DOCSLIB.ORG

Research Report 2018

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Research Report 2018 MPIMG RESEARCH REPORT 2018 Max Planck Institute for Molecular Genetics Impairment of cerebral organoid development induced by microRNA overexpression Shown are day 30 organoids following microRNA overexpression (left), and their matched day 30 untreated organoids (right), stained for the neural stem/progenitor cell marker SOX2 (red) and the neuronal differentiation marker DCX (green). The development of cerebral organoid cytoarchitecture can be clearly seen in control organoids, while it appears stalled following microRNA overexpression. The Elkabetz group is studying the mechanisms by which microRNAs govern cerebral development. Picture: Elkabetz Lab / Human Brain and Neural Stem Cell Studies MPIMG RESEARCH REPORT 2018 Max Planck Institute for Molecular Genetics Michael Müller, Governing Mayor of Berlin, at a visit at the MPIMG during the Long Night of Sciences 2017 (from left to right: Hans Lehrach, Martin Vingron, Michael Müller, Bernhard Herrmann, Alexander Meissner) 4 The Max Planck Institute for Molecular Genetics – Research Report 2018 Editorial We are very pleased to present the 2018 Research Report of the Max Planck Institute for Molecular Genetics (MPIMG), covering the time period from 2015 to mid-2018. The Insti- tute looks back on three challenging and productive years. The first important step in the transition and scientific reorientation following the retire- ment of H.-Hilger Ropers and Hans Lehrach in 2014 has been completed successfully by the appointment of Alexander Meissner to director of the new department “Genome Regulation”. This recruitment marks the beginning of a new important track of research at the MPIMG with a strong emphasis on gene regulatory mechanisms in stem cell differen- tiation, embryogenesis and human disease. In addition, the refurbishment of tower 2 has been finalized successfully, providing two departments and a large research group with state-of-the art laboratories. We are also looking forward to the refurbishment of tower 1, which after a long preparation phase is finally scheduled to start this year. We would like to take the occasion to express our gratitude to the Max Planck Society and the many funding agencies that support our work, for providing scientific freedom and independence to pursue the research we burn for and to tackle the most interesting sci- entific questions. We are confident that the scientific results presented in this report have made a significant contribution to science. Compared to earlier reports, this year we have strived to produce a shorter, more concise report. Compact information on our research projects and research results can be found in the reports of the individual departments and groups. In addition, the introduction sum- marizes the main development of the MPIMG and gives a broader outline of the Institute. We hope that the Research Report 2018 will provide a clear impression of the Institute and the work we are doing. Berlin, October 2018 Bernhard G. Herrmann, Alexander Meissner, Martin Vingron 5 The Max Planck Institute for Molecular Genetics – Research Report 2018 Table of contents The Max Planck Institute for Molecular Genetics ..................................................................................7 Structure and organization of the Institute ...............................................................................................7 Research concept of the Institute ..............................................................................................................9 Scientific highlights ......................................................................................................................................9 Scientific awards.........................................................................................................................................12 Appointments of former members of the MPIMG ..................................................................................13 Recruitment of new group leaders ...........................................................................................................13 Support of junior scientists at the MPIMG ..............................................................................................14 Equal opportunities .....................................................................................................................................15 Material resources, equipment and spatial arrangements ...................................................................16 Public relations work ..................................................................................................................................18 Department of Developmental Genetics .................................................................................................20 Department of Genome Regulation ..........................................................................................................28 Meissner Lab – Genome Regulation Group .............................................................................................30 Elkabetz Lab – Lab for Human Brain & Neural Stem Cell Studies ........................................................36 Department of Computational Molecular Biology .................................................................................40 Vingron Lab – Transcriptional Regulation Group ....................................................................................42 Meijsing Lab – Mechanisms of Transcriptional Regulation Group .......................................................50 Herwig Lab – Bioinformatics Group .........................................................................................................55 Hoehe Lab – Diploid Genomics Group ......................................................................................................60 Arndt Lab – Research Group Evolutionary Genomics ............................................................................63 Research Group Development & Disease ...............................................................................................68 Mundlos Lab – Research Group Development & Disease .....................................................................68 Kalscheuer Lab – Chromosome Rearrangements and Disease Group ................................................76 6 The Max Planck Institute for Molecular Genetics – Research Report 2018 Table of contents Otto Warburg Laboratories .......................................................................................................................82 Chung Lab – Max Planck Research Group Epigenomics .......................................................................82 Marsico Lab – Research Group RNA Bioinformatics .............................................................................86 Mayer Lab – Max Planck Research Group Nascent Transcription & Cell Differentiation ................92 Ørom Lab – Research Group Non-coding RNA .......................................................................................97 Schulz Lab – Max Planck Research Group Regulatory Networks in Stem Cells .............................101 Yaspo Lab – Research Group Gene Regulation & Systems Biology of Cancer ................................106 Zi Lab – Research Group Cell Signaling Dynamics ............................................................................... 111 Lehrach Lab – Emeritus Group Vertebrate Genomics .........................................................................116 Reinert Lab – Max Planck Fellow Group Efficient Algorithms for Omics Data ..............................120 Scientific services ....................................................................................................................................124 Animal facility ...........................................................................................................................................124 Transgenic Unit ..........................................................................................................................................127 Sequencing Core Facility..........................................................................................................................130 Mass Spectrometry Facility .....................................................................................................................134 Service Group Microscopy and Cryo-Electron Microscopy ...............................................................140 IT Group ......................................................................................................................................................146 Library & Scientific Information ..............................................................................................................149 Research Support .....................................................................................................................................151 Administration ...........................................................................................................................................151 Technical Management & Workshops ...................................................................................................154 7 Scientific Advisory Board Board of Directors Board of Trustees 8 Bernhard G. Herrmann, Alexander Meissner, Martin Vingron, N.N. Administration, Research Support Scientific Coordination, Public Relations Managing Director Christoph Krukenkamp
Load more

Recommended publications
  • STRING V10: Protein–Protein Interaction Networks, Integrated
    Published online 28 October 2014 Nucleic Acids Research, 2015, Vol. 43, Database issue D447–D452 doi: 10.1093/nar/gku1003 STRING v10: protein–protein interaction networks, integrated over the tree of life Damian Szklarczyk1, Andrea Franceschini1, Stefan Wyder1, Kristoffer Forslund2, Davide Heller1, Jaime Huerta-Cepas2, Milan Simonovic1, Alexander Roth1, Alberto Santos3, Kalliopi P. Tsafou3, Michael Kuhn4,5, Peer Bork2,*, Lars J. Jensen3,* and Christian von Mering1,* 1Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, 2European Molecular Biology Laboratory, 69117 Heidelberg, Germany, 3Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark, 4Biotechnology Center, Technische Universitat¨ Dresden, 01062 Dresden, Germany and 5Max Planck Institute of Molecular Cell Biology and Downloaded from Genetics, 01062 Dresden, Germany Received September 15, 2014; Accepted October 07, 2014 ABSTRACT INTRODUCTION http://nar.oxfordjournals.org/ The many functional partnerships and interactions For a full description of a protein’s function, knowledge that occur between proteins are at the core of cel- about its specific interaction partners is an important pre- lular processing and their systematic characteriza- requisite. The concept of protein ‘function’ is somewhat hi- tion helps to provide context in molecular systems erarchical (1–4), and at all levels in this hierarchy, interac- biology. However, known and predicted interactions tions between proteins help to describe and narrow down a protein’s function: its three-dimensional structure may are scattered over multiple resources, and the avail- become meaningful only in the context of a larger pro- able data exhibit notable differences in terms of qual- tein assembly, its molecular actions may be regulated by at University of Aarhus on January 22, 2015 ity and completeness.
    [Show full text]
  • Ontology-Based Methods for Analyzing Life Science Data
    Habilitation a` Diriger des Recherches pr´esent´ee par Olivier Dameron Ontology-based methods for analyzing life science data Soutenue publiquement le 11 janvier 2016 devant le jury compos´ede Anita Burgun Professeur, Universit´eRen´eDescartes Paris Examinatrice Marie-Dominique Devignes Charg´eede recherches CNRS, LORIA Nancy Examinatrice Michel Dumontier Associate professor, Stanford University USA Rapporteur Christine Froidevaux Professeur, Universit´eParis Sud Rapporteure Fabien Gandon Directeur de recherches, Inria Sophia-Antipolis Rapporteur Anne Siegel Directrice de recherches CNRS, IRISA Rennes Examinatrice Alexandre Termier Professeur, Universit´ede Rennes 1 Examinateur 2 Contents 1 Introduction 9 1.1 Context ......................................... 10 1.2 Challenges . 11 1.3 Summary of the contributions . 14 1.4 Organization of the manuscript . 18 2 Reasoning based on hierarchies 21 2.1 Principle......................................... 21 2.1.1 RDF for describing data . 21 2.1.2 RDFS for describing types . 24 2.1.3 RDFS entailments . 26 2.1.4 Typical uses of RDFS entailments in life science . 26 2.1.5 Synthesis . 30 2.2 Case study: integrating diseases and pathways . 31 2.2.1 Context . 31 2.2.2 Objective . 32 2.2.3 Linking pathways and diseases using GO, KO and SNOMED-CT . 32 2.2.4 Querying associated diseases and pathways . 33 2.3 Methodology: Web services composition . 39 2.3.1 Context . 39 2.3.2 Objective . 40 2.3.3 Semantic compatibility of services parameters . 40 2.3.4 Algorithm for pairing services parameters . 40 2.4 Application: ontology-based query expansion with GO2PUB . 43 2.4.1 Context . 43 2.4.2 Objective .
    [Show full text]
  • Research Report 2006 Max Planck Institute for Molecular Genetics, Berlin Imprint | Research Report 2006
    MAX PLANCK INSTITUTE FOR MOLECULAR GENETICS Research Report 2006 Max Planck Institute for Molecular Genetics, Berlin Imprint | Research Report 2006 Published by the Max Planck Institute for Molecular Genetics (MPIMG), Berlin, Germany, August 2006 Editorial Board Bernhard Herrmann, Hans Lehrach, H.-Hilger Ropers, Martin Vingron Coordination Claudia Falter, Ingrid Stark Design & Production UNICOM Werbeagentur GmbH, Berlin Number of copies: 1,500 Photos Katrin Ullrich, MPIMG; David Ausserhofer Contact Max Planck Institute for Molecular Genetics Ihnestr. 63–73 14195 Berlin, Germany Phone: +49 (0)30-8413 - 0 Fax: +49 (0)30-8413 - 1207 Email: [email protected] For further information about the MPIMG please see our website: www.molgen.mpg.de MPI for Molecular Genetics Research Report 2006 Table of Contents The Max Planck Institute for Molecular Genetics . 4 • Organisational Structure. 4 • MPIMG – Mission, Development of the Institute, Research Concept. .5 Department of Developmental Genetics (Bernhard Herrmann) . 7 • Transmission ratio distortion (Hermann Bauer) . .11 • Signal Transduction in Embryogenesis and Tumor Progression (Markus Morkel). 14 • Development of Endodermal Organs (Heiner Schrewe) . 16 • Gene Expression and 3D-Reconstruction (Ralf Spörle). 18 • Somitogenesis (Lars Wittler). 21 Department of Vertebrate Genomics (Hans Lehrach) . 25 • Molecular Embryology and Aging (James Adjaye). .31 • Protein Expression and Protein Structure (Konrad Büssow). .34 • Mass Spectrometry (Johan Gobom). 37 • Bioinformatics (Ralf Herwig). .40 • Comparative and Functional Genomics (Heinz Himmelbauer). 44 • Genetic Variation (Margret Hoehe). 48 • Cell Arrays/Oligofingerprinting (Michal Janitz). .52 • Kinetic Modeling (Edda Klipp) . .56 • In Vitro Ligand Screening (Zoltán Konthur). .60 • Neurodegenerative Disorders (Sylvia Krobitsch). .64 • Protein Complexes & Cell Organelle Assembly/ USN (Bodo Lange/Thorsten Mielke). .67 • Automation & Technology Development (Hans Lehrach).
    [Show full text]
  • Reactome Pathway Analysis
    Fabregat et al. BMC Bioinformatics (2017) 18:142 DOI 10.1186/s12859-017-1559-2 SOFTWARE Open Access Reactome pathway analysis: a high- performance in-memory approach Antonio Fabregat1,2, Konstantinos Sidiropoulos1, Guilherme Viteri1, Oscar Forner1, Pablo Marin-Garcia3,4, Vicente Arnau5,6, Peter D’Eustachio7, Lincoln Stein8,9 and Henning Hermjakob1,10* Abstract Background: Reactome aims to provide bioinformatics tools for visualisation, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modelling, systems biology and education. Pathway analysis methods have a broad range of applications in physiological and biomedical research; one of the main problems, from the analysis methods performance point of view, is the constantly increasing size of the data samples. Results: Here, we present a new high-performance in-memory implementation of the well-established over- representation analysis method. To achieve the target, the over-representation analysis method is divided in four different steps and, for each of them, specific data structures are used to improve performance and minimise the memory footprint. The first step, finding out whether an identifier in the user’s sample corresponds to an entity in Reactome, is addressed using a radix tree as a lookup table. The second step, modelling the proteins, chemicals, their orthologous in other species and their composition in complexes and sets, is addressed with a graph. The third and fourth steps, that aggregate the results and calculate the statistics, are solved with a double-linked tree. Conclusion: Through the use of highly optimised, in-memory data structures and algorithms, Reactome has achieved a stable, high performance pathway analysis service, enabling the analysis of genome-wide datasets within seconds, allowing interactive exploration and analysis of high throughput data.
    [Show full text]
  • Next Generation Machine Learning Prediction of Protein Cellular Sorting
    TECHNISCHE UNIVERSITÄT MÜNCHEN Lehrstuhl für Bioinformatik Next Generation Machine Learning Prediction of Protein Cellular Sorting Tatyana Goldberg Vollständiger Abdruck der von der Fakultät für Informatik der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften genehmigten Dissertation. Vorsitzender: Univ.-Prof. Dr. Daniel Cremers Prüfer der Dissertation: 1. Univ.-Prof. Dr. Burkhard Rost 2. Prof. Dr. Yana Bromberg, The State University of New Jersey/USA 3. Univ.-Prof. Dr. Iris Antes Die Dissertation wurde am 15.12.2015 bei der Technischen Universität München eingereicht und durch die Fakultät für Informatik am 22.03.2016 angenommen. ii Table of Contents Abstract ............................................................................................................................................. v Zusammenfassung ........................................................................................................................... vii Acknowledgments ............................................................................................................................ ix List of publications ........................................................................................................................... xi List of Figures and Tables ............................................................................................................... xiii 1. Introduction ...............................................................................................................................
    [Show full text]
  • First Analysis Steps
    FirstFirst analysisanalysis stepssteps o quality control and optimization o calibration and error modeling o data transformations Wolfgang Huber Dep. of Molecular Genome Analysis (A. Poustka) DKFZ Heidelberg Acknowledgements Anja von Heydebreck Günther Sawitzki Holger Sültmann, Klaus Steiner, Markus Vogt, Jörg Schneider, Frank Bergmann, Florian Haller, Katharina Finis, Stephanie Süß, Anke Schroth, Friederike Wilmer, Judith Boer, Martin Vingron, Annemarie Poustka Sandrine Dudoit, Robert Gentleman, Rafael Irizarry and Yee Hwa Yang: Bioconductor short course, summer 2002 and many others a microarray slide Slide: 25x75 mm Spot-to-spot: ca. 150-350 µm 4 x 4 or 8x4 sectors 17...38 rows and columns per sector ca. 4600…46000 probes/array sector: corresponds to one print-tip Terminology sample: RNA (cDNA) hybridized to the array, aka target, mobile substrate. probe: DNA spotted on the array, aka spot, immobile substrate. sector: rectangular matrix of spots printed using the same print-tip (or pin), aka print-tip-group plate: set of 384 (768) spots printed with DNA from the same microtitre plate of clones slide, array channel: data from one color (Cy3 = cyanine 3 = green, Cy5 = cyanine 5 = red). batch: collection of microarrays with the same probe layout. Raw data scanner signal resolution: 5 or 10 mm spatial, 16 bit (65536) dynamical per channel ca. 30-50 pixels per probe (60 µm spot size) 40 MB per array Raw data scanner signal resolution: 5 or 10 mm spatial, 16 bit (65536) dynamical per channel ca. 30-50 pixels per probe (60 µm spot size) 40 MB per array Image Analysis Raw data scanner signal resolution: 5 or 10 mm spatial, 16 bit (65536) dynamical per channel ca.
    [Show full text]
  • Probabilistic Models for Gene Silencing Data
    Probabilistic Models for Gene Silencing Data Florian Markowetz Dezember 2005 Dissertation zur Erlangung des Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) am Fachbereich Mathematik und Informatik der Freien Universit¨atBerlin 1. Referent: Prof. Dr. Martin Vingron 2. Referent: Prof. Dr. Klaus-Robert M¨uller Tag der Promotion: 26. April 2006 Preface Acknowledgements This work was carried out in the Computational Diagnostics group of the Department of Computational Molecular Biology at the Max Planck Institute for Molecular Genetics in Berlin. I thank all past and present colleagues for the good working atmosphere and the scientific—and sometimes maybe not so scientific—discussions. Especially, I am grateful to my supervisor Rainer Spang for suggesting the topic, his scientific support, and the opportunity to write this thesis under his guidance. I thank Michael Boutros for providing the expression data and for introducing me to the world of RNAi when I visited his lab at the DKFZ in Heidelberg. I thank Anja von Heydebreck, J¨org Schultz, and Martin Vingron for their advice and counsel as members of my PhD commitee. During the time I worked on this thesis, I enjoyed fruitful discussions with many people. In particular, I gratefully acknowledge Jacques Bloch, Steffen Grossmann, Achim Tresch, and Chen-Hsiang Yeang for their contributions. Special thanks go to Viola Gesellchen, Britta Koch, Stefanie Scheid, Stefan Bentink, Stefan Haas, Dennis Kostka, and Stefan R¨opcke, who read drafts of this thesis and greatly improved it by their comments. Publications Parts of this thesis have been published before. Chapter 2 grew out of lectures I gave in 2005 at the Instiute for Theoretical Physics and Mathematics (IPM) in Tehran, Iran, and at the German Conference on Bioinformatics (GCB), Hamburg, Germany.
    [Show full text]
  • 15Th International Symposium on Bioinformatics Research and Applications
    ISBRA 2019 15th International Symposium on Bioinformatics Research and Applications June 3–6, 2019 Technical University of Catalonia Barcelona, Spain http://alan.cs.gsu.edu/isbra19/ About the Technical University of Catalonia The Technical University of Catalonia (UPC) is a public institution of Higher Education and Research, specialized in the areas of Architecture, Engineering, Science and Technology. The UPC has a wide spread presence in Catalonia, with nine campuses located in Barcelona and nearby towns. The campuses are accessible, well connected by public transport and equipped with the necessary facilities and services to contribute to learning, research and university life. Founded in 1968 by grouping together existing state technical schools of Architecture and Engineering in Barcelona, which date back from the mid-19th century, it gained university status in 1971. The UPC today has a student population of over 30,000, with more than 3,000 teaching and research staff and about 2,000 administrative and service staff. There are 64 bachelor’s degrees, 68 master’s degrees, and 46 doctoral programs offered by 20 schools. Further, there are 50 international double-degree agreements with 30 universities, and about 3,000 students in international mobility programs. Research teams generate e60 million in annual funding, with a total budget of about e300 million. About the Department of Computer Science The Department of Computer Science was created in 1987 and is one of the largest depart- ments at the UPC. It has about 90 full-time faculty and 50 Ph.D. students. The Department is responsible for teaching and research in several disciplines related to the foundations of computing and their applications.
    [Show full text]
  • Research Report 2009 Max Planck Institute for Molecular Genetics, Berlin Imprint | Research Report 2009
    Research Report 2009 Max Planck Institute for Molecular Genetics, Berlin Imprint | Research Report 2009 Published by the Max Planck Institute for Molecular Genetics (MPIMG), Berlin, Germany, December 2009 Editorial Board: B.G. Herrmann, H. Lehrach, H.-H. Ropers, M. Vingron Conception & coordination: Patricia Marquardt Photography: Katrin Ullrich, MPIMG; David Ausserhofer Scientific Illustrations: MPIMG Production: Thomas Didier, Meta Data Contact: Max Planck Institute for Molecular Genetics Ihnestr. 63 – 73 14195 Berlin Germany Phone: +49 (0)30 8413-0 Fax: +49 (0)30 8413-1207 Email: [email protected] For further information about the MPIMG, please visit http://www.molgen.mpg.de MPI for Molecular Genetics Research Report 2009 Research Report 2009 1 Max Planck Institute for Molecular Genetics Berlin, December 2009 The Max Planck Institute for Molecular Genetics 2 MPI for Molecular Genetics Research Report 2009 Table of contents Organisational structure . 6 The Max Planck Institute for Molecular Genetics . 7 Mission . 7 Development of the Institute. 7 Research Concept . 8 Department of Developmental Genetics (Bernhard Herrmann) . 9 Transmission ratio distortion (H. Bauer) . 13 Regulatory Networks of Mesoderm Formation & Somitogenesis (B. Herrmann) . 17 Signal Transduction in Embryogenesis and Tumour Progression (M. Morkel) . 22 Organogenesis (H. Schrewe) . 26 General information about the whole Department . 29 Department of Vertebrate Genomics (Hans Lehrach) . 33 Molecular Embryology and Aging (J. Adjaye) . 40 Neuropsychiatric Genetics (L. Bertram) . 46 Automation (A. Dahl, W. Nietfeld, H. Seitz) . 49 Nucleic Acid-based Technologies (J. Glökler) . 55 Bioinformatics (R. Herwig) . 60 Comparative and Functional Genomics (H. Himmelbauer) . .65 Genetic Variation, Haplotypes & Genetics of Complex Diseases (M. Hoehe) . 69 3 in vitro Ligand Screening (Z.
    [Show full text]
  • Network-Based Association of Hypoxia-Responsive Genes with Cardiovascular Diseases
    Network-based association of hypoxia-responsive genes with cardiovascular diseases Rui-Sheng Wang, William M Oldham and Joseph Loscalzo Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA E-mail: [email protected] Received 19 May 2014, revised 21 July 2014 Accepted for publication 11 August 2014 Published 24 October 2014 New Journal of Physics 16 (2014) 105014 doi:10.1088/1367-2630/16/10/105014 Abstract Molecular oxygen is indispensable for cellular viability and function. Hypoxia is a stress condition in which oxygen demand exceeds supply. Low cellular oxygen content induces a number of molecular changes to activate regulatory pathways responsible for increasing the oxygen supply and optimizing cellular metabolism under limited oxygen conditions. Hypoxia plays critical roles in the pathobiol- ogy of many diseases, such as cancer, heart failure, myocardial ischemia, stroke, and chronic lung diseases. Although the complicated associations between hypoxia and cardiovascular (and cerebrovascular) diseases (CVD) have been recognized for some time, there are few studies that investigate their biological link from a systems biology perspective. In this study, we integrate hypoxia genes, CVD genes, and the human protein interactome in order to explore the relationship between hypoxia and cardiovascular diseases at a systems level. We show that hypoxia genes are much closer to CVD genes in the human protein interactome than that expected by chance. We also find that hypoxia genes play significant bridging roles in connecting different cardiovascular diseases. We construct a hypoxia-CVD bipartite network and find several interesting hypoxia- CVD modules with significant gene ontology similarity. Finally, we show that hypoxia genes tend to have more CVD interactors in the human interactome than in random networks of matching topology.
    [Show full text]
  • Biomolecule and Bioentity Interaction Databases in Systems Biology: a Comprehensive Review
    biomolecules Review Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review Fotis A. Baltoumas 1,* , Sofia Zafeiropoulou 1, Evangelos Karatzas 1 , Mikaela Koutrouli 1,2, Foteini Thanati 1, Kleanthi Voutsadaki 1 , Maria Gkonta 1, Joana Hotova 1, Ioannis Kasionis 1, Pantelis Hatzis 1,3 and Georgios A. Pavlopoulos 1,3,* 1 Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; zafeiropoulou@fleming.gr (S.Z.); karatzas@fleming.gr (E.K.); [email protected] (M.K.); [email protected] (F.T.); voutsadaki@fleming.gr (K.V.); [email protected] (M.G.); hotova@fleming.gr (J.H.); [email protected] (I.K.); hatzis@fleming.gr (P.H.) 2 Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark 3 Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece * Correspondence: baltoumas@fleming.gr (F.A.B.); pavlopoulos@fleming.gr (G.A.P.); Tel.: +30-210-965-6310 (G.A.P.) Abstract: Technological advances in high-throughput techniques have resulted in tremendous growth Citation: Baltoumas, F.A.; of complex biological datasets providing evidence regarding various biomolecular interactions. Zafeiropoulou, S.; Karatzas, E.; To cope with this data flood, computational approaches, web services, and databases have been Koutrouli, M.; Thanati, F.; Voutsadaki, implemented to deal with issues such as data integration, visualization, exploration, organization, K.; Gkonta, M.; Hotova, J.; Kasionis, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more I.; Hatzis, P.; et al. Biomolecule and and more difficult for an end user to know what the scope and focus of each repository is and how Bioentity Interaction Databases in redundant the information between them is.
    [Show full text]
  • The 2021 Update of the EPA's Adverse Outcome Pathway Database
    www.nature.com/scientificdata OPEN The 2021 update of the EPA’s Data DescRiptor adverse outcome pathway database Holly M. Mortensen1 ✉ , Jonathan Senn2, Trevor Levey2,3, Phillip Langley2,4 & Antony J. Williams5 The EPA developed the Adverse Outcome Pathway Database (AOP-DB) to better characterize adverse outcomes of toxicological interest that are relevant to human health and the environment. Here we present the most recent version of the EPA Adverse Outcome Pathway Database (AOP-DB), version 2. AOP-DB v.2 introduces several substantial updates, which include automated data pulls from the AOP-Wiki 2.0, the integration of tissue-gene network data, and human AOP-gene data by population, semantic mapping and SPARQL endpoint creation, in addition to the presentation of the frst publicly available AOP-DB web user interface. Potential users of the data may investigate specifc molecular targets of an AOP, the relation of those gene/protein targets to other AOPs, cross-species, pathway, or disease-AOP relationships, or frequencies of AOP-related functional variants in particular populations, for example. Version updates described herein help inform new testable hypotheses about the etiology and mechanisms underlying adverse outcomes of environmental and toxicological concern. Background & Summary Tere is a need for approaches to understand the biological mechanism of adverse outcomes and human varia- bility in response to environmental chemical exposure. A recent legislation, the Frank R. Lautenberg Chemical Safety for the twenty-frst Century Act of 20161, requires the US Environmental Protection Agency to evaluate new and existing toxic chemicals with explicit consideration of susceptible populations of all types (life stage, exposure, genetic, etc.).
    [Show full text]