Volume 26, pp. 39-48, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)
Copy BibTex To Clipboard
@InProceedings{chernyavsky_et_al:OASIcs.GCB.2012.39,
author = {Chernyavsky, Ilya and Alexandrov, Theodore and Maass, Peter and Nikolenko, Sergey I.},
title = {{A Two-Step Soft Segmentation Procedure for MALDI Imaging Mass Spectrometry Data}},
booktitle = {German Conference on Bioinformatics 2012},
pages = {39--48},
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.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2012.39},
URN = {urn:nbn:de:0030-drops-37163},
doi = {10.4230/OASIcs.GCB.2012.39},
annote = {Keywords: MALDI imaging mass spectrometry, hyperspectral image segmentation, probabilistic graphical models, latent Dirichlet allocation, Markov random field}
}
Document
Building and Documenting Workflows with Python-Based Snakemake
Abstract
Snakemake is a novel workflow engine with a simple Python-derived workflow definition language and an optimizing execution environment. It is the first system that supports multiple named wildcards (or variables) in input and output filenames of each rule definition. It also allows to write human-readable workflows that document themselves. We have found Snakemake especially useful for building high-throughput sequencing data analysis pipelines and present examples from this area. Snakemake exemplifies a generic way to implement a domain specific language in python, without writing a full parser or introducing syntactical overhead by overloading language features.
Cite as
Johannes Köster and Sven Rahmann. Building and Documenting Workflows with Python-Based Snakemake. In German Conference on Bioinformatics 2012. Open Access Series in Informatics (OASIcs), Volume 26, pp. 49-56, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)
Copy BibTex To Clipboard
@InProceedings{koster_et_al:OASIcs.GCB.2012.49,
author = {K\"{o}ster, Johannes and Rahmann, Sven},
title = {{Building and Documenting Workflows with Python-Based Snakemake}},
booktitle = {German Conference on Bioinformatics 2012},
pages = {49--56},
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.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2012.49},
URN = {urn:nbn:de:0030-drops-37179},
doi = {10.4230/OASIcs.GCB.2012.49},
annote = {Keywords: workflow engine, dependency graph, knapsack problem, Python, high-throughput sequencing, next-generation sequencing}
}
Document
Online Transitivity Clustering of Biological Data with Missing Values
Abstract
Motivation: Equipped with sophisticated biochemical measurement techniques we generate a massive amount of biomedical data that needs to be analyzed computationally. One long-standing challenge in automatic knowledge extraction is clustering. We seek to partition a set of objects into groups such that the objects within the clusters share common traits. Usually, we have given a similarity matrix computed from a pairwise similarity function. While many approaches for biomedical data clustering exist, most methods neglect two important problems: (1) Computing the similarity matrix might not be trivial but resource-intense. (2) A clustering algorithm itself is not sufficient for the biologist, who needs an integrated online system capable of performing preparative and follow-up tasks as well.
Results: Here, we present a significantly extended version of Transitivity Clustering. Our first main contribution is its’ capability of dealing with missing values in the similarity matrix such that we save time and memory. Hence, we reduce one main bottleneck of computing all pairwise similarity values. We integrated this functionality into the Weighted Graph Cluster Editing model underlying Transitivity Clustering. By means of identifying protein (super)families from incomplete all-vs-all BLAST results we demonstrate the robustness of our approach. While most tools concentrate on the partitioning process itself, we present a new, intuitive web interface that aids with all important steps of a cluster analysis: (1) computing and post-processing of a similarity matrix, (2) estimation of a meaningful density parameter, (3) clustering, (4) comparison with given gold standards, and (5) fine-tuning of the clustering by varying the parameters.
Availability: Transitivity Clustering, the new Cost Matrix Creator, all used data sets as well as an online documentation are online available at http://transclust.mmci.uni-saarland.de/.
Cite as
Richard Röttger, Christoph Kreutzer, Thuy Duong Vu, Tobias Wittkop, and Jan Baumbach. Online Transitivity Clustering of Biological Data with Missing Values. In German Conference on Bioinformatics 2012. Open Access Series in Informatics (OASIcs), Volume 26, pp. 57-68, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)
Copy BibTex To Clipboard
@InProceedings{rottger_et_al:OASIcs.GCB.2012.57,
author = {R\"{o}ttger, Richard and Kreutzer, Christoph and Vu, Thuy Duong and Wittkop, Tobias and Baumbach, Jan},
title = {{Online Transitivity Clustering of Biological Data with Missing Values}},
booktitle = {German Conference on Bioinformatics 2012},
pages = {57--68},
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.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2012.57},
URN = {urn:nbn:de:0030-drops-37189},
doi = {10.4230/OASIcs.GCB.2012.57},
annote = {Keywords: Transitivity Clustering, Large Scale clustering, Missing Values, Web Interface}
}
Document
ConReg: Analysis and Visualization of Conserved Regulatory Networks in Eukaryotes
Abstract
Transcription factors (TFs) play a fundamental role in cellular regulation by binding to promoter regions of target genes (TGs) in order to control their gene expression. TF-TG networks are widely used as representations of regulatory mechanisms, e.g. for modeling the cellular response to input signals and perturbations.
As the experimental identification of regulatory interactions is time consuming and expensive, one tries to use knowledge from related species when studying an organism of interest. Here, we present ConReg, an interactive web application to store regulatory relations for various species and to investigate their level of conservation in related species. Currently, ConReg contains data for eight model organisms. The regulatory relations stored in publicly available databases cover only a small fraction both of the actual interactions and also of the regulatory relations described in the scientific literature. Therefore, we included regulatory relations extracted from PubMed and PubMedCentral using sophisticated text-mining approaches and from binding site predictions into ConReg.
We applied ConReg for the investigation of conserved regulatory motifs in D. melanogaster. From the 471 regulatory relations in REDfly our system was able to identify 66 confirmed conserved regulations in at least one vertebrate model organism (H. sapiens, M. musculus, R. norvegicus, D. rerio). The conserved network consists among others of the well studied motifs for eye-development and the pan-bilaterian kernel for heart specification, which are well-known examples for conserved regulatory relations between different organisms.
ConReg is available at http://services.bio.ifi.lmu.de/ConReg/ and can be used to analyze and visualize regulatory networks and their conservation among eight model organisms. It also provides direct links to annotations including literature references to potentially conserved regulatory relations.
Cite as
Robert Pesch, Matthias Böck, and Ralf Zimmer. ConReg: Analysis and Visualization of Conserved Regulatory Networks in Eukaryotes. In German Conference on Bioinformatics 2012. Open Access Series in Informatics (OASIcs), Volume 26, pp. 69-81, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)
Copy BibTex To Clipboard
@InProceedings{pesch_et_al:OASIcs.GCB.2012.69,
author = {Pesch, Robert and B\"{o}ck, Matthias and Zimmer, Ralf},
title = {{ConReg: Analysis and Visualization of Conserved Regulatory Networks in Eukaryotes}},
booktitle = {German Conference on Bioinformatics 2012},
pages = {69--81},
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.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2012.69},
URN = {urn:nbn:de:0030-drops-37194},
doi = {10.4230/OASIcs.GCB.2012.69},
annote = {Keywords: web application, evolutionary biology, regulatory networks, text-mining}
}
Document
Designing q-Unique DNA Sequences with Integer Linear Programs and Euler Tours in De Bruijn Graphs
Abstract
DNA nanoarchitechtures require carefully designed oligonucleotides with certain non-hybridization guarantees, which can be formalized as the q-uniqueness property on the sequence level. We study the optimization problem of finding a longest q-unique DNA sequence. We first present a convenient formulation as an integer linear program on the underlying De Bruijn graph that allows to flexibly incorporate a variety of constraints; solution times for practically relevant values of q are short. We then provide additional insights into the problem structure using the quotient graph of the De Bruijn graph with respect to the equivalence relation induced by reverse complementarity. Specifically, for odd q the quotient graph is Eulerian, so finding a longest q-unique sequence is equivalent to finding an Euler tour and solved in linear time with respect to the output string length. For even q, self-complementary edges complicate the problem, and the graph has to be Eulerized by deleting a minimum number of edges. Two sub-cases arise, for one of which we present a complete solution, while the other one remains open.
Cite as
Marianna D'Addario, Nils Kriege, and Sven Rahmann. Designing q-Unique DNA Sequences with Integer Linear Programs and Euler Tours in De Bruijn Graphs. In German Conference on Bioinformatics 2012. Open Access Series in Informatics (OASIcs), Volume 26, pp. 82-92, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)
Copy BibTex To Clipboard
@InProceedings{daddario_et_al:OASIcs.GCB.2012.82,
author = {D'Addario, Marianna and Kriege, Nils and Rahmann, Sven},
title = {{Designing q-Unique DNA Sequences with Integer Linear Programs and Euler Tours in De Bruijn Graphs}},
booktitle = {German Conference on Bioinformatics 2012},
pages = {82--92},
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.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2012.82},
URN = {urn:nbn:de:0030-drops-37200},
doi = {10.4230/OASIcs.GCB.2012.82},
annote = {Keywords: DNA sequence design, De Bruijn graph, quotient graph, reverse complement, Euler graph, Euler tour}
}
Document
Polyglutamine and Polyalanine Tracts Are Enriched in Transcription Factors of Plants
Abstract
Polyglutamine (polyQ) tracts have been studied extensively for their roles in a number of human diseases such as Huntington's or different Ataxias. However, it has also been recognized that polyQ tracts are abundant and may have important functional and evolutionary roles. Especially the association of polyQ and also polyalanine (polyA) tracts with transcription factors and their activation activity has been noted. While a number of examples for this association have been found for proteins from opisthokonts (animals and fungi), only a few studies exist for polyQ and polyA stretches in plants, and systematic investigations of the significance of these repeats in plant transcription factors are scarce. Here, we analyze the abundance and length of polyQ and polyA stretches in the conceptual proteomes of six plant species and examine the connection between polyQ and polyA tracts and transcription factors of the repeat-containing proteins. We show that there is an association of polyQ stretches with transcription factors in plants. In grasses, transcription factors are also significantly enriched in polyA stretches. While there is variation in the abundance, length, and association with certain functions of polyQ and polyA stretches between different species, no general differences in the evolution of these repeats could be observed between plants and opisthokonts.
Cite as
Nina Kottenhagen, Lydia Gramzow, Fabian Horn, Martin Pohl, and Günter Theißen. Polyglutamine and Polyalanine Tracts Are Enriched in Transcription Factors of Plants. In German Conference on Bioinformatics 2012. Open Access Series in Informatics (OASIcs), Volume 26, pp. 93-107, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)
Copy BibTex To Clipboard
@InProceedings{kottenhagen_et_al:OASIcs.GCB.2012.93,
author = {Kottenhagen, Nina and Gramzow, Lydia and Horn, Fabian and Pohl, Martin and Thei{\ss}en, G\"{u}nter},
title = {{Polyglutamine and Polyalanine Tracts Are Enriched in Transcription Factors of Plants}},
booktitle = {German Conference on Bioinformatics 2012},
pages = {93--107},
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.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2012.93},
URN = {urn:nbn:de:0030-drops-37217},
doi = {10.4230/OASIcs.GCB.2012.93},
annote = {Keywords: tandem repeats, molecular evolution, GO annotation}
}
Document
Computation and Visualization of Protein Topology Graphs Including Ligand Information
Abstract
Motivation: Ligand information is of great interest to understand protein function. Protein structure topology can be modeled as a graph with secondary structure elements as vertices and spatial contacts between them as edges. Meaningful representations of such graphs in 2D are required for the visual inspection, comparison and analysis of protein folds, but their automatic visualization is still challenging. We present an approach which solves this task, supports different graph types and can optionally include ligand contacts.
Results: Our method extends the field of protein structure description and visualization by including ligand information. It generates a mathematically unique representation and high- quality 2D plots of the secondary structure of a protein based on a protein-ligand graph. This graph is computed from 3D atom coordinates in PDB files and the corresponding SSE assignments of the DSSP algorithm. The related software supports different notations and allows a rapid visualization of protein structures. It can also export graphs in various standard file formats so they can be used with other software. Our approach visualizes ligands in relationship to protein structure topology and thus represents a useful tool for exploring protein structures.
Availability: The software is released under an open source license and available at http://www.bioinformatik.uni-frankfurt.de/ in the Software section under Visualization of Protein Ligand Graphs.
Cite as
Tim Schäfer, Patrick May, and Ina Koch. Computation and Visualization of Protein Topology Graphs Including Ligand Information. In German Conference on Bioinformatics 2012. Open Access Series in Informatics (OASIcs), Volume 26, pp. 108-118, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)
Copy BibTex To Clipboard
@InProceedings{schafer_et_al:OASIcs.GCB.2012.108,
author = {Sch\"{a}fer, Tim and May, Patrick and Koch, Ina},
title = {{Computation and Visualization of Protein Topology Graphs Including Ligand Information}},
booktitle = {German Conference on Bioinformatics 2012},
pages = {108--118},
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.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2012.108},
URN = {urn:nbn:de:0030-drops-37226},
doi = {10.4230/OASIcs.GCB.2012.108},
annote = {Keywords: protein structure, graph theory, ligand, secondary structure, protein ligang graph}
}
Document
Unbiased Protein Interface Prediction Based on Ligand Diversity Quantification
Abstract
Proteins interact with each other to perform essential functions in cells. Consequently, identification of their binding interfaces can provide key information for drug design. Here, we introduce Weighted Protein Interface Prediction (WePIP), an original framework which predicts protein interfaces from homologous complexes. WePIP takes advantage of a novel weighted score which is not only based on structural neighbours' information but, unlike current state-of-the-art methods, also takes into consideration the nature of their interaction partners. Experimental validation demonstrates that our weighted schema significantly improves prediction performance. In particular, we have established a major contribution to ligand diversity quantification. Moreover, application of our framework on a standard dataset shows WePIP performance compares favourably with other state of the art methods.
Cite as
Reyhaneh Esmaielbeiki and Jean-Christophe Nebel. Unbiased Protein Interface Prediction Based on Ligand Diversity Quantification. In German Conference on Bioinformatics 2012. Open Access Series in Informatics (OASIcs), Volume 26, pp. 119-130, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)
Copy BibTex To Clipboard
@InProceedings{esmaielbeiki_et_al:OASIcs.GCB.2012.119,
author = {Esmaielbeiki, Reyhaneh and Nebel, Jean-Christophe},
title = {{Unbiased Protein Interface Prediction Based on Ligand Diversity Quantification}},
booktitle = {German Conference on Bioinformatics 2012},
pages = {119--130},
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.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2012.119},
URN = {urn:nbn:de:0030-drops-37235},
doi = {10.4230/OASIcs.GCB.2012.119},
annote = {Keywords: Protein-protein interaction, protein interface prediction, homology modeling}
}