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  • 1.
    Alneberg, Johannes
    et al.
    Science for Life Laboratory, School of Biotechnology, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
    Bjarnason, Brynjar Smári
    Science for Life Laboratory, School of Biotechnology, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
    de Bruijn, Ino
    Bioinformatics Infrastructure for Life Sciences (BILS), Stockholm, Sweden.
    Schirmer, Melanie
    School of Engineering, University of Glasgow, Glasgow, UK.
    Quick, Joshua
    Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK; National Institute for Health Research (NIHR), Surgical Reconstruction and Microbiology Research Centre, University of Birmingham, Birmingham, UK.
    Ijaz, Umer Z.
    School of Engineering, University of Glasgow, Glasgow, UK.
    Lahti, Leo
    Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland; Laboratory of Microbiology, Wageningen University, Wageningen, the Netherlands.
    Loman, Nicholas J
    Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK.
    Andersson, Anders F
    Science for Life Laboratory, School of Biotechnoloy, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
    Quince, Christopher
    School of Engineering, University of Glasgow, Glasgow, UK.
    Binning metagenomic contigs by coverage and composition2014In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 11, no 11, p. 1144-6Article in journal (Refereed)
    Abstract [en]

    Shotgun sequencing enables the reconstruction of genomes from complex microbial communities, but because assembly does not reconstruct entire genomes, it is necessary to bin genome fragments. Here we present CONCOCT, a new algorithm that combines sequence composition and coverage across multiple samples, to automatically cluster contigs into genomes. We demonstrate high recall and precision on artificial as well as real human gut metagenome data sets.

  • 2.
    Antila, Kari
    et al.
    VTT Technical Research Centre of Finland, Tampere, Finland.
    Lötjönen, Jyrki
    VTT Technical Research Centre of Finland, Tampere, Finland.
    Thurfjell, Lennart
    GE Healthcare, Stockholm, Sweden.
    Laine, Jarmo
    Nexstim Ltd, Helsinki, Finland.
    Massimini, Marcello
    University of Milan, Milan, Italy.
    Rueckert, Daniel
    Imperial College London, London, United Kingdom.
    Zubarev, Roman A.
    Karolinska Institutet, Stockholm, Sweden.
    Oresic, Matej
    Örebro University, School of Medical Sciences. VTT Technical Research Centre of Finland, Tampere, Finland.
    van Gils, Mark
    VTT Technical Research Centre of Finland, Tampere, Finland.
    Mattila, Jussi
    VTT Technical Research Centre of Finland, Tampere, Finland.
    Hviid Simonsen, Anja
    Rigshospitalet, Copenhagen, Denmark.
    Waldemar, Gunhild
    Rigshospitalet, Copenhagen, Denmark.
    Soininen, Hilkka
    University of Eastern Finland, Kuopio, Finland.
    The PredictAD project: development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease2013In: Interface Focus, ISSN 2042-8898, E-ISSN 2042-8901, Vol. 3, no 2, article id 20120072Article in journal (Refereed)
    Abstract [en]

    Alzheimer's disease (AD) is the most common cause of dementia affecting 36 million people worldwide. As the demographic transition in the developed countries progresses towards older population, the worsening ratio of workers per retirees and the growing number of patients with age-related illnesses such as AD will challenge the current healthcare systems and national economies. For these reasons AD has been identified as a health priority, and various methods for diagnosis and many candidates for therapies are under intense research. Even though there is currently no cure for AD, its effects can be managed. Today the significance of early and precise diagnosis of AD is emphasized in order to minimize its irreversible effects on the nervous system. When new drugs and therapies enter the market it is also vital to effectively identify the right candidates to benefit from these. The main objective of the PredictAD project was to find and integrate efficient biomarkers from heterogeneous patient data to make early diagnosis and to monitor the progress of AD in a more efficient, reliable and objective manner. The project focused on discovering biomarkers from biomolecular data, electrophysiological measurements of the brain and structural, functional and molecular brain images. We also designed and built a statistical model and a framework for exploiting these biomarkers with other available patient history and background data. We were able to discover several potential novel biomarker candidates and implement the framework in software. The results are currently used in several research projects, licensed to commercial use and being tested for clinical use in several trials.

  • 3.
    de Mas, Igor Marin
    et al.
    Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain;; Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain.
    Selivanov, Vitaly A.
    Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain; Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain; A.N.Belozersky Institute of Physico-Chemical Biology, Moscow, Russia.
    Marin, Silvia
    Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain;; Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain.
    Roca, Josep
    Hospital Clínic, August Pi i Sunyer Biomedical Research Institute (IDIBAPS),Centro de Investigación Biomédica en Red de Enfermedade Respiratorias (CIBERES) Universitat de Barcelona, Barcelona, Spain.
    Oresic, Matej
    Technical Research Centre of Finland, Espoo, Finland; Institute for Molecular Medicine, Helsinki, Finland.
    Agius, Loranne
    Institute of Cellular Medicine, The Medical School, Newcastle University, Newcastle, UK.
    Cascante, Marta
    Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain; Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain.
    Compartmentation of glycogen metabolism revealed from 13C isotopologue distributions2011In: BMC Systems Biology, ISSN 1752-0509, E-ISSN 1752-0509, Vol. 5, article id 175Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Stable isotope tracers are used to assess metabolic flux profiles in living cells. The existing methods of measurement average out the isotopic isomer distribution in metabolites throughout the cell, whereas the knowledge of compartmental organization of analyzed pathways is crucial for the evaluation of true fluxes. That is why we accepted a challenge to create a software tool that allows deciphering the compartmentation of metabolites based on the analysis of average isotopic isomer distribution.

    RESULTS: The software Isodyn, which simulates the dynamics of isotopic isomer distribution in central metabolic pathways, was supplemented by algorithms facilitating the transition between various analyzed metabolic schemes, and by the tools for model discrimination. It simulated 13C isotope distributions in glucose, lactate, glutamate and glycogen, measured by mass spectrometry after incubation of hepatocytes in the presence of only labeled glucose or glucose and lactate together (with label either in glucose or lactate). The simulations assumed either a single intracellular hexose phosphate pool, or also channeling of hexose phosphates resulting in a different isotopic composition of glycogen. Model discrimination test was applied to check the consistency of both models with experimental data. Metabolic flux profiles, evaluated with the accepted model that assumes channeling, revealed the range of changes in metabolic fluxes in liver cells.

    CONCLUSIONS: The analysis of compartmentation of metabolic networks based on the measured 13C distribution was included in Isodyn as a routine procedure. The advantage of this implementation is that, being a part of evaluation of metabolic fluxes, it does not require additional experiments to study metabolic compartmentation. The analysis of experimental data revealed that the distribution of measured 13C-labeled glucose metabolites is inconsistent with the idea of perfect mixing of hexose phosphates in cytosol. In contrast, the observed distribution indicates the presence of a separate pool of hexose phosphates that is channeled towards glycogen synthesis.

  • 4.
    Katajamaa, Mikko
    et al.
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Miettinen, Jarkko
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Oresic, Matej
    Turku Centre for Biotechnology, Turku, Finland.
    MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data2006In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 22, no 5, p. 634-636Article in journal (Refereed)
    Abstract [en]

    Summary: New additional methods are presented for processing and visualizing mass spectrometry based molecular profile data, implemented as part of the recently introduced MZmine software. They include new features and extensions such as support for mzXML data format, capability to perform batch processing for large number of files, support for parallel processing, new methods for calculating peak areas using post-alignment peak picking algorithm and implementation of Sammon's mapping and curvilinear distance analysis for data visualization and exploratory analysis.

    Avalibility: MZmine is available under GNU Public license from http://mzmine.sourceforge.net/.

  • 5.
    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.

  • 6.
    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)
  • 7.
    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.

  • 8. 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)
  • 9.
    Rush, Stephen
    et al.
    Department of Mathematics and Statistics, University of Guelph, Guelph ON, Canada.
    Pinder, Shaun
    Department of Mathematics and Statistics, University of Guelph, Guelph ON, Canada.
    Costa, Marcio
    Department of Pathobiology, University of Guelph, Guelph ON, Canada.
    Kim, Peter
    Department of Mathematics and Statistics, University of Guelph, Guelph ON, Canada.
    A microbiology primer for pyrosequencing2012In: Quantitative Bio-Science, ISSN 2288-1344, Vol. 31, no 2, p. 53-81Article in journal (Refereed)
    Abstract [en]

    Metagenomic analysis is a very rich area for understanding the microbiology of organisms. Once the data has been assembled mathematical and statistical methods can be applied providing insights into biological properties that created the data in the first place. The foundations however, require some knowledge of microbiology which is not usually part of a mathematician’s nor a statistician’s training, and therefore, the data creation can itself be quite mysterious. In this paper we attempt to explain the microbiology to mathematicians and statisticians in a way that would hopefully provide insights into the data generating process. In particular our approach is specific to the open-source bioinformatics toolbox mothur. We will assume the reader has very little microbiology training but has some mathematical skills. It is the endeavor of this write-up to help bridge a needed gap.

  • 10.
    Salek, Reza M
    et al.
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus Hinxton, Cambridge, United Kingdom; Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.
    Neumann, Steffen
    Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.
    Schober, Daniel
    Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.
    Hummel, Jan
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Billiau, Kenny
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Kopka, Joachim
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Correa, Elon
    School of Chemistry & Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom.
    Reijmers, Theo
    Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands.
    Rosato, Antonio
    Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino FI, Italy.
    Tenori, Leonardo
    Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino FI, Italy; FiorGen Foundation, Sesto Fiorentin FI, Italy.
    Turano, Paola
    Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino FI, Italy.
    Marin, Silvia
    Department of Biochemistry and Molecular Biology, IBUB, Universitat de Barcelona, Barcelona, Spain.
    Deborde, Catherine
    INRA, Univ. Bordeaux, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux (MetaboHUB), Functional Genomics Center, IBVM, Centre INRA Bordeaux, Villenave d’Ornon, France.
    Jacob, Daniel
    INRA, Univ. Bordeaux, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux (MetaboHUB), Functional Genomics Center, IBVM, Centre INRA Bordeaux, Villenave d’Ornon, France.
    Rolin, Dominique
    INRA, Univ. Bordeaux, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux (MetaboHUB), Functional Genomics Center, IBVM, Centre INRA Bordeaux, Villenave d’Ornon, France.
    Dartigues, Benjamin
    Centre of bioinformatics of Bordeaux (CBiB), University of Bordeaux, Bordeaux, France.
    Conesa, Pablo
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus Hinxton, Cambridge, United Kingdom.
    Haug, Kenneth
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus Hinxton, Cambridge, United Kingdom.
    Rocca-Serra, Philippe
    University of Oxford e-Research Centre, Oxford, United Kingdom.
    O'Hagan, Steve
    School of Chemistry & Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom.
    Hao, Jie
    Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, United Kingdom.
    van Vliet, Michael
    Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands.
    Sysi-Aho, Marko
    Zora Biosciences OY, Espoo, Finland.
    Ludwig, Christian
    School of Cancer Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom.
    Bouwman, Jildau
    Microbiology & Systems Biology TNO, Zeist, Netherlands.
    Cascante, Marta
    Department of Biochemistry and Molecular Biology, IBUB, Universitat de Barcelona, Barcelona, Spain.
    Ebbels, Timothy
    Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, United Kingdom.
    Griffin, Julian L
    Medical Research Council Human Nutrition Research, Cambridge, United Kingdom; Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.
    Moing, Annick
    INRA, UMR1332 Fruit Biology and Pathology, Metabolome Facility of Bordeaux (MetaboHUB), Functional Genomics Center (IBVM), Centre INRA Bordeaux Villenave d’Ornon, Univ. Bordeaux, Bordeaux, France.
    Nikolski, Macha
    University of Bordeaux CBiB/LaBRI, Bordeaux, France.
    Oresic, Matej
    Örebro University, School of Medical Sciences. Zora Biosciences OY, Espoo, Finland.
    Sansone, Susanna-Assunta
    University of Oxford e-Research Centre, Oxford, United Kingdom.
    Viant, Mark R.
    School of Biosciences, University of Birmingham Edgbaston, Birmingham, United Kingdom.
    Goodacre, Royston
    School of Chemistry & Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom.
    Günther, Ulrich L
    School of Cancer Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom.
    Hankemeier, Thomas
    Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands.
    Luchinat, Claudio
    Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino FI, Italy.
    Walther, Dirk
    Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
    Steinbeck, Christoph
    European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus Hinxton, Cambridge, United Kingdom.
    COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access2015In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 11, no 6, p. 1587-1597Article in journal (Refereed)
    Abstract [en]

    Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative 'coordination of standards in metabolomics' (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities' participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.

  • 11.
    Sen, Partho
    et al.
    Örebro University, School of Medical Sciences. Örebro University Hospital. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland; .
    Oresic, Matej
    Örebro University, School of Medical Sciences. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland.
    Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview2019In: Metabolites, ISSN 2218-1989, E-ISSN 2218-1989, Vol. 9, no 2, article id E22Article, review/survey (Refereed)
    Abstract [en]

    There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet⁻microbiome, microbe⁻microbe and host⁻microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.

  • 12.
    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.

  • 13.
    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.

  • 14.
    Xueli, Zhang
    et al.
    Örebro University, School of Medical Sciences. Centre for Systems Biology, Soochow University, Suzhou, China.
    Sun, Xiao-Feng
    Department of Oncology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden.
    Cao, Yang
    Örebro University, School of Medical Sciences. Örebro University Hospital. Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden.
    Ye, Benchen
    Centre for Systems Biology, Soochow University, Suzhou, China.
    Peng, Qiliang
    Department of Radiotherapy and Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
    Liu, Xingyun
    Centre for Systems Biology, Soochow University, Suzhou, China.
    Shen, Bairong
    Centre for Systems Biology, Soochow University, Suzhou, China.
    Zhang, Hong
    Örebro University, School of Medical Sciences.
    CBD: a biomarker database for colorectal cancer2018In: Database: The Journal of Biological Databases and Curation, ISSN 1758-0463, E-ISSN 1758-0463, article id bay046Article in journal (Refereed)
    Abstract [en]

    Colorectal cancer (CRC) biomarker database (CBD) was established based on 870 identified CRC biomarkers and their relevant information from 1115 original articles in PubMed published from 1986 to 2017. In this version of the CBD, CRC biomarker data were collected, sorted, displayed and analysed. The CBD with the credible contents as a powerful and time-saving tool provide more comprehensive and accurate information for further CRC biomarker research. The CBD was constructed under MySQL server. HTML, PHP and JavaScript languages have been used to implement the web interface. The Apache was selected as HTTP server. All of these web operations were implemented under the Windows system. The CBD could provide to users the multiple individual biomarker information and categorized into the biological category, source and application of biomarkers; the experiment methods, results, authors and publication resources; the research region, the average age of cohort, gender, race, the number of tumours, tumour location and stage. We only collect data from the articles with clear and credible results to prove the biomarkers are useful in the diagnosis, treatment or prognosis of CRC. The CBD can also provide a professional platform to researchers who are interested in CRC research to communicate, exchange their research ideas and further design high-quality research in CRC. They can submit their new findings to our database via the submission page and communicate with us in the CBD.

  • 15. Ziegler, Andreas
    et al.
    Repsilber, Dirk
    Data rotation for efficient comparative genomics2004Conference paper (Refereed)
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