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11 results on '"Heiner Klingenberg"'

1. How to normalize metatranscriptomic count data for differential expression analysis

Author

Heiner Klingenberg and Peter Meinicke

Subjects
Statistical modeling, Metatransriptomics, Differential expression analysis, RNA-Seq, Count data, Normalization, Medicine, Biology (General), QH301-705.5
Abstract

Background Differential expression analysis on the basis of RNA-Seq count data has become a standard tool in transcriptomics. Several studies have shown that prior normalization of the data is crucial for a reliable detection of transcriptional differences. Until now it has not been clear whether and how the transcriptomic approach can be used for differential expression analysis in metatranscriptomics. Methods We propose a model for differential expression in metatranscriptomics that explicitly accounts for variations in the taxonomic composition of transcripts across different samples. As a main consequence the correct normalization of metatranscriptomic count data under this model requires the taxonomic separation of the data into organism-specific bins. Then the taxon-specific scaling of organism profiles yields a valid normalization and allows us to recombine the scaled profiles into a metatranscriptomic count matrix. This matrix can then be analyzed with statistical tools for transcriptomic count data. For taxon-specific scaling and recombination of scaled counts we provide a simple R script. Results When applying transcriptomic tools for differential expression analysis directly to metatranscriptomic data with an organism-independent (global) scaling of counts the resulting differences may be difficult to interpret. The differences may correspond to changing functional profiles of the contributing organisms but may also result from a variation of taxonomic abundances. Taxon-specific scaling eliminates this variation and therefore the resulting differences actually reflect a different behavior of organisms under changing conditions. In simulation studies we show that the divergence between results from global and taxon-specific scaling can be drastic. In particular, the variation of organism abundances can imply a considerable increase of significant differences with global scaling. Also, on real metatranscriptomic data, the predictions from taxon-specific and global scaling can differ widely. Our studies indicate that in real data applications performed with global scaling it might be impossible to distinguish between differential expression in terms of transcriptomic changes and differential composition in terms of changing taxonomic proportions. Conclusions As in transcriptomics, a proper normalization of count data is also essential for differential expression analysis in metatranscriptomics. Our model implies a taxon-specific scaling of counts for normalization of the data. The application of taxon-specific scaling consequently removes taxonomic composition variations from functional profiles and therefore provides a clear interpretation of the observed functional differences.

Published
2017
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4. BinChecker: a new algorithm for quality assessment of microbial draft genomes

Author

Heiner Klingenberg and Peter Meinicke

Subjects
0303 health sciences, Computer science, Feature selection, 02 engineering and technology, computer.software_genre, Genome, Domain (software engineering), 03 medical and health sciences, Identification (information), ComputingMethodologies_PATTERNRECOGNITION, Metagenomics, Feature (computer vision), 020204 information systems, Completeness (order theory), 0202 electrical engineering, electronic engineering, information engineering, Data mining, Cluster analysis, computer, 030304 developmental biology
Abstract

In the reconstruction of microbial genomes from metagenomic sequence data, the estimation of the final completeness and possible contamination is crucial for quality control. In metagenomics candidate genomes are usually obtained from a metagenome assembly and a subsequent binning of the assembled contigs. BinChecker provides a novel approach to quality assessment that is based on a fast protein domain search and a clustering approach for identification of marker domain (“feature”) sets. The feature sets that are used for estimation are not pre-computed for a given database of reference genomes, but are individually found for each bin by adaptive clustering and feature selection. In particular, the adaptivity facilitates the creation and extension of the underlying database, which just requires to add protein feature profiles of reference genomes. Tests with simulated bins indicate that the prediction accuracy of BinChecker meets the current state of the art while providing significant advantages in terms of speed and flexibility.

Published
2021
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5. Identification of New Fungal Peroxisomal Matrix Proteins and Revision of the PTS1 Consensus

Author

Sven Thoms, Thomas Lingner, Heiner Klingenberg, and Christopher Nötzel

Subjects
0301 basic medicine, Peroxisomal matrix, Peroxisomal Targeting Signal 1, In silico, Peroxisome-Targeting Signal 1 Receptor, Cell Biology, Biology, Peroxisome, Biochemistry, 3. Good health, Cell biology, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Structural Biology, Genetics, Consensus sequence, Molecular Biology, Peroxisomal targeting signal, Peptide sequence, 030217 neurology & neurosurgery
Abstract

The peroxisomal targeting signal type 1 (PTS1) is a seemingly simple peptide sequence at the C-terminal end of most peroxisomal matrix proteins. PTS1 can be described as a tripeptide with the consensus motif [S/A/C] [K/R/H] L. However, this description is neither necessary nor sufficient. It does not cover all cases of PTS1 proteins, and some proteins in accordance with this consensus do not target to the peroxisome. In order to find new PTS proteins in yeast and to arrive at a more complete description of the PTS1 consensus motif, we developed a machine learning approach that involves orthologue expansion of the set of known peroxisomal proteins. We performed a genome-wide in silico screen, characterised several PTS1-containing peptides and identified two new peroxisomal matrix proteins, which we named Pxp1 (Yel020c) and Pxp2 (Yjr111c). Based on these in silico and in vivo analyses, we revised the yeast PTS1 consensus which now includes all known PTS1 proteins.

Published
2016
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6. Critical Assessment of Metagenome Interpretation – a benchmark of computational metagenomics software

Author

Alexander Sczyrba, Peter Hofmann, Peter Belmann, David Koslicki, Stefan Janssen, Johannes Dröge, Ivan Gregor, Stephan Majda, Jessika Fiedler, Eik Dahms, Andreas Bremges, Adrian Fritz, Ruben Garrido-Oter, Tue Sparholt Jørgensen, Nicole Shapiro, Philip D. Blood, Alexey Gurevich, Yang Bai, Dmitrij Turaev, Matthew Z. DeMaere, Rayan Chikhi, Niranjan Nagarajan, Christopher Quince, Fernando Meyer, Monika Balvoit, Lars Hestbjerg Hansen, Søren J. Sørensen, Burton K. H. Chia, Bertrand Denis, Jeff L. Froula, Zhong Wang, Robert Egan, Dongwan Don Kang, Jeffrey J. Cook, Charles Deltel, Michael Beckstette, Claire Lemaitre, Pierre Peterlongo, Guillaume Rizk, Dominique Lavenier, Yu-Wei Wu, Steven W. Singer, Chirag Jain, Marc Strous, Heiner Klingenberg, Peter Meinicke, Michael Barton, Thomas Lingner, Hsin-Hung Lin, Yu-Chieh Liao, Genivaldo Gueiros Z. Silva, Daniel A. Cuevas, Robert A. Edwards, Surya Saha, Vitor C. Piro, Bernhard Y. Renard, Mihai Pop, Hans-Peter Klenk, Markus Göker, Nikos C. Kyrpides, Tanja Woyke, Julia A. Vorholt, Paul Schulze-Lefert, Edward M. Rubin, Aaron E. Darling, Thomas Rattei, Alice C. McHardy, Center for Biotechnology (CeBiTec), Universität Bielefeld = Bielefeld University, Technische Fakultät, Universität Bielefeld, Algorithmische Bioinformatik [Düsseldorf], Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Computational Biology of Infection Research [Braunschweig], Helmholtz Centre for Infection Research (HZI), Braunschweig Integrated Centre of Systems Biology [Braunschweig] (BRICS), Technische Universität Braunschweig = Technical University of Braunschweig [Braunschweig]-Helmholtz Centre for Infection Research (HZI), Department of Mathematics [Corvallis, Oregon], Oregon State University (OSU), Department of Computer Science and Engineering [Univ California San Diego] (CSE - UC San Diego), University of California [San Diego] (UC San Diego), University of California (UC)-University of California (UC), Department of Pediatrics [Univ California San Diego] (UC San Diego), School of Medicine [Univ California San Diego] (UC San Diego), University of California (UC)-University of California (UC)-University of California [San Diego] (UC San Diego), Max Planck Institute for Informatics [Saarbrücken], Faculty of Biology [Essen], Universität Duisburg-Essen = University of Duisburg-Essen [Essen], German Center for Infection Research - partner site Hannover-Braunschweig (DZIF), Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research (MPIPZ), Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf]-Max Planck Institute for Plant Breeding Research (MPIPZ)-Universität zu Köln = University of Cologne, Department of Environmental Science [Roskilde] (ENVS), Aarhus University [Aarhus], Section of Microbiology [Copenhagen], Department of Biology [Copenhagen], Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH)-Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Department of Science and Environment [Roskilde], Roskilde University, DOE Joint Genome Institute [Walnut Creek], Pittsburgh Supercomputing Center (PSC), Center for Algorithmic Biotechnology [Saint Petersburg], Institute of Translational Biomedicine [Saint-Petersburg], Saint Petersburg University (SPBU)-Saint Petersburg University (SPBU), Centre of Excellence for Plant and Microbial Sciences (CEPAMS), John Innes Centre [Norwich], Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC)-Chinese Academy of Agricultural Sciences (CAAS), Department of Microbiology and Ecosystem Science [Vienna], University of Vienna [Vienna], iThree Institute, University of Technology Sydney (UTS), Bioinformatics and Sequence Analysis (BONSAI), Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Department of Computational and Systems Biology [Singapore], Genome Institute of Singapore (GIS), Department of Microbiology and Infection [Coventry], Warwick Medical School, University of Warwick [Coventry]-University of Warwick [Coventry], Intel Corporation [Hillsboro], Intel Corporation [USA], Scalable, Optimized and Parallel Algorithms for Genomics (GenScale), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Department of Molecular Infection Biology [Braunschweig], Joint BioEnergy Institute [Emeryville], Graduate Institute of Biomedical Informatics [Taipei], Taipei Medical University, Biological Systems and Engineering [LBNL Berkeley], Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Max planck Institute for Biology of Ageing [Cologne], Energy Engineering and Geomicrobiology [Calgary], University of Calgary, Institute of Microbiology and Genetics [Göttingen], Georg-August-University = Georg-August-Universität Göttingen, University Medical Center Göttingen (UMG), Institute of Population Health Sciences [Taiwan], National Health Research Institutes [Taiwan] (NHRI), San Diego State University (SDSU), Boyce Thompson Institute [Ithaca], Robert Koch Institute [Berlin] (RKI), Ministry of Education [Brazil], Center for Bioinformatics and Computational Biology [Maryland] (CBCB), University of Maryland [College Park], University of Maryland System-University of Maryland System, School of Biology [Newcastle upon Tyne], Newcastle University [Newcastle], Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH / Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures (DSMZ), biological sciences department [Jeddah], King Abdulaziz University, Institute of Microbiology [Zurich], Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Department of Computer Science and Engineering [San Diego] (CSE-UCSD), University of California-University of California, Department of Pediatrics [san Diego], UC San Diego School of Medicine, Universität Duisburg-Essen [Essen], Universität zu Köln-Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf]-Max Planck Institute for Plant Breeding Research (MPIPZ), University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU)-Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), John Innes Centre [Norwich]-Chinese Academy of Agricultural Sciences (CAAS), Centre National de la Recherche Scientifique (CNRS)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Georg-August-University [Göttingen], Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)

Subjects
0303 health sciences, Biological data, business.industry, Benchmarking, Biology, Data science, Genome, 03 medical and health sciences, 0302 clinical medicine, Software, Metagenomics, Profiling (information science), Critical assessment, Taxonomic rank, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

In metagenome analysis, computational methods for assembly, taxonomic profiling and binning are key components facilitating downstream biological data interpretation. However, a lack of consensus about benchmarking datasets and evaluation metrics complicates proper performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on datasets of unprecedented complexity and realism. Benchmark metagenomes were generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids, including genomes with varying degrees of relatedness to each other and to publicly available ones and representing common experimental setups. Across all datasets, assembly and genome binning programs performed well for species represented by individual genomes, while performance was substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below the family level. Parameter settings substantially impacted performances, underscoring the importance of program reproducibility. While highlighting current challenges in computational metagenomics, the CAMI results provide a roadmap for software selection to answer specific research questions.

Published
2017
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7. Protein signature-based estimation of metagenomic abundances including all domains of life and viruses

Author

Thomas Lingner, Peter Meinicke, Kathrin Petra Aßhauer, and Heiner Klingenberg

Subjects
Statistics and Probability, Computational biology, Biology, Biochemistry, Genome, 03 medical and health sciences, Three-domain system, Humans, Profiling (information science), Molecular Biology, Phylogeny, 030304 developmental biology, 0303 health sciences, Ecology, 030302 biochemistry & molecular biology, Community structure, Mixture model, Original Papers, Protein Structure, Tertiary, Computer Science Applications, Computational Mathematics, DNA, Archaeal, Computational Theory and Mathematics, Metagenomics, DNA, Viral, Data analysis, Metagenome, Sequence Analysis, Human Microbiome Project
Abstract

Motivation: Metagenome analysis requires tools that can estimate the taxonomic abundances in anonymous sequence data over the whole range of biological entities. Because there is usually no prior knowledge about the data composition, not only all domains of life but also viruses have to be included in taxonomic profiling. Such a full-range approach, however, is difficult to realize owing to the limited coverage of available reference data. In particular, archaea and viruses are generally not well represented by current genome databases. Results: We introduce a novel approach to taxonomic profiling of metagenomes that is based on mixture model analysis of protein signatures. Our results on simulated and real data reveal the difficulties of the existing methods when measuring achaeal or viral abundances and show the overall good profiling performance of the protein-based mixture model. As an application example, we provide a large-scale analysis of data from the Human Microbiome Project. This demonstrates the utility of our method as a first instance profiling tool for a fast estimate of the community structure. Availability: http://gobics.de/TaxyPro. Contact: pmeinic@gwdg.de Supplementary information: Supplementary Material is available at Bioinformatics online.

Published
2013
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8. Critical Assessment of Metagenome Interpretation - A benchmark of metagenomics software

Author

Andreas Bremges, Charles Deltel, Jeff Froula, Marc Strous, Tue Sparholt Jørgensen, Alexander Sczyrba, Søren J. Sørensen, Michael D. Barton, Hsin-Hung Lin, Aaron E. Darling, Nikos C. Kyrpides, Surya Saha, Claire Lemaitre, Ivan Gregor, Julia A. Vorholt, Chirag Jain, David Koslicki, Alexey Gurevich, Lars Hestbjerg Hansen, Dominique Lavenier, Steven W. Singer, Peter Hofmann, Philip D. Blood, Peter Belmann, Stephan Majda, Jeffrey J. Cook, Dongwan Don Kang, Peter Meinicke, Genivaldo G. Z. Silva, Michael Beckstette, Matthew Z. DeMaere, Yang Bai, Zhong Wang, Monika Balvočiūtė, Alice C. McHardy, Hans-Peter Klenk, Yu Wei Wu, Burton Kuan Hui Chia, Markus Göker, Nicole Shapiro, Rayan Chikhi, Thomas Rattei, Christopher Quince, Edward M. Rubin, Niranjan Nagarajan, Paul Schulze-Lefert, Thomas Lingner, Tanja Woyke, Bernhard Y. Renard, Eik Dahms, Robert Egan, Pierre Peterlongo, Yu-Chieh Liao, Daniel A. Cuevas, Mihai Pop, Dmitrij Turaev, Robert Edwards, Vitor C. Piro, Guillaume Rizk, Fernando Meyer, Jessika Fiedler, Adrian Fritz, Ruben Garrido-Oter, Bertrand Denis, Johannes Dröge, Stefan Janssen, and Heiner Klingenberg

Subjects
0301 basic medicine, Technology, DATASETS, Computer science, GENOMES, Computational biology, Medical and Health Sciences, Biochemistry, Genome, Article, CONTIGS, 03 medical and health sciences, Software, Profiling (information science), MICROBIAL COMMUNITIES, Taxonomic rank, Molecular Biology, SEQUENCES, IDENTIFICATION, business.industry, Sequence Analysis, DNA, DNA, Cell Biology, Benchmarking, Biological Sciences, DEFINITION, 030104 developmental biology, Metagenomics, Software selection, MARKER GENES, Critical assessment, Metagenome Interpretation, RAPID RECONSTRUCTION, business, Biologie, Sequence Analysis, Algorithms, Developmental Biology, Biotechnology
Abstract

Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions., Nature Methods, 14 (11), ISSN:1548-7105, ISSN:1548-7091

Published
2017
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9. Identification of New Fungal Peroxisomal Matrix Proteins and Revision of the PTS1 Consensus

Author

Christopher, Nötzel, Thomas, Lingner, Heiner, Klingenberg, and Sven, Thoms

Subjects
Machine Learning, Saccharomyces cerevisiae Proteins, Peroxisome-Targeting Signal 1 Receptor, Amino Acid Motifs, Consensus Sequence, Databases, Genetic, Peroxisomes, Receptors, Cytoplasmic and Nuclear, Saccharomyces cerevisiae, Genome, Fungal, Protein Sorting Signals, Genome-Wide Association Study
Abstract

The peroxisomal targeting signal type 1 (PTS1) is a seemingly simple peptide sequence at the C-terminal end of most peroxisomal matrix proteins. PTS1 can be described as a tripeptide with the consensus motif [S/A/C] [K/R/H] L. However, this description is neither necessary nor sufficient. It does not cover all cases of PTS1 proteins, and some proteins in accordance with this consensus do not target to the peroxisome. In order to find new PTS proteins in yeast and to arrive at a more complete description of the PTS1 consensus motif, we developed a machine learning approach that involves orthologue expansion of the set of known peroxisomal proteins. We performed a genome-wide in silico screen, characterised several PTS1-containing peptides and identified two new peroxisomal matrix proteins, which we named Pxp1 (Yel020c) and Pxp2 (Yjr111c). Based on these in silico and in vivo analyses, we revised the yeast PTS1 consensus which now includes all known PTS1 proteins.

Published
2016

10. Visual attention to variation in female facial skin color distribution

Author

Bettina Weege, Bernhard Fink, Sebastian Kuntze, Paul J. Matts, Heiner Klingenberg, and Karl Grammer

Subjects
Attractiveness, Adult, Male, medicine.medical_specialty, Visual perception, genetic structures, Adolescent, Eye Movements, Skin Pigmentation, Dermatology, Audiology, Stimulus (physiology), 050105 experimental psychology, White People, 030207 dermatology & venereal diseases, 03 medical and health sciences, Beauty, Judgment, 0302 clinical medicine, medicine, Photography, Visual attention, Humans, 0501 psychology and cognitive sciences, Computer vision, Attention, 10. No inequality, Analysis of Variance, integumentary system, business.industry, 05 social sciences, Age Factors, Eye movement, Middle Aged, Attraction, Facial skin, Face, Visual Perception, Eye tracking, Female, Artificial intelligence, Psychology, business, Photic Stimulation
Abstract

Summary Objectives Visible skin condition of women is argued to influence human physical attraction. Recent research has shown that people are sensitive to variation in skin color distribution, and such variation affects visual perception of female facial attractiveness, healthiness, and age. Methods The eye gaze of 39 males and females, aged 13 to 45 years, was tracked while they viewed images of shape- and topography-standardized stimulus faces that varied only in terms of skin color distribution. Results The number of fixations and dwell time were significantly higher when viewing stimulus faces with the homogeneous skin color distribution of young people, compared with those of more elderly people. In accordance with recent research, facial stimuli with even skin tones were also judged to be younger and received higher attractiveness ratings. Finally, visual attention measures were negatively correlated with perceived age, but positively associated with attractiveness judgments. Conclusions Variation in visible skin color distribution (independent of facial form and skin surface topography) is able to selectively attract people’s attention toward female faces, and this higher attention results in more positive statements about a woman’s face.

Published
2008

11. Dinucleotide distance histograms for fast detection of rRNA in metatranscriptomic sequences

Author

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

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.

Published
2013
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