6 results on '"Kristian Davidsen"'
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2. Author response: Deep generative models for T cell receptor protein sequences
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Frederick A. Matsen, Jean Feng, Branden J. Olson, William S DeWitt, Elias Harkins, Kristian Davidsen, and Philip Bradley
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T-cell receptor ,Biology ,Generative grammar ,Cell biology - Published
- 2019
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3. Benchmarking tree and ancestral sequence inference for B cell receptor sequences
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Kristian Davidsen and Frederick A. Matsen
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0301 basic medicine ,B cell receptor repertoire ,lcsh:Immunologic diseases. Allergy ,B-cell receptor ,Immunology ,Inference ,Receptors, Antigen, B-Cell ,Computational biology ,Biology ,phylogeny ,Affinity maturation ,Evolution, Molecular ,03 medical and health sciences ,Tree (descriptive set theory) ,0302 clinical medicine ,Phylogenetics ,Immunology and Allergy ,Humans ,ancestral sequence reconstruction ,antibodies ,Computer Simulation ,benchmarking ,030304 developmental biology ,Original Research ,0303 health sciences ,B-Lymphocytes ,Phylogenetic tree ,Models, Genetic ,Immunoglobulin Class Switching ,030104 developmental biology ,Tree structure ,Mutation (genetic algorithm) ,Mutation ,lcsh:RC581-607 ,Algorithms ,030215 immunology - Abstract
B cell receptor sequences evolve during affinity maturation according to a Darwinian process of mutation and selection. Phylogenetic tools are used extensively to reconstruct ancestral sequences and phylogenetic trees from affinity-matured sequences. In addition to using general-purpose phylogenetic methods, researchers have developed new tools to accommodate the special features of B cell sequence evolution. However, the performance of classical phylogenetic techniques in the presence of B cell-specific features is not well understood, nor how much the newer generation of B cell specific tools represent an improvement over classical methods. In this paper we benchmark the performance of classical phylogenetic and new B cell-specific tools when applied to B cell receptor sequences simulated from a forward-time model of B cell receptor affinity maturation towards a mature receptor. We show that the currently used tools vary substantially in terms of tree structure and ancestral sequence inference accuracy. Furthermore, we show that there are still large performance gains to be achieved by modeling the special mutation process of B cell receptors. These conclusions are further strengthened with real data using the rules of isotype switching to count possible violations within each inferred phylogeny.
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- 2018
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4. High-resolution kinetics and modeling of hydrogen peroxide degradation in live cells
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Uffe Hasbro Mortensen, Thomas Sams, J. Christian Brasen, Christopher T. Workman, Kristian Davidsen, Ali Altıntaş, and Christian Garde
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0301 basic medicine ,Saccharomyces cerevisiae Proteins ,Mutant ,Kinetics ,Saccharomyces cerevisiae ,Biology ,medicine.disease_cause ,Biochemistry ,03 medical and health sciences ,Physiology (medical) ,Gene Expression Regulation, Fungal ,medicine ,Transcription factor ,Models, Statistical ,Hydrogen Peroxide ,biology.organism_classification ,Yeast ,Cell biology ,High-Throughput Screening Assays ,DNA-Binding Proteins ,Oxidative Stress ,030104 developmental biology ,Multigene Family ,Inactivation, Metabolic ,Intracellular ,Function (biology) ,Oxidative stress ,Gene Deletion ,Transcription Factors - Abstract
Although the role of oxidative stress factors and their regulation is well studied, the temporal dynamics of stress recovery is still poorly understood. In particular, measuring the kinetics of stress recovery in the first minutes after acute exposure provides a powerful technique for assessing the role of regulatory proteins or enzymes through the use of mutant backgrounds. This project endeavors to screen the temporal dynamics of intracellular oxidant levels in live cells as a function of gene deletion in the budding yeast, Saccharomyces cerevisiae. Using the detailed time dynamics of extra- and intra-cellular peroxide we have developed a mathematical model that describes two distinct kinetic processes, an initial rapid degradation in the first 10-20min followed by a slower process. Using this model, a qualitative comparison allowed us to assign the dependence of temporal events to genetic factors. Surprisingly, we found that the deletion of transcription factors Yap1p or Skn7p was sufficient to disrupt the establishment of the second degradation phase but not the initial phase. A better fundamental understanding of the role protective factors play in the recovery from oxidative stress may lead to strategies for protecting or sensitizing cell to this stress.
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- 2016
5. Antibody Cross-Reactivity in Antivenom Research
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Andreas Hougaard Laustsen, Timothy P. Jenkins, Ole Lund, Andrea Martos-Esteban, Mikael Rørdam Andersen, Mikael Engmark, Kristian Davidsen, Line Ledsgaard, and Kamille Elvstrøm Krause
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0301 basic medicine ,cross-reactivity ,antivenomics ,Health, Toxicology and Mutagenesis ,Antivenom ,venom ,lcsh:Medicine ,Cross-neutralization ,Venom ,Review ,Computational biology ,Cross Reactions ,Toxicology ,medicine.disease_cause ,complex mixtures ,Cross-reactivity ,Epitope ,Enzymatic Assays ,03 medical and health sciences ,medicine ,Animals ,Toxins ,Snakebite envenoming ,antivenom ,biology ,Antivenins ,business.industry ,cross-neutralization ,lcsh:R ,toxins ,Antivenomics ,Immunodiffusion ,030104 developmental biology ,high-density peptide microarray technology ,biology.protein ,High-density peptide microarray technology ,Peptide microarray ,Antibody ,Peptides ,business ,Snake Venoms ,snakebite envenoming - Abstract
Antivenom cross-reactivity has been investigated for decades to determine which antivenoms can be used to treat snakebite envenomings from different snake species. Traditionally, the methods used for analyzing cross-reactivity have been immunodiffusion, immunoblotting, enzyme-linked immunosorbent assay (ELISA), enzymatic assays, and in vivo neutralization studies. In recent years, new methods for determination of cross-reactivity have emerged, including surface plasmon resonance, antivenomics, and high-density peptide microarray technology. Antivenomics involves a top-down assessment of the toxin-binding capacities of antivenoms, whereas high-density peptide microarray technology may be harnessed to provide in-depth knowledge on which toxin epitopes are recognized by antivenoms. This review provides an overview of both the classical and new methods used to investigate antivenom cross-reactivity, the advantages and disadvantages of each method, and examples of studies using the methods. A special focus is given to antivenomics and high-density peptide microarray technology as these high-throughput methods have recently been introduced in this field and may enable more detailed assessments of antivenom cross-reactivity.
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- 2018
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6. Tools and data services registry: a community effort to document bioinformatics resources
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Callum Smith, Paolo Uva, Thomas Gatter, Peter Løngreen, Peter Juvan, Hans Ienasescu, Giuseppe Profiti, Aleksandra Nenadic, Kristoffer Rapacki, Chris Morris, Paola Roncaglia, Steffen Möller, Laura Emery, Søren Brunak, Maria Maddalena Sperotto, Heinz Stockinger, Kristian Davidsen, Federico Zambelli, Helen Parkinson, Olivia Doppelt-Azeroual, Luana Licata, Tatyana Goldberg, Andrea Schafferhans, Elisabeth Gasteiger, Emil Karol Rydza, Camille Laibe, Victor De La Torre, Marie Grosjean, Manuela Helmer-Citterich, Hervé Ménager, Radka Svobodová Vařeková, Rafael C. Jimenez, Martin Closter Jespersen, Anthony Bretaudeau, Jan Brezovsky, Tunca Doğan, Matúš Kalaš, Peter M. Rice, Ivan Mičetić, Rune Møllegaard Friborg, Maximilian Koch, Silvio C. E. Tosatto, Nick Juty, Björn Grüning, Gianmauro Cuccuru, Frederik Coppens, Gianni Cesareni, Jon Ison, Rabie Saidi, Sébastien Moretti, Rita Casadio, Gert Vriend, Guy Yachdav, Niall Beard, Timothy F. Booth, Michael Cornell, Piotr Jaroslaw Chmura, Veit Schwämmle, Karel Berka, Dan Bolser, Vassilios Ioannidis, Jing-Woei Li, Burkhard Rost, Gianluca Della Vedova, Fabien Mareuil, Hedi Peterson, Allegra Via, Paolo Romano, Christian Anthon, Technical University of Denmark [Lyngby] (DTU), Institut Pasteur de Madagascar, Réseau International des Instituts Pasteur (RIIP), University of Bergen (UIB), University of Copenhagen = Københavns Universitet (KU), University of Manchester, Palacky University, European Bioinformatics Institute, NEBC Wallingford, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Masaryk University, University of Bologna, Università degli Studi di Roma Tor Vergata [Roma], Ghent University [Belgium] (UGENT), Flanders Institute for Biotechnology, CRS4 Bioinformat, Università degli studi di Milano-Bicocca, Swiss Institute of Bioinformatics, Universität Bielefeld = Bielefeld University, Tumor Biology Center, Centre National de la Recherche Scientifique (CNRS), University of Freiburg, University of Ljubljana, The Chinese University of Hong Kong [Hong Kong], Universita degli Studi di Padova, Bioinformatics Research Centre, Université de Lausanne, CCLRC Daresbury Laboratory, Universität zu Lübeck [Lübeck] - University of Lübeck [Lübeck], Universität Rostock, University of Tartu, Imperial College London, IRCCS Azienda Ospedaliera Universitaria Integrata San Martino (IRCCS AOU San Martino), University of Southern Denmark (SDU), WTCHG, Central European Institute of Technology [Brno] (CEITEC), Instituto Nacional de Bioinformática, Sapienza University of Rome (DIAG), Consiglio Nazionale delle Ricerche, University of Milan, Radboud University Nijmegen, Ison, J, Rapacki, K, Ménager, H, Kalaš, M, Rydza, E, Chmura, P, Anthon, C, Beard, N, Berka, K, Bolser, D, Booth, T, Bretaudeau, A, Brezovsky, J, Casadio, R, Cesareni, G, Coppens, F, Cornell, M, Cuccuru, G, Davidsen, K, DELLA VEDOVA, G, Dogan, T, Doppelt Azeroual, O, Emery, L, Gasteiger, E, Gatter, T, Goldberg, T, Grosjean, M, Grüning, B, Helmer Citterich, M, Ienasescu, H, Ioannidis, V, Jespersen, M, Jimenez, R, Juty, N, Juvan, P, Koch, M, Laibe, C, Li, J, Licata, L, Mareuil, F, Mičetić, I, Friborg, R, Moretti, S, Morris, C, Möller, S, Nenadic, A, Peterson, H, Profiti, G, Rice, P, Romano, P, Roncaglia, P, Saidi, R, Schafferhans, A, Schwämmle, V, Smith, C, Sperotto, M, Stockinger, H, Vařeková, R, Tosatto, S, de la Torre, V, Uva, P, Via, A, Yachdav, G, Zambelli, F, Vriend, G, Rost, B, Parkinson, H, Løngreen, P, Brunak, S, University of Bergen (UiB), Palacky University Olomouc, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Masaryk University [Brno] (MUNI), Universiteit Gent = Ghent University [Belgium] (UGENT), Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne (UNIL), Universität zu Lübeck [Lübeck], Central European Institute of Technology [Brno] (CEITEC MU), Brno University of Technology [Brno] (BUT), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Danmarks Tekniske Universitet = Technical University of Denmark (DTU), University of Copenhagen = Københavns Universitet (UCPH), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, University of Bologna/Università di Bologna, Universiteit Gent = Ghent University (UGENT), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), Université de Lausanne = University of Lausanne (UNIL), Università degli Studi di Padova = University of Padua (Unipd), Universität zu Lübeck = University of Lübeck [Lübeck], Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), Università degli Studi di Milano = University of Milan (UNIMI), Ison, Jon, Rapacki, Kristoffer, Ménager, Hervé, Kalaš, Matúš, Rydza, Emil, Chmura, Piotr, Anthon, Christian, Beard, Niall, Berka, Karel, Bolser, Dan, Booth, Tim, Bretaudeau, Anthony, Brezovsky, Jan, Casadio, Rita, Cesareni, Gianni, Coppens, Frederik, Cornell, Michael, Cuccuru, Gianmauro, Davidsen, Kristian, Vedova, Gianluca Della, Dogan, Tunca, Doppelt-Azeroual, Olivia, Emery, Laura, Gasteiger, Elisabeth, Gatter, Thoma, Goldberg, Tatyana, Grosjean, Marie, Grüning, Björn, Helmer-Citterich, Manuela, Ienasescu, Han, Ioannidis, Vassilio, Jespersen, Martin Closter, Jimenez, Rafael, Juty, Nick, Juvan, Peter, Koch, Maximilian, Laibe, Camille, Li, Jing-Woei, Licata, Luana, Mareuil, Fabien, Mičetić, Ivan, Friborg, Rune Møllegaard, Moretti, Sebastien, Morris, Chri, Möller, Steffen, Nenadic, Aleksandra, Peterson, Hedi, Profiti, Giuseppe, Rice, Peter, Romano, Paolo, Roncaglia, Paola, Saidi, Rabie, Schafferhans, Andrea, Schwämmle, Veit, Smith, Callum, Sperotto, Maria Maddalena, Stockinger, Heinz, Vařeková, Radka Svobodová, Tosatto, Silvio C E, de la Torre, Victor, Uva, Paolo, Via, Allegra, Yachdav, Guy, Zambelli, Federico, Vriend, Gert, Rost, Burkhard, Parkinson, Helen, Løngreen, Peter, and Brunak, Søren
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0301 basic medicine ,[SDV]Life Sciences [q-bio] ,registry ,Bioinformatics ,computer.software_genre ,Matematikk og naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Systemutvikling og -arbeid: 426 [VDP] ,Task (project management) ,Documentation ,Data and Information ,Database Issue ,Registries ,bioinformatique ,Data Curation ,base de données ,Settore BIO/11 ,gestion de données ,tool ,SOFTWARE-DEVELOPMENT ,bioinformatics ,ddc ,outil informatique ,Tools and data services registry ,SEQANSWERS ,Web service ,MOLECULAR-BIOLOGY ,Biology ,Ecology and Environment ,03 medical and health sciences ,SDG 3 - Good Health and Well-being ,Genetics ,Implementation ,Dissemination ,Bioinformatikk / Bioinformatics ,Data curation ,bioinformatic ,business.industry ,Computational Biology ,Software ,Software development ,bioinformatics, tools, registry, elixir ,Biology and Life Sciences ,Mathematics and natural scienses: 400::Information and communication science: 420::System development and design: 426 [VDP] ,FRAMEWORK ,ELIXIR ,Settore BIO/18 - Genetica ,030104 developmental biology ,tools ,Data as a service ,COMPILATION ,business ,COLLECTION ,Nanomedicine Radboud Institute for Molecular Life Sciences [Radboudumc 19] ,computer ,WEB SERVICES ,LIFE SCIENCES - Abstract
Contains fulltext : 171819.pdf (Publisher’s version ) (Open Access) Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand.Here we present a community-driven curation effort, supported by ELIXIR-the European infrastructure for biological information-that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners.As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools.
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