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  • 51. Melzer, Nina
    et al.
    Wittenburg, Dörte
    Repsilber, Dirk
    Accounting for a complex genotype-phenotype map in milk phenotypes from genome-wide data2010In: Statistical Computing 2010: Abstracts der 42. Arbeitstagung, 2010, Vol. 5Conference paper (Refereed)
  • 52.
    Melzer, Nina
    et al.
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Wittenburg, Dörte
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Including metabolomic profiles to improve genetic value prediction: an integrated bioinformatics approach using weighted genome-wide marker information2011In: 12th Day of the Doctoal Student: abstracts; 19 May 2011, Dummerstorf / [ed] Seyfert, H.-M., Viereck, G., Dummerstorf, Germany: FBN , 2011, p. 55-58Conference paper (Refereed)
  • 53.
    Melzer, Nina
    et al.
    Institute for Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Wittenburg, Dörte
    Institute for Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Repsilber, Dirk
    Institute for Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Integrating milk metabolite profile information for the prediction of traditional milk traits based on SNP information for Holstein cows2013In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, no 8, article id e70256Article in journal (Refereed)
    Abstract [en]

    In this study the benefit of metabolome level analysis for the prediction of genetic value of three traditional milk traits was investigated. Our proposed approach consists of three steps: First, milk metabolite profiles are used to predict three traditional milk traits of 1,305 Holstein cows. Two regression methods, both enabling variable selection, are applied to identify important milk metabolites in this step. Second, the prediction of these important milk metabolite from single nucleotide polymorphisms (SNPs) enables the detection of SNPs with significant genetic effects. Finally, these SNPs are used to predict milk traits. The observed precision of predicted genetic values was compared to the results observed for the classical genotype-phenotype prediction using all SNPs or a reduced SNP subset (reduced classical approach). To enable a comparison between SNP subsets, a special invariable evaluation design was implemented. SNPs close to or within known quantitative trait loci (QTL) were determined. This enabled us to determine if detected important SNP subsets were enriched in these regions. The results show that our approach can lead to genetic value prediction, but requires less than 1% of the total amount of (40,317) SNPs., significantly more important SNPs in known QTL regions were detected using our approach compared to the reduced classical approach. Concluding, our approach allows a deeper insight into the associations between the different levels of the genotype-phenotype map (genotype-metabolome, metabolome-phenotype, genotype-phenotype).

  • 54.
    Melzer, Nina
    et al.
    Institute of Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Wittenburg, Dörte
    Institute of Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Repsilber, Dirk
    Institute of Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Investigating a complex genotype-phenotype map for the development of methods to predict genetic values based on genome-wide marker data – a simulation study for the livestock perspective2013In: Archiv für Tierzucht, ISSN 0003-9438, Vol. 56, no 38, p. 380-398Article in journal (Refereed)
    Abstract [en]

    Phenotypic variation can partly be explained by genetic variation, such as variation in single nucleotide polymorphism (SNP) genotypes. Genomic selection methods seek to predict genetic values (breeding values) based on SNP genotypes. To develop and to optimize these methods, simulated data are often used, which follow a rather simple genotype-phenotype map. Is the conventional approach for data simulation in this field an appropriate basis to optimize such methods in view of experimental data? Here, we present an alternative approach, striving to simulate more realistic data based on a genotype-phenotype map which includes a simulated metabolome level. This level was used to simulate genetic values, implicitly including additive and non-additive genetic effects, whereas in a conventional approach additive and dominance effects were explicitly simulated and assembled to genetic values. For both simulation approaches, different scenarios regarding numbers of quantitative trait loci (QTLs) and SNPs were analysed using fastBayesB as prediction method. We observed that our alternative map showed a smaller prediction precision (at least 3.75 %) compared to the conventional approach in all investigated scenarios. The observed degree of linearity is at least 94.12 % of the conventional approach or less. Additionally, we present results for different simulated data and experimental data to allow a comparison on a purely conceptual level. Concluding, simulating a more complex genotype-phenotype map including a molecular level, allows to study processing of variation from the genetic to the phenotype level in more detail and may prepare the ground for modern methods of genomic selection.

  • 55.
    Melzer, Nina
    et al.
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Wittenburg, Dörte
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Metabolites as new molecular traits and their role for genetic evaluation of traditional milk traits2012In: Book of Abstracts of the 63rd Annual Meeting of the European Federation of Animal Science: Bratislava, Slovakia, 27 - 31 August 2012, Wageningen: Wageningen Academic Publishers , 2012, p. 88-88Conference paper (Refereed)
  • 56.
    Melzer, Nina
    et al.
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany..
    Wittenburg, Dörte
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Simulating SNP data: influence of simulation design on the extent of linkage disequilibrium2010In: 11th Day of the Doctoral Student: abstracts; 19 May 2010, Dummerstorf / [ed] Seyfert, H.-M., Viereck, G., Dummerstorf, Germany: FBN , 2010, p. 19-22Conference paper (Refereed)
  • 57.
    Meyer, Rhonda C
    et al.
    Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
    Witucka-Wall, Hanna
    Department of Genetics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany.
    Becher, Martina
    Department of Genetics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany.
    Blacha, Anna
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Boudichevskaia, Anastassia
    Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
    Dörmann, Peter
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Fiehn, Oliver
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Friedel, Svetlana
    Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
    von Korff, Maria
    Department of Genetics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany.
    Lisec, Jan
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Melzer, Michael
    Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
    Schmidt, Renate
    Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
    Scholz, Matthias
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Selbig, Joachim
    Department of Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany.
    Willmitzer, Lothar
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany; King Abdulaziz University, P.O., Jeddah, Saudi Arabia.
    Altmann, Thomas
    Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany; Department of Genetics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany.
    Heterosis manifestation during early Arabidopsis seedling development is characterized by intermediate gene expression and enhanced metabolic activity in the hybrids2012In: The Plant Journal, ISSN 0960-7412, E-ISSN 1365-313X, Vol. 71, no 4, p. 669-83Article in journal (Refereed)
    Abstract [en]

    Heterosis-associated cellular and molecular processes were analyzed in seeds and seedlings of Arabidopsis thaliana accessions Col-0 and C24 and their heterotic hybrids. Microscopic examination revealed no advantages in terms of hybrid mature embryo organ sizes or cell numbers. Increased cotyledon sizes were detectable 4 days after sowing. Growth heterosis results from elevated cell sizes and numbers, and is well established at 10 days after sowing. The relative growth rates of hybrid seedlings were most enhanced between 3 and 4 days after sowing. Global metabolite profiling and targeted fatty acid analysis revealed maternal inheritance patterns for a large proportion of metabolites in the very early stages. During developmental progression, the distribution shifts to dominant, intermediate and heterotic patterns, with most changes occurring between 4 and 6 days after sowing. The highest incidence of heterotic patterns coincides with establishment of size differences at 4 days after sowing. In contrast, overall transcript patterns at 4, 6 and 10 days after sowing are characterized by intermediate to dominant patterns, with parental transcript levels showing the largest differences. Overall, the results suggest that, during early developmental stages, intermediate gene expression and higher metabolic activity in the hybrids compared to the parents lead to better resource efficiency, and therefore enhanced performance in the hybrids.

  • 58.
    Neumann, Gunter
    et al.
    School of Medical Health (MV), Örebro University, Örebro, Sweden.
    Wall, Rebecca
    Örebro University, School of Medical Sciences.
    Rangel, Ignacio
    Örebro University, School of Medical Sciences.
    Marques, Tatiana M.
    Örebro University, School of Medical Sciences.
    Repsilber, Dirk
    Örebro University, School of Medical Sciences.
    Qualitative modelling of the interplay of inflammatory status and butyrate in the human gut: a hypotheses about robust bi-stability2018In: BMC Systems Biology, ISSN 1752-0509, E-ISSN 1752-0509, Vol. 12, no 1, article id 144Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Gut microbiota interacts with the human gut in multiple ways. Microbiota composition is altered in inflamed gut conditions. Likewise, certain microbial fermentation products as well as the lipopolysaccharides of the outer membrane are examples of microbial products with opposing influences on gut epithelium inflammation status. This system of intricate interactions is known to play a core role in human gut inflammatory diseases. Here, we present and analyse a simplified model of bidirectional interaction between the microbiota and the host: in focus is butyrate as an example for a bacterial fermentation product with anti-inflammatory properties.

    RESULTS: We build a dynamical model based on an existing model of inflammatory regulation in gut epithelial cells. Our model introduces both butyrate as a bacterial product which counteracts inflammation, as well as bacterial LPS as a pro-inflammatory bacterial product. Moreover, we propose an extension of this model that also includes a feedback interaction towards bacterial composition. The analysis of these dynamical models shows robust bi-stability driven by butyrate concentrations in the gut. The extended model hints towards a further possible enforcement of the observed bi-stability via alteration of gut bacterial composition. A theoretical perspective on the stability of the described switch-like character is discussed.

    CONCLUSIONS: Interpreting the results of this qualitative model allows formulating hypotheses about the switch-like character of inflammatory regulation in the gut epithelium, involving bacterial products as constitutive parts of the system. We also speculate about possible explanations for observed bimodal distributions in bacterial compositions in the human gut. The switch-like behaviour of the system proved to be mostly independent of parameter choices. Further implications of the qualitative character of our modeling approach for the robustness of the proposed hypotheses are discussed, as well as the pronounced role of butyrate compared to other inflammatory regulators, especially LPS, NF- κB and cytokines.

  • 59.
    Rajan, Sukithar K.
    et al.
    Örebro University, School of Medical Sciences.
    Lindqvist, Carl Mårten
    Örebro University, School of Medical Sciences.
    Brummer, Robert Jan
    Örebro University, School of Medical Sciences.
    Schoultz, Ida
    Örebro University, School of Medical Sciences.
    Repsilber, Dirk
    Örebro University, School of Medical Sciences.
    Phylogenetic microbiota profiling in fecal samples depends on combination of sequencing depth and choice of NGS analysis method2019In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, no 9, article id e0222171Article in journal (Refereed)
    Abstract [en]

    The human gut microbiota is well established as an important factor in health and disease. Fecal sample microbiota are often analyzed as a proxy for gut microbiota, and characterized with respect to their composition profiles. Modern approaches employ whole genome shotgun next-generation sequencing as the basis for these analyses. Sequencing depth as well as choice of next-generation sequencing data analysis method constitute two main interacting methodological factors for such an approach. In this study, we used 200 million sequence read pairs from one fecal sample for comparing different taxonomy classification methods, using default and custom-made reference databases, at different sequencing depths. A mock community data set with known composition was used for validating the classification methods. Results suggest that sequencing beyond 60 million read pairs does not seem to improve classification. The phylogeny prediction pattern, when using the default databases and the consensus database, appeared to be similar for all three methods. Moreover, these methods predicted rather different species. We conclude that the choice of sequencing depth and classification method has important implications for taxonomy composition prediction. A multi-method-consensus approach for robust gut microbiota NGS analysis is recommended.

  • 60.
    Rangel, Ignacio
    et al.
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden.
    Sundin, Johanna
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden.
    Fuentes, S.
    Laboratory of Microbiology, Wageningen University, Wageningen, The Netherlands.
    Repsilber, Dirk
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden.
    de Vos, W. M.
    Laboratory of Microbiology, Wageningen University, Wageningen, The Netherlands; Departments of Bacteriology & Immunology and Veterinary Biosciences, University of Helsinki, Helsinki, Finland .
    Brummer, Robert Jan
    Örebro University, School of Medicine, Örebro University, Sweden.
    The relationship between faecal-associated and mucosal-associated microbiota in irritable bowel syndrome patients and healthy subjects2015In: Alimentary Pharmacology and Therapeutics, ISSN 0269-2813, E-ISSN 1365-2036, Vol. 42, no 10, p. 1211-1221Article in journal (Refereed)
    Abstract [en]

    Background: The faecal-associated microbiota is commonly seen as a surrogate of the mucosal-associated microbiota. However, previous studies indicate that they are different. Furthermore, analyses of the mucosal microbiota are commonly done after standard bowel cleansing, affecting the microbial composition.

    Aim: To compare the mucosal-associated microbiota, obtained from unprepared colon, with faecal-associated microbiota in healthy subjects and irritable bowel syndrome (IBS) patients.

    Methods: Faecal and mucosal biopsies were obtained from 33 IBS patients and 16 healthy controls. Of IBS patients, 49% belonged to the diarrhoea-predominant subgroup and 80% suffered from IBS symptoms during at least 5 years. Biopsies were collected from unprepared sigmoid colon and faecal samples a day before colonoscopy. Microbiota analyses were performed with a phylogenetic microarray and redundancy discriminant analysis.

    Results: The composition of the mucosal- and the faecal-associated microbiota in unprepared sigmoid colon differs significantly (P = 0.002). Clinical characteristics of IBS did not correlate with this difference. Bacteroidetes dominate the mucosal-associated microbiota. Firmicutes, Actinobacteria and Proteobacteria dominate the faecal-associated microbiota. Healthy subjects had a significantly higher (P < 0.005) abundance (1.9%) of the bacterial group uncultured Clostridiales I in the mucosal-associated microbiota than IBS patients (0.3%). Bacterial diversity was higher in faecal- compared with mucosal-associated microbiota in IBS patients (P < 0.005). No differences were found in healthy subjects.

    Conclusions: Differences in the mucosal-associated microbiota between healthy individuals and IBS patients are minimal (one bacterial group) compared to differences in the faecal microbiota of both groups (53 bacterial groups). Microbial aberrations characterising IBS are more pronounced in the faeces than in the mucosa.

  • 61.
    Rangel, Ignacio
    et al.
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden.
    Sundin, Johanna
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden.
    Fuentes, Susana
    Wageningen University, Wageningen, The Netherlands.
    Repsilber, Dirk
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden.
    de Vos, Willem M.
    Wageningen University, Wageningen, The Netherlands; University of Helsinki, Finland.
    Brummer, Robert J.
    Örebro University, School of Medicine, Örebro University, Sweden.
    Mucosal-associated microbiota differs less than fecal-associated microbiota between Irritable Bowel Syndrome patients and healthy subjectsManuscript (preprint) (Other academic)
  • 62.
    Redestig, Henning
    et al.
    Max Planck Institute for Molecular Plant Physiology, Golm, Germany.
    Repsilber, Dirk
    University of Potsdam, Potsdam, Germany.
    Sohler, Florian
    Institute for Informatics, Ludwig Maximilians University,Munich, Germany.
    Selbig, Joachim
    Max Planck Institute for Molecular Plant Physiology, Golm, Germany; University of Potsdam, Potsdam, Germany.
    Integrating functional knowledge during sample clustering for microarray data using unsupervised decision trees2007In: Biometrical Journal, ISSN 0323-3847, E-ISSN 1521-4036, Vol. 49, no 2, p. 214-29Article in journal (Refereed)
    Abstract [en]

    Clustering of microarray gene expression data is performed routinely, for genes as well as for samples. Clustering of genes can exhibit functional relationships between genes; clustering of samples on the other hand is important for finding e.g. disease subtypes, relevant patient groups for stratification or related treatments. Usually this is done by first filtering the genes for high-variance under the assumption that they carry most of the information needed for separating different sample groups. If this assumption is violated, important groupings in the data might be lost. Furthermore, classical clustering methods do not facilitate the biological interpretation of the results. Therefore, we propose to methodologically integrate the clustering algorithm with prior biological information. This is different from other approaches as knowledge about classes of genes can be directly used to ease the interpretation of the results and possibly boost clustering performance. Our approach computes dendrograms that resemble decision trees with gene classes used to split the data at each node which can help to find biologically meaningful differences between the sample groups. We have tested the proposed method both on simulated and real data and conclude its usefulness as a complementary method, especially when assumptions of few differentially expressed genes along with an informative mapping of genes to different classes are met.

  • 63.
    Repsilber, Dirk
    Institute of Genetics and Biometry, Bioinformatics and Biomathematics Unit, Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
    Biosignatures from blood: disentagling patterns and cell types in heterogeneous tissue2011Conference paper (Refereed)
  • 64.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology, FBN Dummerstorf, Germany.
    From spots to candidates: image analysis, data processing, candidate selection2008In: Hämostaseologie, 2008, Vol. 28, p. A5 WS-04-02-Conference paper (Refereed)
  • 65.
    Repsilber, Dirk
    et al.
    Institute for Forest Genetics and Forest Tree Breeding, Federal Research Centre of Forestry and Forest Products, Grosshansdorf, Germany; Institute for Molecular Evolution, Evolutionary Biology Centre, University of Uppsala, Uppsala, .
    Bialozyt, F. Ronald
    Institute for Forest Genetics and Forest Tree Breeding, Federal Research Centre of Forestry and Forest Products, Grosshansdorf, Germany.
    Spatial genetic patterns systematically accelerate and bias drift-based genetic erosion2002In: Ecological Modelling, ISSN 0304-3800, E-ISSN 1872-7026, Vol. 148, no 3, p. 251-261Article in journal (Refereed)
    Abstract [en]

    Genetic erosion of an ecosystem’s key species weakens the basis of ecosystem stability. In the absence of selection and migration genetic drift is the only factor influencing the degree of genetic erosion. Populations of sessile organisms represent a pattern of genetic information in space. In this paper, we show how spatial genetic patterns bias and accelerate the dynamics of drift-based genetic erosion. Using a Cellular Automaton (CA) as a modeling environment for discrete systems with local dynamics, we study the boundary conditions for such pattern-dependent genetic erosion. The system is designed as a one locus two allele model for haploid loci. Each cell in the CA represents one sessile individual of the simulated population. In order to analyze the behavior of the model we varied the following four variables, (1) the initial spatial distribution of haplotypes; (2) the magnitude of local gene-flow; (3) the noise in the initial pattern and; (4) the intensity of global (non-local) gene-flow. We show that for certain spatial genetic patterns genetic drift systematically leads to the fixation of one allele, if the size of the patterns and the dimension of local gene flow are of similar scale. Moreover, drift is substantially accelerated compared to the situation, where the two alleles are randomly distributed. These results are rather stable to noise in the initial pattern but external gene flow (EGF) has to be limited to a certain threshold to allow spatial patterns to drive genetic erosion.

  • 66.
    Repsilber, Dirk
    et al.
    Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany.
    Fink, Ludger
    Institut für Pathologie, Justus-Liebig-Universität Gießen, Gießen, Germany.
    Jacobsen, Marc
    Max-Planck-Institut für Infektionsbiologie, Berlin, Germany .
    Bläsing, Oliver
    Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam, Germany.
    Ziegler, Andreas
    Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany.
    Sample selection for microarray gene expression studies2005In: Methods of Information in Medicine, ISSN 0026-1270, Vol. 44, no 3, p. 461-7Article in journal (Refereed)
    Abstract [en]

    Objectives: The choice of biomedical samples for microarray gene expression studies is decisive for both validity and interpretability of results. We present a consistent, comprehensive framework to deal with the typical selection problems in microarray studies.

    Methods: Microarray studies are designed either as case-control studies or as comparisons of parallel groups from cohort studies, since high levels of random variation in the experimental approach thwart absolute measurements of gene expression levels. Validity and results of gene expression studies heavily rely on the appropriate choice of these study groups. Therefore, the so-called principles of comparability, which are well known from both clinical and epidemiological studies, need to be applied to microarray experiments.

    Results: The principles of comparability are the study-base principle, the principle of deconfounding and the principle of comparable accuracy in measurements. We explain each of these principles, show how they apply to microarray experiments, and illustrate them with examples. The examples are chosen as to represent typical stumbling blocks of microarray experimental design, and to exemplify the benefits of implementing the principles of comparability in the setting of microarray experiments.

    Conclusions: Microarray studies are closely related to classical study designs and therefore have to obey the same principles of comparability as these. Their validity should not be compromised by selection, confounding or information bias. The so-called study-base principle, calling for comparability and thorough definition of the compared cell populations, is the key principle for the choice of biomedical samples and controls in microarray studies.

  • 67.
    Repsilber, Dirk
    et al.
    Institute for Medical Biometry and Statistics (IMBS) University at Lübeck, Germany.
    Hennig, Christian
    Swiss Federal Institute of Technology, Zurich, Switzerland.
    Scholz, Florian
    Institute for Forest Genetics and Forest Tree Breeding, Federal Research Center for Forestry and Forest Products, GroBhansdorf, Germany.
    On sources of variation in expression of phosphoenolpyruvate-carboxylase in Norway spruce (Picea abies (L.) Karst.): PEPC genotype, genetic background and growth temperature2004In: Forest Genetics, Vol. 11, no 1, p. 73-81Article in journal (Refereed)
    Abstract [en]

    For gene-enzyme systems in forest trees it is unclear how much structural genetic diversity as compared to diversity of the genetic background contributes to phenotypic variability. The gene-enzyme-system of PEPC-A, phosphoenolpyruvate-carboxylase (PEPCase, EC 4.1.1.3 I), of Norway spruce (Picea abies (L.) Karst.) was chosen as an example to investigate the relative importance of the following sources of phenotypic variation in enzyme activity: (1) Variation at the structural PEPC-A-locus with three genotypes, (2) Variation in the genetic background, (3) Variation in growth temperature. The amount, specific activity and catalytic efficiency of PEPCase in crude needle extracts were assessed as quantitative traits. According to our ANOVA results, variation in the genetic background, i.e. epistasis in the general sense, is the most important source of variation compared to variation at the levels of both PEPC-A-genotype or growth temperature. Moreover, evaluation of the kinship partition of the genetic background revealed the individual level as most important. We compare to results of similar investigations for other species with different life histories and point to consequences for forest tree conservation genetics.

  • 68. Repsilber, Dirk
    et al.
    Jacobsen, Marc
    Biomarker discovery: Introduction to Statistical Learning and Integrative Bioinformatics Approaches2011In: Handbook of Systems Toxicology / [ed] Daniel A. Casciano & Saura C. Sahu, USA: John Wiley & Sons, 2011, p. 361-386Chapter in book (Other academic)
  • 69. Repsilber, Dirk
    et al.
    Kern, Sabine
    Selbig, Joachim
    Jacobsen, Marc
    Analysing and interpreting gene expression in heterogeneous tissues2008In: Statistics and Life Sciences: Perspectives and Challenges / [ed] Hothorn, L A; Mansmann, U; Tutz, G; Burger, U and Mejza, S, 2008, p. 126-Conference paper (Refereed)
  • 70.
    Repsilber, Dirk
    et al.
    Department of Genetics and Biometry, Research Institute for the Biology of Farm Animals, Dummerstorf, Germany.
    Kern, Sabine
    Bioinformatics Chair, Institute for Biochemistry and Biology at the University of Potsdam, Potsdam-Golm, Germany.
    Telaar, Anna
    Department of Genetics and Biometry, Research Institute for the Biology of Farm Animals, Dummerstorf, Germany .
    Walzl, Gerhard
    Molecular Biology and Human Genetics, University of Stellenbosch, Tygerberg, Cape Town, South Africa .
    Black, Gillian F
    Molecular Biology and Human Genetics, University of Stellenbosch, Tygerberg, Cape Town, South Africa .
    Selbig, Joachim
    Bioinformatics Chair, Institute for Biochemistry and Biology at the University of Potsdam, Potsdam-Golm, Germany .
    Parida, Shreemanta K
    Department of Immunology, Max-Planck-Institute for Infection Biology, Berlin, Germany.
    Kaufmann, Stefan H E
    Department of Immunology, Max-Planck-Institute for Infection Biology, Berlin, Germany.
    Jacobsen, Marc
    Department of Immunology, Max-Planck-Institute for Infection Biology, Berlin, Germany; Department of Immunology, Bernhard-Nocht-Institute for Tropical Medicine, Hamburg, Germany.
    Biomarker discovery in heterogeneous tissue samples: taking the in-silico deconfounding approach2010In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 11, article id 27Article in journal (Refereed)
    Abstract [en]

    Background: For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues.

    Results: Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach.Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available.

    Conclusions: The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.

  • 71.
    Repsilber, Dirk
    et al.
    Institute of Medical Biometry and Statistics, Lübeck, Germany.
    Kim, Jan T.
    Institute for Neuro- and Bioinformatics, Lübeck, Germany.
    Developing and Testing methods for microarray data analysis Using an Artificial life framework2003In: Advances in artificial life: 7th European Conference, ECAL 2003 Dortmund, Germany, September 14-17, 2003 Proceedings / [ed] Banzhaf, W, Christaller, T, Dittrich, P, Kim, JT, Ziegler, J, Berlin, Germany, 2003, Vol. 2801, p. 686-695Conference paper (Refereed)
    Abstract [en]

    Microarray technology has resulted in large sets of gene expression data. Using these data to derive knowledge about the underlying mechanisms that control gene expression dynamics has become an important challenge. Adequate models of the fundamental principles of gene regulation, such as Artificial Life models of regulatory networks, are pivotal for progress in this area. In this contribution, we present a framework for simulating microarray gene expression experiments. Within this framework, artificial regulatory networks with a simple regulon structure are generated. Simulated expression profiles are obtained from these networks under a series of different environmental conditions. The expression profiles show a complex diversity. Consequently, success in using hierarchical clustering to detect groups of genes which form a regulon proves to depend strongly on the method which is used to quantify similarity between expression profiles. When measurements are noisy, even clusters of identically regulated genes are surprisingly difficult to detect. Finally, we suggest cluster support, a method based on overlaying multiple clustering trees, to find out which clusters in a tree are biologically significant.

  • 72.
    Repsilber, Dirk
    et al.
    Institute for Biometry and Informatics, SLU, Uppsala.
    Kim, Jan T.
    Institut für Neuro- und Bioinformatik, Lübeck, Germany.
    Liljenström, Hans
    Institute for Biometry and Informatics, SLU, Uppsala.
    Martinetz, Thomas
    Institut für Neuro- und Bioinformatik, Lübeck, Germany.
    Using coarse-grained, discrete systems for data-driven inference of regulatory gene networks: Perspectives and limitations for reverse engineering2002In: Proceedings of the Fifth German Workshop on Artificial Life (GWAL2002) / [ed] Polani, D., Kim, J. T., Martinetz, T., 2002, p. 67-76Conference paper (Refereed)
  • 73.
    Repsilber, Dirk
    et al.
    Institute for Molecular Evolution, Evolutionary Biology Centre of the University of Uppsala, Uppsala, Department of Biometry and Informatics, SLU, Uppsala.
    Liljenström, Hans
    Department of Biometry and Informatics, SLU, Uppsala.
    Andersson, Siv G E
    Institute for Molecular Evolution, Evolutionary Biology Centre of the University of Uppsala, Uppsala; Linnaeus Centre for Bioinformatics, Uppsala University and SLU, Uppsala, Sweden.
    Reverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypotheses.2002In: Biosystems (Amsterdam. Print), ISSN 0303-2647, E-ISSN 1872-8324, Vol. 66, no 1-2, p. 31-41Article in journal (Refereed)
    Abstract [en]

    Reverse engineering algorithms (REAs) aim at using gene expression data to reconstruct interactions in regulatory genetic networks. This may help to understand the basis of gene regulation, the core task of functional genomics. Collecting data for a number of environmental conditions is necessary to reengineer even the smallest regulatory networks with reasonable confidence. We systematically tested the requirements for the experimental design necessary for ranking alternative hypotheses about the structure of a given regulatory network. A genetic algorithm (GA) was used to explore the parameter space of a multistage discrete genetic network model with fixed connectivity and number of states per node. Our results show that it is not necessary to determine all parameters of the genetic network in order to rank hypotheses. The ranking process is easier the more experimental environmental conditions are used for the data set. During the ranking, the number of fixed parameters increases with the number of environmental conditions, while some errors in the hypothetical network structure may pass undetected, due to a maintained dynamical behaviour.

  • 74.
    Repsilber, Dirk
    et al.
    Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany.
    Mansmann, Ulrich
    Institut für Medizinische Biometrie und Informatik, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany .
    Brunner, Edgar
    Abteilung Medizinische Statistik, Georg-August-Universität Göttingen, Göttingen, Germany .
    Ziegler, Andreas
    Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany.
    Tutorial on microarray gene expression experiments: An introduction2005In: Methods of Information in Medicine, ISSN 0026-1270, Vol. 44, no 3, p. 392-9Article in journal (Refereed)
    Abstract [en]

    Objectives: With the collection of articles presented in this special issue, we aim at educating interested statisticians and biometricians on the one hand as well as biologists and medical researchers on the other with respect to basic necessities in planning, conducting and analyzing microarray gene expression experiments. The reader should get comprehensive directions to understand both the overall structure of this approach as well as the decisive details, which enable--or thwart--a meaningful data analysis.

    Methods: For a one-day workshop with tutorial character we brought together experts in design, conduct and analysis of microarray gene expression experiments who prepared a series of comprehensive lessons. These contributions were then reworked into a series of introductory articles and bundled in form and content as a Special Topic.

    Results: It was possible to present a tutorial overview of the field. The interested reader was able to learn the basic necessities and was referred to further references for details on the possible alternatives. A recipe style all-embracing plan, covering all eventualities and possibilities was not only beyond the scope of an introductory tutorial-like presentation, but was also not yet agreed upon by the scientific society.

    Conclusions: It proved feasible to find a framework for integrating the interdisciplinary approaches to the challenging field of gene expression analysis with microarrays, hopefully contributing to a rapid and comprehensive introduction for novices.

  • 75.
    Repsilber, Dirk
    et al.
    Department of Genetics and Biometry, Research Institute for the Biology of Farm Animals (FBN), Dummerstorf, Germany.
    Martinetz, Thomas
    Institute for Neuro- and Bioinformatics, University of Lübeck, Lüubeck, Germany.
    Björklund, Mats
    Department of Animal Ecology, Evolutionary Biology Centre, University of Uppsala, Uppsala.
    Adaptive dynamics of regulatory networks: size matters2009In: EURASIP Journal on Bioinformatics and Systems Biology, ISSN 1687-4145, E-ISSN 1687-4153, article id 618502Article in journal (Refereed)
    Abstract [en]

    To accomplish adaptability, all living organisms are constructed of regulatory networks on different levels which are capable to differentially respond to a variety of environmental inputs. Structure of regulatory networks determines their phenotypical plasticity, that is, the degree of detail and appropriateness of regulatory replies to environmental or developmental challenges. This regulatory network structure is encoded within the genotype. Our conceptual simulation study investigates how network structure constrains the evolution of networks and their adaptive abilities. The focus is on the structural parameter network size. We show that small regulatory networks adapt fast, but not as good as larger networks in the longer perspective. Selection leads to an optimal network size dependent on heterogeneity of the environment and time pressure of adaptation. Optimal mutation rates are higher for smaller networks. We put special emphasis on discussing our simulation results on the background of functional observations from experimental and evolutionary biology.

  • 76.
    Repsilber, Dirk
    et al.
    Universitätsklinikum Schleswig-Holstein, Lübeck, Institut für Medizinische Biometrie und Statistik, Lübeck, Germany.
    Mira, A
    Lindroos, H
    Ziegler, A
    Andersson, Siv
    Reducing false positive rates by rotating microarray-based genomotyping data2004In: Biometrical Journal, ISSN 0323-3847, E-ISSN 1521-4036, Vol. 46, p. 57-Article in journal (Refereed)
    Abstract [en]

    Microarray-based comparative genomic hybridization is leading to an increased understanding of bacterial evolution and patho-genesis by efficiently comparing whole genomes. Here, a sample genome is compared to a reference genome via comparative hybridization using two different fluorescent dyes. The logratio of the fluorescence intensities for sample and reference genome for each gene on the array is then usually compared to a cutoff classifying the belonging gene as absent or present with respect to the sample genome. The resulting list of candidate absent genes then undergoes confirmational analyses, i.e. PCR or sequencing, which are decisive with respect to both time and costs of the experiment. Thus, there is vital interest to reduce the rate of false positives in the list of candidate absent genes from the comparative genomic hybridization step. Our approach to accomplish this task uses the expected relationship between the logratio and the mean log intensities for absent genes to linear transform the inten-sity data before comparing to a cutoff. For validated data from a series of comparative genomic hybridizations for two Bartonella species we assessed significance and efficacy of the new approach. We show that we are able to halve the rate of false positives in the list of candidate absent genes for a typical comparative genomic hybridization experiment, thus significantly reducing time and costs of necessary confirmational analyses.

  • 77.
    Repsilber, Dirk
    et al.
    Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany .
    Mira, Alex
    DivisiÕn de MicrobiologÌa, Universidad Miguel Hernandez, Alicante, Spain.
    Lindroos, Hillevi
    Department of Molecular Evolution, Evolutionary Biology Center, Uppsala University, Uppsala, Sweden .
    Andersson, Siv
    Department of Molecular Evolution, Evolutionary Biology Center, Uppsala University, Uppsala, Sweden .
    Ziegler, Andreas
    Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany .
    Data rotation improves genomotyping efficiency2005In: Biometrical Journal, ISSN 0323-3847, E-ISSN 1521-4036, Vol. 47, no 4, p. 585-98Article in journal (Refereed)
    Abstract [en]

    Unsequenced bacterial strains can be characterized by comparing their genomic DNA to a sequenced reference genome of the same species. This comparative genomic approach, also called genomotyping, is leading to an increased understanding of bacterial evolution and pathogenesis. It is efficiently accomplished by comparative genomic hybridization on custom-designed cDNA microarrays. The microarray experiment results in fluorescence intensities for reference and sample genome for each gene. The log-ratio of these intensities is usually compared to a cut-off, classifying each gene of the sample genome as a candidate for an absent or present gene with respect to the reference genome. Reducing the usually high rate of false positives in the list of candidates for absent genes is decisive for both time and costs of the experiment. We propose a novel method to improve efficiency of genomotyping experiments in this sense, by rotating the normalized intensity data before setting up the list of candidate genes. We analyze simulated genomotyping data and also re-analyze an experimental data set for comparison and illustration. We approximately halve the proportion of false positives in the list of candidate absent genes for the example comparative genomic hybridization experiment as well as for the simulation experiments.

  • 78. Repsilber, Dirk
    et al.
    Selbig, Joachim
    Ziegler, Andreas
    Ensuring comparability for valid design and analysis of microarray gene expression experiments2006In: Clinical Chemistry and Laboratory Medicine, ISSN 1434-6621, E-ISSN 1437-4331, Vol. 44, no 6, p. A101-A102Article in journal (Refereed)
  • 79.
    Repsilber, Dirk
    et al.
    Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Germany.
    Wiese, S
    Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Germany.
    Rachen, M
    Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Germany.
    Schröder, A W
    Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Germany.
    Riesner, D
    Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Germany.
    Steger, G
    Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Germany.
    Formation of metastable RNA structures by sequential folding during transcription: time-resolved structural analysis of potato spindle tuber viroid (-)-stranded RNA by temperature-gradient gel electrophoresis1999In: RNA: A publication of the RNA Society, ISSN 1355-8382, E-ISSN 1469-9001, Vol. 5, no 4, p. 574-84Article in journal (Refereed)
    Abstract [en]

    A model of functional elements critical for replication and infectivity of the potato spindle tuber viroid (PSTVd) was proposed earlier: a thermodynamically metastable structure containing a specific hairpin (HP II) in the (-)-strand replication intermediate is essential for template activity during (+)-strand synthesis. We present here a detailed kinetic analysis on how PSTVd (-)-strands fold during synthesis by sequential folding into a variety of metastable structures that rearrange only slowly into the structure distribution of the thermodynamic equilibrium. Synthesis of PSTVd (-)-strands was performed by T7-RNA-polymerase; the rate of synthesis was varied by altering the concentration of nucleoside triphosphates to mimic the in vivo synthesis rate of DNA-dependent RNA polymerase II. With dependence on rate and duration of the synthesis, the structure distributions were analyzed by temperature-gradient gel electrophoresis (TGGE). Metastable structures are generated preferentially at low transcription rates--similar to in vivo rates--or at short transcription times at higher rates. Higher transcription rates or longer transcription times lead to metastable structures in low or undetectable amounts. Instead different structures do gradually appear having a more rod-like shape and higher thermodynamic stability, and the thermodynamically optimal rod-like structure dominates finally. It is concluded that viroids are able to use metastable as well as stable structures for their biological functions.

  • 80.
    Repsilber, Dirk
    et al.
    Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany.
    Ziegler, Andreas
    Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany .
    Two-color microarray experiments. Technology and sources of variance.2005In: Methods of Information in Medicine, ISSN 0026-1270, Vol. 44, no 3, p. 400-4Article in journal (Refereed)
    Abstract [en]

    Objectives: Microarray gene expression experiments have a complex technical background. Knowledge about certain technical details is inevitable to judge alternatives for both experimental design and analysis. Here, we introduce the necessary details for the so-called two-color microarray experiments and review major sources of technical variance.

    Methods: We follow the sequence of experimental steps during a typical two-color microarray gene expression experiment, stressing decisive points in the choice of technique, experimental handling and biophysical basics. We point out where technical variation is to be expected.

    Results: Tissue storage, RNA extraction techniques, as well as the microarray hybridization represent major components of technical variance to be considered. Depending on the possibilities for access to the biomedical material under investigation, choice of amplification and labeling techniques can also be decisive to avoid additional technical variance. The two-color microarray experimental approach seeks to avoid a group of probe-level technical biases making use of the advantages of an incomplete block-design.

    Conclusions: It is worth to know the major sources of technical variance during the typical experimental sequence, both for choice of experimental design and techniques of molecular biology, as well as for the understanding of quality control and normalization approaches. Here, early investments pay at the level of reduced technical variance, allowing for enhanced detection levels for the effects under investigation.

  • 81.
    Roehle, Anja
    et al.
    Institute for Pathology, University Clinic Schleswig-Holstein, Campus Luebeck, Luebeck, Germany.
    Hoefig, Kai P
    Clinic Schleswig-Holstein, Campus Luebeck, Luebeck, Germany.
    Repsilber, Dirk
    Research Institute for the Biology of Farm Animals FBN, Dummerstorf, Germany.
    Thorns, Christoph
    Clinic Schleswig-Holstein, Campus Luebeck, Luebeck, Germany.
    Ziepert, Marita
    Institute for Medical Informatics, Statistics and Epidemiology, IMISE, University Leipzig, Leipzig, Germany.
    Wesche, Kai O
    Institute for Pathology, University Clinic Schleswig-Holstein, Campus Luebeck, Luebeck, Germany.
    Thiere, Marlen
    Institute for Pathology, University Clinic Schleswig-Holstein, Campus Luebeck, Luebeck, Germany.
    Loeffler, Markus
    Institute for Medical Informatics, Statistics and Epidemiology, IMISE, University Leipzig, Leipzig, Germany.
    Klapper, Wolfram
    Institute for Pathology, University Clinic Schleswig-Holstein, Campus Kiel, Kiel, Germany.
    Pfreundschuh, Michael
    Department of Internal Medicine I, University Clinic Saarland, Homburg/Saar, Germany.
    Matolcsy, András
    1st Department of Pathology and Experimental Cancer Research, Faculty of Medicine, Semmelweis University, Budapest, Hungary.
    Bernd, Heinz-Wolfram
    Institute for Pathology, University Clinic Schleswig-Holstein, Campus Luebeck, Luebeck, Germany.
    Reiniger, Lila
    Institute for Pathology, University Clinic Schleswig-Holstein, Campus Luebeck, Luebeck, Germany.
    Merz, Hartmut
    Institute for Pathology, University Clinic Schleswig-Holstein, Campus Luebeck, Luebeck, Germany.
    Feller, Alfred C
    MicroRNA signatures characterize diffuse large B-cell lymphomas and follicular lymphomas2008In: British Journal of Haematology, ISSN 0007-1048, E-ISSN 1365-2141, Vol. 142, no 5, p. 732-44Article in journal (Refereed)
    Abstract [en]

    MicroRNAs (miRNA, miR) are negative regulators of gene expression that play an important role in diverse biological processes such as development, cell growth, apoptosis and haematopoiesis, suggesting their association with cancer. Here we analysed the expression signatures of 157 miRNAs in 58 diffuse large B-cell lymphoma (DLBCL), 46 follicular lymphoma (FL) and seven non-neoplastic lymph nodes (LN). Comparison of the possible combinations of DLBCL-, FL- and LN resulted in specific DLBCL- and FL-signatures, which include miRNAs with previously published function in haematopoiesis (MIRN150 and MIRN155) or tumour development (MIRN210, MIRN10A, MIRN17-5P and MIRN145). As compared to LN, some miRNAs are differentially regulated in both lymphoma types (MIRN155, MIRN210, MIRN106A, MIRN149 and MIRN139). Conversely, some miRNAs show lymphoma-specific aberrant expression, such as MIRN9/9*, MIRN301, MIRN338 and MIRN213 in FL and MIRN150, MIRN17-5P, MIRN145, MIRN328 and others in DLBCL. A classification tree was computed using four miRNAs (MIRN330, MIRN17-5P, MIRN106a and MIRN210) to correctly identify 98% of all 111 cases that were analysed in this study. Finally, eight miRNAs were found to correlate with event-free and overall survival in DLBCL including known tumour suppressors (MIRN21, MIRN127 and MIRN34a) and oncogenes (MIRN195 and MIRNLET7G).

  • 82.
    Rush, Stephen
    et al.
    Örebro University, School of Medical Sciences.
    Repsilber, Dirk
    Örebro University, School of Medical Sciences.
    Capturing context-specific regulation in molecular interaction networks2018In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 19, no 1, article id 539Article in journal (Refereed)
    Abstract [en]

    Background: Molecular profiles change in response to perturbations. These changes are coordinated into functional modules via regulatory interactions. The genes and their products within a functional module are expected to be differentially expressed in a manner coherent with their regulatory network. This perspective presents a promising approach to increase precision in detecting differential signals as well as for describing differential regulatory signals within the framework of a priori knowledge about the underlying network, and so from a mechanistic point of view.

    Results: We present Coherent Network Expression (CoNE), an effective procedure for identifying differentially activated functional modules in molecular interaction networks. Differential gene expression is chosen as example, and differential signals coherent with the regulatory nature of the network are identified. We apply our procedure to systematically simulated data, comparing its performance to alternative methods. We then take the example case of a transcription regulatory network in the context of particle-induced pulmonary inflammation, recapitulating and proposing additional candidates to previously obtained results. CoNE is conveniently implemented in an R-package along with simulation utilities.

    Conclusion: Combining coherent interactions with error control on differential gene expression results in uniformly greater specificity in inference than error control alone, ensuring that captured functional modules constitute real findings.

  • 83.
    Schauer, Nicolas
    et al.
    Max-Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany.
    Semel, Yaniv
    Institute of Plant Sciences and Genetics and Otto Warburg Centre for Biotechnology, Faculty of Agriculture, Hebrew University of Jerusalem, Rehovot, Israel.
    Balbo, Ilse
    Max-Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany.
    Steinfath, Matthias
    Max-Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany; University of Potsdam, Institute for Biochemistry and Biology, Department of Bioinformatics, Potsdam, Germany.
    Repsilber, Dirk
    Research Institute of the Biology of Farm Animals, Dummerstorf, Germany.
    Selbig, Joachim
    Max-Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany; University of Potsdam, Institute for Biochemistry and Biology, Department of Bioinformatics, Potsdam, Germany.
    Pleban, Tzili
    Institute of Plant Sciences and Genetics and Otto Warburg Centre for Biotechnology, Faculty of Agriculture, Hebrew University of Jerusalem, Rehovot, Israel.
    Zamir, Dani
    Institute of Plant Sciences and Genetics and Otto Warburg Centre for Biotechnology, Faculty of Agriculture, Hebrew University of Jerusalem, Rehovot, Israel.
    Fernie, Alisdair R
    Max-Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany.
    Mode of inheritance of primary metabolic traits in tomato2008In: The Plant Cell, ISSN 1040-4651, E-ISSN 1532-298X, Vol. 20, no 3, p. 509-23Article in journal (Refereed)
    Abstract [en]

    To evaluate components of fruit metabolic composition, we have previously metabolically phenotyped tomato (Solanum lycopersicum) introgression lines containing segmental substitutions of wild species chromosome in the genetic background of a cultivated variety. Here, we studied the hereditability of the fruit metabolome by analyzing an additional year's harvest and evaluating the metabolite profiles of lines heterozygous for the introgression (ILHs), allowing the evaluation of putative quantitative trait locus (QTL) mode of inheritance. These studies revealed that most of the metabolic QTL (174 of 332) were dominantly inherited, with relatively high proportions of additively (61 of 332) or recessively (80 of 332) inherited QTL and a negligible number displaying the characteristics of overdominant inheritance. Comparison of the mode of inheritance of QTL revealed that several metabolite pairs displayed a similar mode of inheritance of QTL at the same chromosomal loci. Evaluation of the association between morphological and metabolic traits in the ILHs revealed that this correlation was far less prominent, due to a reduced variance in the harvest index within this population. These data are discussed in the context of genomics-assisted breeding for crop improvement, with particular focus on the exploitation of wide biodiversity.

  • 84.
    Scheubert, Lena
    et al.
    Institute of Computer Science, University of Osnabrück, Osnabrück, Germany; Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostock, Germany.
    Luštrek, Mitja
    Department of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia.
    Schmidt, Rainer
    Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostock, Germany.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology (FBN Dummersdorf), Dummerstorf, Germany.
    Fuellen, Georg
    Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostock, Germany; German Center for Neroudegenerative Disorders (DZNE), Rostock, Germany.
    Tissue-based Alzheimer gene expression markers-comparison of multiple machine learning approaches and investigation of redundancy in small biomarker sets2012In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 13, no 1, article id 266Article in journal (Refereed)
    Abstract [en]

    Background: Alzheimer's disease has been known for more than 100 years and the underlying molecular mechanisms are not yet completely understood. The identification of genes involved in the processes in Alzheimer affected brain is an important step towards such an understanding. Genes differentially expressed in diseased and healthy brains are promising candidates.

    Results: Based on microarray data we identify potential biomarkers as well as biomarker combinations using three feature selection methods: information gain, mean decrease accuracy of random forest and a wrapper of genetic algorithm and support vector machine (GA/SVM). Information gain and random forest are two commonly used methods. We compare their output to the results obtained from GA/SVM. GA/SVM is rarely used for the analysis of microarray data, but it is able to identify genes capable of classifying tissues into different classes at least as well as the two reference methods.

    Conclusion: Compared to the other methods, GA/SVM has the advantage of finding small, less redundant sets of genes that, in combination, show superior classification characteristics. The biological significance of the genes and gene pairs is discussed.

  • 85.
    Scheubert, Lena
    et al.
    University of Osnabrück, Osnabrück, Germany.
    Schmidt, R
    University of Rostock, Rostock, Germany.
    Lustrek, M
    University of Rostock, Rostock, Germany.
    Repsilber, Dirk
    Leibniz Institute, Dummerstorf, Germany.
    Fuellen, Georg
    University of Rostock, Rostock, Germany.
    Searching for Biomarkers of Pluripotent Stem Cells2011In: Abstractand 56. GMDS Jahrestagung / [ed] Blettner M; Klug S, Mainz, Germany: Kirchheim & Co , 2011, p. 55-56Conference paper (Refereed)
  • 86.
    Scheubert, Lena
    et al.
    Institute of Computer Science, University of Osnabrück, Osnabrück, Germany.
    Schmidt, Rainer
    Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostoch, Germany.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology (FBN Dummerstorf ), Dummerstorf, Germany.
    Lustrek, Mitja
    Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostoch, Germany; Department of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia.
    Fuellen, Georg
    Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostoch, Germany.
    Learning biomarkers of pluripotent stem cells in mouse2011In: DNA research, ISSN 1340-2838, E-ISSN 1756-1663, Vol. 18, no 4, p. 233-51Article in journal (Refereed)
    Abstract [en]

    Pluripotent stem cells are able to self-renew, and to differentiate into all adult cell types. Many studies report data describing these cells, and characterize them in molecular terms. Machine learning yields classifiers that can accurately identify pluripotent stem cells, but there is a lack of studies yielding minimal sets of best biomarkers (genes/features). We assembled gene expression data of pluripotent stem cells and non-pluripotent cells from the mouse. After normalization and filtering, we applied machine learning, classifying samples into pluripotent and non-pluripotent with high cross-validated accuracy. Furthermore, to identify minimal sets of best biomarkers, we used three methods: information gain, random forests and a wrapper of genetic algorithm and support vector machine (GA/SVM). We demonstrate that the GA/SVM biomarkers work best in combination with each other; pathway and enrichment analyses show that they cover the widest variety of processes implicated in pluripotency. The GA/SVM wrapper yields best biomarkers, no matter which classification method is used. The consensus best biomarker based on the three methods is Tet1, implicated in pluripotency just recently. The best biomarker based on the GA/SVM wrapper approach alone is Fam134b, possibly a missing link between pluripotency and some standard surface markers of unknown function processed by the Golgi apparatus.

  • 87.
    Schmidt, Rainer
    et al.
    Institute for Biostatistics and Informatics in Medicine and Aging Research, University of Rostock, Rostock, Germany.
    Scheubert, Lena
    Institute for Computer Science, University of Onsnabrueck, Onsnabrueck, Germany.
    Lustrek, Mitja
    Institute for Biostatistics and Informatics in Medicine and Aging Research, University of Rostock, Rostock, Germany; Department of Intelligent Science, University of Ljubljana, Ljubljana, Slovenia.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Fuellen, Georg
    Institute for Biostatistics and Informatics in Medicine and Aging Research, University of Rostock, Rostock, Germany.
    Pluripotent stem cells in mice2012In: Quality of Life through Quality of Information / [ed] J. Mandas et al, Amsterdam, Netherlands: IOS Press, 2012, Vol. 180, p. 1159-61Conference paper (Refereed)
    Abstract [en]

    Pluripotent stem cells are able to self-renew and to differentiate into all adult cell types. Many studies report data describing these cells and characterize them in molecular terms. Gene expression data of pluripotent and non-pluripotent cells from mouse were assembled. Machine learning was applied to classify samples into pluripotent and non-pluripotent cells. To identify minimal sets of best biomarkers, three methods were used: information gain, random forests, and genetic algorithm.

  • 88.
    Schuck, Sebastian D
    et al.
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany.
    Mueller, Henrik
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany.
    Kunitz, Frank
    Respiratory Diseases Clinic Heckeshorn, Department of Pneumology, HELIOS Klinikum Emil von Behring, Berlin, Germany.
    Neher, Albert
    Asklepios Professional Clinic München-Gauting, Centre for Pneumology and Thorax Surgery, Munich, Germany.
    Hoffmann, Harald
    Asklepios Professional Clinic München-Gauting, Centre for Pneumology and Thorax Surgery, Munich, Germany.
    Franken, Kees L C M
    Department of Immunohematology & Blood Transfusion/Department of Infectious Diseases, Leiden University Medical Center, Leiden, The Netherlands.
    Repsilber, Dirk
    Research Institute for the Biology of Farm Animals, Genetics and Biometry, Dummerstorf, Germany.
    Ottenhoff, Tom H M
    Department of Immunohematology & Blood Transfusion/Department of Infectious Diseases, Leiden University Medical Center, Leiden, The Netherlands.
    Kaufmann, Stefan H E
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany.
    Jacobsen, Marc
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany.
    Identification of T-cell antigens specific for latent mycobacterium tuberculosis infection2009In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 4, no 5, article id e5590Article in journal (Refereed)
    Abstract [en]

    Background: T-cell responses against dormancy-, resuscitation-, and reactivation-associated antigens of Mycobacterium tuberculosis are candidate biomarkers of latent infection in humans.

    Methodology/Principal findings: We established an assay based on two rounds of in vitro restimulation and intracellular cytokine analysis that detects T-cell responses to antigens expressed during latent M. tuberculosis infection. Comparison between active pulmonary tuberculosis (TB) patients and healthy latently M. tuberculosis-infected donors (LTBI) revealed significantly higher T-cell responses against 7 of 35 tested M. tuberculosis latency-associated antigens in LTBI. Notably, T cells specific for Rv3407 were exclusively detected in LTBI but not in TB patients. The T-cell IFNgamma response against Rv3407 in individual donors was the most influential factor in discrimination analysis that classified TB patients and LTBI with 83% accuracy using cross-validation. Rv3407 peptide pool stimulations revealed distinct candidate epitopes in four LTBI.

    Conclusions: Our findings further support the hypothesis that the latency-associated antigens can be exploited as biomarkers for LTBI.

  • 89.
    Schumacher, J
    et al.
    Institut für Humangenetik, Universität Bonn, Germany.
    König, I R
    Institut für Medizinische Biometrie und Statistik, Universitätsklinikum Schleswig-Holstein, Universität Zu Lübeck, Lübeck, Germany .
    Plume, E
    Klinik und Poliklinik für Kinder- und Jugendpsychiatrie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
    Propping, P
    Institut für Humangenetik, Universität Bonn, Germany.
    Warnke, A
    Klinik und Poliklinik für Kinder- und Jugendpsychiatrie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
    Manthey, M
    Institut für Humangenetik, Universität Bonn, Germany.
    Duell, M
    Institut für Humangenetik, Universität Bonn, Germany.
    Kleensang, A
    Institut für Medizinische Biometrie und Statistik, Universitätsklinikum Schleswig-Holstein, Universität Zu Lübeck, Lübeck, Germany .
    Repsilber, Dirk
    Institut für Medizinische Biometrie und Statistik, Universitätsklinikum Schleswig-Holstein, Universität Zu Lübeck, Lübeck, Germany .
    Preis, M
    Klinik für Psychiatrie und Psychotherapie des Kindes- und Jugendalters, Philipps-Universität Marburg, Marburg, Germany .
    Remschmidt, H
    Klinik für Psychiatrie und Psychotherapie des Kindes- und Jugendalters, Philipps-Universität Marburg, Marburg, Germany .
    Ziegler, Andreas
    Institut für Medizinische Biometrie und Statistik, Universitätsklinikum Schleswig-Holstein, Universität Zu Lübeck, Lübeck, Germany .
    Nöthen, M M
    Department of Genomics, Life and Brain Center, Universität Bonn, Germany .
    Schulte-Körne, G
    Klinik für Psychiatrie und Psychotherapie des Kindes- und Jugendalters, Philipps-Universität Marburg, Marburg, Germany; Department of Child and Adolescent Psychiatry, Philipps University Marburg, Marburg, Germany .
    Linkage analyses of chromosomal region 18p11-q12 in dyslexia2006In: Journal of neural transmission, ISSN 0300-9564, E-ISSN 1435-1463, Vol. 113, no 3, p. 417-23Article in journal (Refereed)
    Abstract [en]

    Dyslexia is characterized as a significant impairment in reading and spelling ability that cannot be explained by low intelligence, low school attendance or deficits in sensory acuity. It is known to be a hereditary disorder that affects about 5% of school aged children, making it the most common of childhood learning disorders. Several susceptibility loci have been reported on chromosomes 1, 2, 3, 6, 15, and 18. The locus on chromosome 18 has been described as having the strongest influence on single word reading, phoneme awareness, and orthographic coding in the largest genome wide linkage study published to date (Fisher et al., 2002). Here we present data from 82 German families in order to investigate linkage of various dyslexia-related traits to the previously described region on chromosome 18p11-q12. Using two- and multipoint analyses, we did not find support for linkage of spelling, single word reading, phoneme awareness, orthographic coding and rapid naming to any of the 14 genotyped STR markers. Possible explanations for our non-replication include differences in study design, limited power of our study and overestimation of the effect of the chromosome 18 locus in the original study.

  • 90.
    Selbig, Joachim
    et al.
    University of Potsdam, Department of Biochemistry and Biology, Bioinformatics Chair, Potsdam, Germany.
    Steinfath, Matthias
    Genetics and Biometry unit, Research Institute for the Biology of Farm Animals (FBN), Dummerstorf, Germany.
    Repsilber, Dirk
    2Genetics and Biometry unit, Research Institute for the Biology of Farm Animals(FBN), Dummerstorf, Germany.
    Network structure and biological function: reconstruction, modeling, and statistical approaches2009In: EURASIP Journal on Bioinformatics and Systems Biology, ISSN 1687-4145, E-ISSN 1687-4153, article id 714985Article in journal (Refereed)
  • 91.
    Singh, Nitesh Kumar
    et al.
    Institute for Biostatistics and Informatics in Medicine and Ageing Research, Department of Medicine, University of Rostock, Rostock, Germany.
    Repsilber, Dirk
    Institute for Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Liebscher, Volkmar
    Institute for Mathematics and Informatics, Ernst Moritz Arndt University of Greifswald, Greifswald, Germany.
    Taher, Leila
    Institute for Biostatistics and Informatics in Medicine and Ageing Research, Department of Medicine, University of Rostock, Rostock, Germany.
    Fuellen, Georg
    Institute for Biostatistics and Informatics in Medicine and Ageing Research, Department of Medicine, University of Rostock, Rostock, Germany.
    Identifying genes relevant to specific biological conditions in time course microarray experiments2013In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, no 10, article id e76561Article in journal (Refereed)
    Abstract [en]

    Microarrays have been useful in understanding various biological processes by allowing the simultaneous study of the expression of thousands of genes. However, the analysis of microarray data is a challenging task. One of the key problems in microarray analysis is the classification of unknown expression profiles. Specifically, the often large number of non-informative genes on the microarray adversely affects the performance and efficiency of classification algorithms. Furthermore, the skewed ratio of sample to variable poses a risk of overfitting. Thus, in this context, feature selection methods become crucial to select relevant genes and, hence, improve classification accuracy. In this study, we investigated feature selection methods based on gene expression profiles and protein interactions. We found that in our setup, the addition of protein interaction information did not contribute to any significant improvement of the classification results. Furthermore, we developed a novel feature selection method that relies exclusively on observed gene expression changes in microarray experiments, which we call "relative Signal-to-Noise ratio" (rSNR). More precisely, the rSNR ranks genes based on their specificity to an experimental condition, by comparing intrinsic variation, i.e. variation in gene expression within an experimental condition, with extrinsic variation, i.e. variation in gene expression across experimental conditions. Genes with low variation within an experimental condition of interest and high variation across experimental conditions are ranked higher, and help in improving classification accuracy. We compared different feature selection methods on two time-series microarray datasets and one static microarray dataset. We found that the rSNR performed generally better than the other methods.

  • 92.
    Snipen, Lars
    et al.
    Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway.
    Repsilber, Dirk
    Department of Biology and Biochemistry/Bioinformatics, University of Potsdam, Germany.
    Nyquist, Ludvig
    Microbial Gene Technology, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway.
    Ziegler, Andreas
    Institute of Medical Biometry and Statistics, University at Lübeck, Germany.
    Aakra, Agot
    Microbial Gene Technology, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway.
    Aastveit, Are
    Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway.
    Detection of divergent genes in microbial aCGH experiments2006In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 7, article id 181Article in journal (Refereed)
    Abstract [en]

    Background: Array-based comparative genome hybridization (aCGH) is a tool for rapid comparison of genomes from different bacterial strains. The purpose of such analysis is to detect highly divergent or absent genes in a sample strain compared to an index strain. Development of methods for analyzing aCGH data has primarily focused on copy number abberations in cancer research. In microbial aCGH analyses, genes are typically ranked by log-ratios, and classification into divergent or present is done by choosing a cutoff log-ratio, either manually or by statistics calculated from the log-ratio distribution. As experimental settings vary considerably, it is not possible to develop a classical discriminant or statistical learning approach.

    Methods: We introduce a more efficient method for analyzing microbial aCGH data using a finite mixture model and a data rotation scheme. Using the average posterior probabilities from the model fitted to log-ratios before and after rotation, we get a score for each gene, and demonstrate its advantages for ranking and detecting divergent genes with enlarged specificity and sensitivity.

    Results: The procedure is tested and compared to other approaches on simulated data sets, as well as on four experimental validation data sets for aCGH analysis on fully sequenced strains of Staphylococcus aureus and Streptococcus pneumoniae.

    Conclusion: When tested on simulated data as well as on four different experimental validation data sets from experiments with only fully sequenced strains, our procedure out-competes the standard procedures of using a simple log-ratio cutoff for classification into present and divergent genes.

  • 93.
    Steinfath, Matthias
    et al.
    Institute of Biochemistry and Biology, University Potsdam, Germany.
    Repsilber, Dirk
    Institute of Biochemistry and Biology, University Potsdam, Germany.
    Hische, Manuela
    Institute of Biochemistry and Biology, University Potsdam, Germany.
    Schauer, Nicolas
    Max Plack Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Fernie, Alisdair R
    Max Plack Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Selbig, Joachim
    Institute of Biochemistry and Biology, University Potsdam, Germany.
    Metabolite profiles as a reflection of physiological status: a methodological validation2006In: Journal of Integrative Bioinformatics, ISSN 1613-4516, Vol. 3, no 2, p. 28-Article in journal (Refereed)
  • 94.
    Steinfath, Matthias
    et al.
    Institute for Biology and Biochemistry, University Potsdam, Potsdam-Golm, Germany .
    Repsilber, Dirk
    Institute for Biology and Biochemistry, University Potsdam, Potsdam-Golm, Germany.
    Scholz, Matthias
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany .
    Walther, Dirk
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany .
    Selbig, Joachim
    Institute for Biology and Biochemistry, University Potsdam, Potsdam-Golm, Germany; Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany .
    Integrated data analysis for genome-wide research2007In: Plant Systems Biology / [ed] Sacha Baginsky & Alisdair R. Fernie, Switzerland: Springer, 2007, Vol. 97, p. 309-29Chapter in book (Refereed)
    Abstract [en]

    Integrated data analysis is introduced as the intermediate level of a systems biology approach to analyse different 'omics' datasets, i.e., genome-wide measurements of transcripts, protein levels or protein-protein interactions, and metabolite levels aiming at generating a coherent understanding of biological function. In this chapter we focus on different methods of correlation analyses ranging from simple pairwise correlation to kernel canonical correlation which were recently applied in molecular biology. Several examples are presented to illustrate their application. The input data for this analysis frequently originate from different experimental platforms. Therefore, preprocessing steps such as data normalisation and missing value estimation are inherent to this approach. The corresponding procedures, potential pitfalls and biases, and available software solutions are reviewed. The multiplicity of observations obtained in omics-profiling experiments necessitates the application of multiple testing correction techniques.

  • 95.
    Sundin, Johanna
    et al.
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden.
    Rangel, Ignacio
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden.
    Repsilber, Dirk
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden.
    Brummer, Robert J.
    Örebro University, School of Medicine, Örebro University, Sweden.
    Cytokine response after stimulation with key commensal bacteria differ in post-­infectious irritable bowel syndrome (PI-­IBS) patients compared to healthy subjectsManuscript (preprint) (Other academic)
  • 96.
    Sundin, Johanna
    et al.
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden.
    Rangel, Ignacio
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden.
    Repsilber, Dirk
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden.
    Brummer, Robert-Jan
    Örebro University, School of Medicine, Örebro University, Sweden.
    Cytokine Response after Stimulation with Key Commensal Bacteria Differ in Post-Infectious Irritable Bowel Syndrome (PI-IBS) Patients Compared to Healthy Controls2015In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 9, article id e0134836Article in journal (Refereed)
    Abstract [en]

    Background: Microbial dysbiosis and prolonged immune activation resulting in low-grade inflammation and intestinal barrier dysfunction have been suggested to be underlying causes of post-infectious irritable bowel syndrome (PI-IBS). The aim of this study was to evaluate the difference in cytokine response between mucosal specimens of PI-IBS patients and healthy controls (HC) after ex vivo stimulation with key anaerobic bacteria.

    Methods: Colonic biopsies from 11 PI-IBS patients and 10 HC were stimulated ex vivo with the commensal bacteria Bacteroides ovatus, Ruminococcus gnavus, Akkermansia muciniphila, Subdoligranulum variabile and Eubacterium limosum, respectively. The cytokine release (IL-1 beta, IL-2, IL-8, IL-10, IL-13, IL-17, TNF-alpha and IFN-gamma) in stimulation supernatants was analyzed using the LUMINEX assay. Comparison of cytokine release between PI-IBS patients and healthy controls was performed taking both unstimulated and bacterially stimulated mucosal specimens into account.

    Key Results: IL-13 release from mucosal specimens without bacterial stimulation was significantly lower in PI-IBS patients compared to HC (p < 0.05). After stimulation with Subdoligranulum variabile, IL-1 beta release from PI-IBS patients was significantly increased compared to HC (p < 0.05). Stimulation with Eubacterium limosum resulted in a significantly decreased IL-10 release in HC compared to PI-IBS patients (p < 0.05) and a tendency to decreased IL-13 release in HC compared to PI-IBS patients (p = 0.07).

    Conclusions & Inferences: PI-IBS patients differ from HC with regard to cytokine release ex vivo after stimulation with selected commensal bacteria. Hence, our results support that the pathogenesis of PI-IBS comprises an altered immune response against commensal gut microbes.

  • 97.
    Telaar, Anna
    et al.
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Liland, K. H.
    Norwegian University of Life Sciences, Ås, Norway.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Nürnberg, Gerd
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Pooled samples for classification: What about PPLS-DA?2011In: 12th Day of the Doctoral Student FBN Dummerstorf: abstracts; 19 May 2011, Dummerstorf / [ed] Seyfert, H.-M., Viereck, G., Dummerstorf, Germany: FBN , 2011, p. 52-54Conference paper (Refereed)
  • 98.
    Telaar, Anna
    et al.
    Institute for Genetics and Biometry, Department of Bioinformatics and Biomathematics, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Liland, Kristian Hovde
    Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway.
    Repsilber, Dirk
    Institute for Genetics and Biometry, Department of Bioinformatics and Biomathematics, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Nürnberg, Gerd
    Institute for Genetics and Biometry, Department of Bioinformatics and Biomathematics, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    An extension of PPLS-DA for classification and comparison to ordinary PLS-DA2013In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, no 2, article id e55267Article in journal (Refereed)
    Abstract [en]

    Classification studies are widely applied, e.g. in biomedical research to classify objects/patients into predefined groups. The goal is to find a classification function/rule which assigns each object/patient to a unique group with the greatest possible accuracy (classification error). Especially in gene expression experiments often a lot of variables (genes) are measured for only few objects/patients. A suitable approach is the well-known method PLS-DA, which searches for a transformation to a lower dimensional space. Resulting new components are linear combinations of the original variables. An advancement of PLS-DA leads to PPLS-DA, introducing a so called 'power parameter', which is maximized towards the correlation between the components and the group-membership. We introduce an extension of PPLS-DA for optimizing this power parameter towards the final aim, namely towards a minimal classification error. We compare this new extension with the original PPLS-DA and also with the ordinary PLS-DA using simulated and experimental datasets. For the investigated data sets with weak linear dependency between features/variables, no improvement is shown for PPLS-DA and for the extensions compared to PLS-DA. A very weak linear dependency, a low proportion of differentially expressed genes for simulated data, does not lead to an improvement of PPLS-DA over PLS-DA, but our extension shows a lower prediction error. On the contrary, for the data set with strong between-feature collinearity and a low proportion of differentially expressed genes and a large total number of genes, the prediction error of PPLS-DA and the extensions is clearly lower than for PLS-DA. Moreover we compare these prediction results with results of support vector machines with linear kernel and linear discriminant analysis.

  • 99.
    Telaar, Anna
    et al.
    Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Nürnberg, Gerd
    Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Repsilber, Dirk
    Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Finding biomarker signatures in pooled sample designs: a simulation framework for methodological comparisons2010In: Advances in Bioinformatics, ISSN 1687-8027, E-ISSN 1687-8035, article id 318573Article in journal (Refereed)
    Abstract [en]

    Detection of discriminating patterns in gene expression data can be accomplished by using various methods of statistical learning. It has been proposed that sample pooling in this context would have negative effects; however, pooling cannot always be avoided. We propose a simulation framework to explicitly investigate the parameters of patterns, experimental design, noise, and choice of method in order to find out which effects on classification performance are to be expected. We use a two-group classification task and simulated gene expression data with independent differentially expressed genes as well as bivariate linear patterns and the combination of both. Our results show a clear increase of prediction error with pool size. For pooled training sets powered partial least squares discriminant analysis outperforms discriminance analysis, random forests, and support vector machines with linear or radial kernel for two of three simulated scenarios. The proposed simulation approach can be implemented to systematically investigate a number of additional scenarios of practical interest.

  • 100.
    Telaar, Anna
    et al.
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Nürnberg, Gerd
    Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.
    Biomarker discovery: classification using pooled samples2013In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 28, no 1, p. 67-106Article in journal (Refereed)
    Abstract [en]

    RNA-sample pooling is sometimes inevitable, but should be avoided in classification tasks like biomarker studies. Our simulation framework investigates a two-class classification study based on gene expression profiles to point out howstrong the outcomes of single sample designs differ to those of pooling designs. The results show how the effects of pooling depend on pool size, discriminating pattern, number of informative features and the statistical learning method used (support vector machines with linear and radial kernel, random forest (RF), linear discriminant analysis, powered partial least squares discriminant analysis (PPLS-DA) and partial least squares discriminant analysis (PLS-DA)). As a measure for the pooling effect, we consider prediction error (PE) and the coincidence of important feature sets for classification based on PLS-DA, PPLS-DAand RF. In general, PPLS-DAand PLS-DAshow constant PE with increasing pool size and low PE for patterns for which the convex hull of one class is not a cover of the other class. The coincidence of important feature sets is larger for PLS-DA and PPLS-DA as it is for RF. RF shows the best results for patterns in which the convex hull of one class is a cover of the other class, but these depend strongly on the pool size. We complete the PE results with experimental data whichwe pool artificially. The PE of PPLS-DAand PLS-DAare again least influenced by pooling and are low. Additionally, we show under which assumption the PLS-DA loading weights, as a measure for importance of features regarding classification, are equal for the different designs.

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