DOCSLIB.ORG

CV Burkhard Rost TUM Technical University of Munich Affiliation: Columbia University

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
CV Burkhard Rost TUM Technical University of Munich Affiliation: Columbia University Burkhard Rost CV BURKHARD ROST TUM Informatics/Bioinformatics i12 Boltzmannstrasse 3 (Rm 01.09.052) 85748 Garching/München, Germany & Dept. Biochemistry & Molecular Biophysics Columbia University, New York, USA Email [email protected] Tel +49-89-289-17-811 Web www.rostlab.org Fax +49-89-289-19-414 Photo: © Eckert & Heddergott, TUM Document: CV Burkhard Rost TUM Technical University of Munich Affiliation: Columbia University Highlights: • 208 invited talks in 37 countries (incl. TedX) • 272 publications (212 peer-review, 183 first/last author) • 2019/03: >38,500 citations, h-index=91 (Google) • PredictProtein 1st Internet server in mol. biol. (since 1992) • 8 years ISCB President (International Society for Computational Biology) • 207 trained (27% female, 55% foreigners from 37 nations on 6 continents) • co-published with over 984 scientists Brief narrative: Burkhard Rost obtained his doctoral degree (Dr. rer. nat.) from the Univ. of Heidelberg (Germany) in the field of theoretical physics. He began his research working on the thermo-dynamical properties of spin glasses and brain-like artificial neural networks. A short project on peace/arms control research sketched a simple, non-intrusive sensor networks to monitor aircraft (1988-1990). He entered the field of molecular biology at the European Molecular Biology Laboratory (EMBL, Heidelberg, Germany, 1990-1995), spent a year at the European Bioinformatics Institute (EBI, Hinxton, Cambridgshire, England, 1995), returned to the EMBL (1996-1998), joined the company LION Biosciences for a brief interim (1998), became faculty in the Medical School of Columbia University in 1998, and joined the TUM Munich to become an Alexander von Humboldt professor in 2009. From 1998-2010, he has been leading a research group of about 10 PhD students in the Dept. of Biochemistry and Molecular Biophysics in the Medical School of Columbia University. Since 2009, he has begun also to also build up a group at the TUM in Germany. Including graduate students, the group in Munich has been exceeding the average of 20 for the last 7 years. Issues related to gender, diversity and equality of opportunity have been important aspects of these developments, as has been the incentive to benefit from the appeal of relevant science to energize young minds. One particular detour aiming at the improvement of teaching was a JavaScript seminar using machine learning and data mining to predict future events in the book/movie series Gamer Of Thrones in spring 2016 that made it to over 600 published media potentially reaching 1.2 billion readers. In 1992, Dr. Rost developed the first Internet server for protein structure prediction (PredictProtein) that began at the EMBL, moved to Columbia University, and on to the TUM in Munich. In fact, in terms of a server that responds to queries more complicated than a lookup, PredictProtein is the oldest such service in molecular biology, having gone online before or at the same time as other database lookup services such as ExPasy, SWISS-PROT, or BLOCKS. Since, the group has contributed many highly used and original methods for the prediction of aspects of protein function and structure. His major contribution has been the combination of machine learning and evolutionary information. His academic research goal focuses on the development of top tools that can be applied in the context of analyzing entirely sequence organisms. Recently, the lab has been focusing on analyzing the effects of sequence variation and their implications for precision medicine and personalized health. Details: http://www.rostlab.org. status Jan 19, 2016 1 Burkhard Rost Tabulated CV Tabulated Curriculum Vitae Date prepared: Mar 22, 2019 Name: Burkhard Rost Gender: Male Marital Status: Married with one daughter (born Dec. 2002) Citizenship: German Birthplace: Northeim, Lower-Saxony, Germany Current position: Professor, Unit chair Address TUM, Department for Computer Sciences (Informatik) Unit for Bioinformatics & Computational Biology (i12) Boltzmannstrasse 3, 85748 Garching/Munich, Germany tel.: +49-89-289-17-811 email: [email protected] Education 1971-1980 High school, Herzberg, Lower-Saxony, Germany 1980-1982 Obligatory service in German air force 1982-1985 Study of physics at Justus-Liebig-University Gießen, Hessia, Germany 10/1984 Bachelor in physics 1985-1988 Study of physics at Ruprecht-Karls-University Heidelberg, Germany 1985-1988 Study of philosophy, history and psychology at Heidelberg University 1986-1988 Master thesis at the Institute of Theoretical Physics, Heidelberg Univ. Topic: 'Learning algorithms for spin-glass-like neural networks' Sponsor: Prof. Dr. Heinz Horner, Heidelberg 11/1988 Bachelor in philosophy and history, Heidelberg University 12/1988 Master in physics, Heidelberg University 12/88-06/90 Grant of the 'Stiftung Volkswagenwerk' (VW Foundation), Inst. Theor. Physics, Heidelberg Topic: 'Theoretical analysis of the possibilities of seismic and acoustical sensor networks to verify arms control treaties for aircraft' 07-10/89 Research project in the USA (Princeton, Washington DC, MIT) 1990-1993 Completing studies in physics, Heidelberg Univ. and EMBL, Germany 07/1993 Writing PhD thesis Topic: 'Neural networks and evolution - prediction of protein secondary structure' Sponsor: none à 07/1994 PhD in physics: Viva voce for Dr. rer. nat. (Doctor rerum naturarum) at Institute for Theoretical Physics, Ruprecht-Karl University Heidelberg, Germany Professional Positions 1986-1988 Assistant at Institute for Theoretical Physics, Heidelberg Univ., Germany 12/88-06/90 Research fellow at Inst. for Theoretical Physics, Heidelberg Univ. 07-10/1989 Visitor at Institutes in the USA (Princeton, U. of Concerned Scientists, MIT) 07/90- 1992 Visitor at EMBL Heidelberg, Germany 07/93-12/94 Research fellow at EMBL Heidelberg, Germany 01/95-12/95 Research fellow at EBI Hinxton, Cambridge, England 01/96- 04/98 Research fellow at EMBL Heidelberg, Germany 05/98-11/98 Researcher at LION Biosciences Heidelberg, Germany à Over 600 years, Heidelberg Universtiy has allowed scholars to present doctoral theses on any research subject without sponsor. Apparently, I was the first who exercised this right over, at least, the last 60 years. 2 Burkhard Rost Tabulated CV 12/98- 05/00 Assistant Professor at Department of Biochemistry and Molecular Biophysics, Columbia University, New York, USA 07/2000-2010 Associate Professor at Dept. of Biochemistry & Mol. Biophys., Columbia 2004-2010 Affiliated Faculty, Dept. of Medical Bioinformatics, Columbia University 2002-2010 Faculty Center of Computational Biology and Bioinformatics (C2B2) 07/2005-2010 Tenure in Dept. of Biochemistry and Mol. Biophysics at Columbia University 2006-2010 Associated Faculty in the Department of Pharmacology at Columbia Univ. 2006-2010 Associated Faculty in the Irving Center of Cancer Research at Columbia since 06/2009 Alexander von Humboldt Professor in Department for Computer Sciences at the TUM Munich, Germany since 06/2009 Fellow at the Institute for Advanced Studies, IAS-TUM, Germany since 02/2012 Adjunct Prof., Inst. Food & Plant Sciences WZW - Weihenstephan-Freising, Germany Professional Organizations and Societies 1995-now Program Committee of ISMB (Intelligent Systems for Molecular Biology) 1996-now Member of ISCB (International Society for Computational Biology) 2002-now Board of Directors ISCB (International Society for Computational Biology) 2002-now Member of NYAS (New York Academy of Sciences) 2005-2006 Vice President ISCB (International Soc. for Computational Biology) 2006 President Elect of ISCB (International Society for Computational Biology) 2007-2014 President of the ISCB (International Society for Computational Biology) Professional Experiences 1992-now Over 500 referee reports for peer-reviewed journals (including Nature, Science, PNAS, Cell, EMBO J) 1995-now Over 50 reviews of grants from individuals and institutes (countries: Austria, Canada, Denmark, England, Germany, Israel, Italy, Norway, Netherlands, Singapore, Spain, Sweden, Switzerland, USA); ad hoc panels for NIH and NSF, including role as chairman. 2001-now Ad-hoc panels for grants from the NIH, NSF and the European Community 2005-2009 Associate Editor of PLoS Computational Biology 2006-now Associate Editor of Bioinformatics 2006-now Associate Editor of Proteins: Structure, Function, and Bioinformatics 2009-now Deputy Editor of PLoS Computational Biology 2005-now Editorial Board of FASEB (representative from ISCB) 2006-now Editorial Board of Journal of Structural and Functional Genomics 2007-now Editorial Board of Bioinformatics and Biology Insights 2001-2006 Editorial Board of Journal of Medical Informatics 2002, 2005 Editor for ISMB (Intelligent Systems for Molecular Biology) proceedings 2004-2006 Editorial Board of Proteins: Structure, Function, and Bioinformatics 2005-2006 Editorial Board of Bioinformatics 2002-2007 Organization of New York Computational Biology Society in the New York Academy of Sciences 1999-2005 SAB for company LION Biosciences, Heidelberg/Cambridge UK 2004-now Co-founder, CEO of BioSof, Delaware, USA Invited Talks (208 invited talks in 37 countries) 1988 (1) 12/1988: London, England: Workshop on Arms Control 1989 (2) 09/1989: Washington, DC, USA: Union of Concerned Scientists; 10/1989: Princeton, USA: Institute for Advanced Studies 1990 (3) 05/1990: Prague, CSFR: Workshop on Arms Control; 09/1990: Vienna, Austria: Conference on Arms Control; 10/1990: Mosbach, Germany: Conference on Arms Control 1992 (3) 06/1992: Elba, Italy: Conference
Load more

Recommended publications
  • Improving the Prediction of Transcription Factor Binding Sites To
    Improving the prediction of transcription factor binding sites to aid the interpretation of non-coding single nucleotide variants. Narayan Jayaram Research Department of Structural and Molecular Biology University College London A thesis submitted to University College London for the degree of Doctor of Philosophy 1 Declaration I, Narayan Jayaram confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Narayan Jayaram 2 Abstract Single nucleotide variants (SNVs) that occur in transcription factor binding sites (TFBSs) can disrupt the binding of transcription factors and alter gene expression which can cause inherited diseases and act as driver SNVs in cancer. The identification of SNVs in TFBSs has historically been challenging given the limited number of experimentally characterised TFBSs. The recent ENCODE project has resulted in the availability of ChIP-Seq data that provides genome wide sets of regions bound by transcription factors. These data have the potential to improve the identification of SNVs in TFBSs. However, as the ChIP-Seq data identify a broader range of DNA in which a transcription factor binds, computational prediction is required to identify the precise TFBS. Prediction of TFBSs involves scanning a DNA sequence with a Position Weight Matrix (PWM) using a pattern matching tool. This thesis focusses on the prediction of TFBSs by: (a) evaluating a set of locally-installable pattern-matching tools and identifying the best performing tool (FIMO), (b) using the ENCODE ChIP-Seq data to evaluate a set of de novo motif discovery tools that are used to derive PWMs which can handle large volumes of data, (c) identifying the best performing tool (rGADEM), (d) using rGADEM to generate a set of PWMs from the ENCODE ChIP-Seq data and (e) by finally checking that the selection of the best pattern matching tool is not unduly influenced by the choice of PWMs.
    [Show full text]
  • Applied Category Theory for Genomics – an Initiative
    Applied Category Theory for Genomics { An Initiative Yanying Wu1,2 1Centre for Neural Circuits and Behaviour, University of Oxford, UK 2Department of Physiology, Anatomy and Genetics, University of Oxford, UK 06 Sept, 2020 Abstract The ultimate secret of all lives on earth is hidden in their genomes { a totality of DNA sequences. We currently know the whole genome sequence of many organisms, while our understanding of the genome architecture on a systematic level remains rudimentary. Applied category theory opens a promising way to integrate the humongous amount of heterogeneous informations in genomics, to advance our knowledge regarding genome organization, and to provide us with a deep and holistic view of our own genomes. In this work we explain why applied category theory carries such a hope, and we move on to show how it could actually do so, albeit in baby steps. The manuscript intends to be readable to both mathematicians and biologists, therefore no prior knowledge is required from either side. arXiv:2009.02822v1 [q-bio.GN] 6 Sep 2020 1 Introduction DNA, the genetic material of all living beings on this planet, holds the secret of life. The complete set of DNA sequences in an organism constitutes its genome { the blueprint and instruction manual of that organism, be it a human or fly [1]. Therefore, genomics, which studies the contents and meaning of genomes, has been standing in the central stage of scientific research since its birth. The twentieth century witnessed three milestones of genomics research [1]. It began with the discovery of Mendel's laws of inheritance [2], sparked a climax in the middle with the reveal of DNA double helix structure [3], and ended with the accomplishment of a first draft of complete human genome sequences [4].
    [Show full text]
  • A Community Proposal to Integrate Structural
    F1000Research 2020, 9(ELIXIR):278 Last updated: 11 JUN 2020 OPINION ARTICLE A community proposal to integrate structural bioinformatics activities in ELIXIR (3D-Bioinfo Community) [version 1; peer review: 1 approved, 3 approved with reservations] Christine Orengo1, Sameer Velankar2, Shoshana Wodak3, Vincent Zoete4, Alexandre M.J.J. Bonvin 5, Arne Elofsson 6, K. Anton Feenstra 7, Dietland L. Gerloff8, Thomas Hamelryck9, John M. Hancock 10, Manuela Helmer-Citterich11, Adam Hospital12, Modesto Orozco12, Anastassis Perrakis 13, Matthias Rarey14, Claudio Soares15, Joel L. Sussman16, Janet M. Thornton17, Pierre Tuffery 18, Gabor Tusnady19, Rikkert Wierenga20, Tiina Salminen21, Bohdan Schneider 22 1Structural and Molecular Biology Department, University College, London, UK 2Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, CB10 1SD, UK 3VIB-VUB Center for Structural Biology, Brussels, Belgium 4Department of Oncology, Lausanne University, Swiss Institute of Bioinformatics, Lausanne, Switzerland 5Bijvoet Center, Faculty of Science – Chemistry, Utrecht University, Utrecht, 3584CH, The Netherlands 6Science for Life Laboratory, Stockholm University, Solna, S-17121, Sweden 7Dept. Computer Science, Center for Integrative Bioinformatics VU (IBIVU), Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands 8Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4367, Luxembourg 9Bioinformatics center, Department of Biology, University of Copenhagen, Copenhagen, DK-2200,
    [Show full text]
  • A Bivalent Chromatin Structure Marks Key Developmental Genes in Embryonic Stem Cells
    A Bivalent Chromatin Structure Marks Key Developmental Genes in Embryonic Stem Cells Bradley E. Bernstein,1,2,3,* Tarjei S. Mikkelsen,3,4 Xiaohui Xie,3 Michael Kamal,3 Dana J. Huebert,1 James Cuff,3 Ben Fry,3 Alex Meissner,5 Marius Wernig,5 Kathrin Plath,5 Rudolf Jaenisch,5 Alexandre Wagschal,6 Robert Feil,6 Stuart L. Schreiber,3,7 and Eric S. Lander3,5 1 Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA 02129, USA 2 Department of Pathology, Harvard Medical School, Boston, MA 02115, USA 3 Broad Institute of Harvard and MIT, Cambridge, MA 02139, USA 4 Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA 5 Whitehead Institute for Biomedical Research, MIT, Cambridge, MA 02139, USA 6 Institute of Molecular Genetics, CNRS UMR-5535 and University of Montpellier-II, Montpellier, France 7 Howard Hughes Medical Institute at the Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA *Contact: [email protected] DOI 10.1016/j.cell.2006.02.041 SUMMARY which in turn modulate chromatin structure (Jenuwein and Allis, 2001; Margueron et al., 2005). The core histones The most highly conserved noncoding ele- H2A, H2B, H3, and H4 are subject to dozens of different ments (HCNEs) in mammalian genomes cluster modifications, including acetylation, methylation, and within regions enriched for genes encoding de- phosphorylation. Histone H3 lysine 4 (Lys4) and lysine velopmentally important transcription factors 27 (Lys27) methylation are of particular interest as these (TFs). This suggests that HCNE-rich regions modifications are catalyzed, respectively, by trithorax- may contain key regulatory controls involved and Polycomb-group proteins, which mediate mitotic in- heritance of lineage-specific gene expression programs in development.
    [Show full text]
  • Semantic Web
    SEMANTIC WEB: REVOLUTIONIZING KNOWLEDGE DISCOVERY IN THE LIFE SCIENCES SEMANTIC WEB: REVOLUTIONIZING KNOWLEDGE DISCOVERY IN THE LIFE SCIENCES Edited by Christopher J. O. Baker1 and Kei-Hoi Cheung2 1Knowledge Discovery Department, Institute for Infocomm Research, Singapore, Singapore; 2Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, USA Kluwer Academic Publishers Boston/Dordrecht/London Contents PART I: Database and Literature Integration Semantic web approach to database integration in the life sciences KEI-HOI CHEUNG, ANDREW K. SMITH, KEVIN Y. L. YIP, CHRISTOPHER J. O. BAKER AND MARK B. GERSTEIN Querying Semantic Web Contents: A case study LOIC ROYER, BENEDIKT LINSE, THOMAS WÄCHTER, TIM FURCH, FRANCOIS BRY, AND MICHAEL SCHROEDER Knowledge Acquisition from the Biomedical Literature LYNETTE HIRSCHMAN, WILLIAM HAYES AND ALFONSO VALENCIA PART II: Ontologies in the Life Sciences Biological Ontologies PATRICK LAMBRIX, HE TAN, VAIDA JAKONIENE, AND LENA STRÖMBÄCK Clinical Ontologies YVES LUSSIER AND OLIVIER BODENREIDER Ontology Engineering For Biological Applications vi Revolutionizing knowledge discovery in the life sciences LARISA N. SOLDATOVA AND ROSS D. KING The Evaluation of Ontologies: Toward Improved Semantic Interoperability LEO OBRST, WERNER CEUSTERS, INDERJEET MANI, STEVE RAY AND BARRY SMITH OWL for the Novice JEFF PAN PART III: Ontology Visualization Techniques for Ontology Visualization XIAOSHU WANG AND JONAS ALMEIDA On Vizualization of OWL Ontologies SERGUEI KRIVOV, FERDINANDO VILLA, RICHARD WILLIAMS, AND XINDONG WU PART IV: Ontologies in Action Applying OWL Reasoning to Genomics: A Case Study KATY WOLSTENCROFT, ROBERT STEVENS AND VOLKER HAARSLEV Can Semantic Web Technologies enable Translational Medicine? VIPUL KASHYAP, TONYA HONGSERMEIER AND SAMUEL J. ARONSON Ontology Design for Biomedical Text Mining RENÉ WITTE, THOMAS KAPPLER, AND CHRISTOPHER J.
    [Show full text]
  • Cancer Informatics: New Tools for a Data-Driven Age in Cancer Research Warren Kibbe1, Juli Klemm1, and John Quackenbush2
    Cancer Focus on Computer Resources Research Cancer Informatics: New Tools for a Data-Driven Age in Cancer Research Warren Kibbe1, Juli Klemm1, and John Quackenbush2 Cancer is a remarkably adaptable and formidable foe. Cancer Precision Medicine Initiative highlighted the importance of data- exploits many biological mechanisms to confuse and subvert driven cancer research, translational research, and its application normal physiologic and cellular processes, to adapt to thera- to decision making in cancer treatment (https://www.cancer.gov/ pies, and to evade the immune system. Decades of research and research/key-initiatives/precision-medicine). And the National significant national and international investments in cancer Strategic Computing Initiative highlighted the importance of research have dramatically increased our knowledge of the computing as a national competitive asset and included a focus disease, leading to improvements in cancer diagnosis, treat- on applying computing in biomedical research. Articles in the ment, and management, resulting in improved outcomes for mainstream media, such as that by Siddhartha Mukherjee in many patients. the New Yorker in April of 2017 (http://www.newyorker.com/ In melanoma, the V600E mutation in the BRAF gene is now magazine/2017/04/03/ai-versus-md), have emphasized the targetable by a specific therapy. BRAF is a serine/threonine protein growing importance of computing, machine learning, and data kinase activating the MAP kinase (MAPK)/ERK signaling pathway, in biomedicine. and both BRAF and MEK inhibitors, such as vemurafenib and The NCI (Rockville, MD) recognized the need to invest in dabrafenib, have shown dramatic responses in patients carrying informatics. In 2011, it established a funding opportunity, the mutation.
    [Show full text]
  • UNIVERSITY of CALIFORNIA SAN DIEGO Making Sense of Microbial
    UNIVERSITY OF CALIFORNIA SAN DIEGO Making sense of microbial populations from representative samples A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Computer Science by James T. Morton Committee in charge: Professor Rob Knight, Chair Professor Pieter Dorrestein Professor Rachel Dutton Professor Yoav Freund Professor Siavash Mirarab 2018 Copyright James T. Morton, 2018 All rights reserved. The dissertation of James T. Morton is approved, and it is acceptable in quality and form for publication on microfilm and electronically: Chair University of California San Diego 2018 iii DEDICATION To my friends and family who paved the road and lit the journey. iv EPIGRAPH The ‘paradox’ is only a conflict between reality and your feeling of what reality ‘ought to be’ —Richard Feynman v TABLE OF CONTENTS Signature Page . iii Dedication . iv Epigraph . .v Table of Contents . vi List of Abbreviations . ix List of Figures . .x List of Tables . xi Acknowledgements . xii Vita ............................................. xiv Abstract of the Dissertation . xvii Chapter 1 Methods for phylogenetic analysis of microbiome data . .1 1.1 Introduction . .2 1.2 Phylogenetic Inference . .4 1.3 Phylogenetic Comparative Methods . .6 1.4 Ancestral State Reconstruction . .9 1.5 Analysis of phylogenetic variables . 11 1.6 Using Phylogeny-Aware Distances . 15 1.7 Challenges of phylogenetic analysis . 18 1.8 Discussion . 19 1.9 Acknowledgements . 21 Chapter 2 Uncovering the horseshoe effect in microbial analyses . 23 2.1 Introduction . 24 2.2 Materials and Methods . 34 2.3 Acknowledgements . 35 Chapter 3 Balance trees reveal microbial niche differentiation . 36 3.1 Introduction .
    [Show full text]
  • Methodology for Predicting Semantic Annotations of Protein Sequences by Feature Extraction Derived of Statistical Contact Potentials and Continuous Wavelet Transform
    Universidad Nacional de Colombia Sede Manizales Master’s Thesis Methodology for predicting semantic annotations of protein sequences by feature extraction derived of statistical contact potentials and continuous wavelet transform Author: Supervisor: Gustavo Alonso Arango Dr. Cesar German Argoty Castellanos Dominguez A thesis submitted in fulfillment of the requirements for the degree of Master’s on Engineering - Industrial Automation in the Department of Electronic, Electric Engineering and Computation Signal Processing and Recognition Group June 2014 Universidad Nacional de Colombia Sede Manizales Tesis de Maestr´ıa Metodolog´ıapara predecir la anotaci´on sem´antica de prote´ınaspor medio de extracci´on de caracter´ısticas derivadas de potenciales de contacto y transformada wavelet continua Autor: Tutor: Gustavo Alonso Arango Dr. Cesar German Argoty Castellanos Dominguez Tesis presentada en cumplimiento a los requerimientos necesarios para obtener el grado de Maestr´ıaen Ingenier´ıaen Automatizaci´onIndustrial en el Departamento de Ingenier´ıaEl´ectrica,Electr´onicay Computaci´on Grupo de Procesamiento Digital de Senales Enero 2014 UNIVERSIDAD NACIONAL DE COLOMBIA Abstract Faculty of Engineering and Architecture Department of Electronic, Electric Engineering and Computation Master’s on Engineering - Industrial Automation Methodology for predicting semantic annotations of protein sequences by feature extraction derived of statistical contact potentials and continuous wavelet transform by Gustavo Alonso Arango Argoty In this thesis, a method to predict semantic annotations of the proteins from its primary structure is proposed. The main contribution of this thesis lies in the implementation of a novel protein feature representation, which makes use of the pairwise statistical contact potentials describing the protein interactions and geometry at the atomic level.
    [Show full text]
  • Standardised Benchmarking in the Quest for Orthologs
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Harvard University - DASH Standardised Benchmarking in the Quest for Orthologs The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Altenhoff, A. M., B. Boeckmann, S. Capella-Gutierrez, D. A. Dalquen, T. DeLuca, K. Forslund, J. Huerta-Cepas, et al. 2016. “Standardised Benchmarking in the Quest for Orthologs.” Nature methods 13 (5): 425-430. doi:10.1038/nmeth.3830. http://dx.doi.org/10.1038/ nmeth.3830. Published Version doi:10.1038/nmeth.3830 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:29408292 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Nat Methods Manuscript Author . Author manuscript; Manuscript Author available in PMC 2016 October 04. Published in final edited form as: Nat Methods. 2016 May ; 13(5): 425–430. doi:10.1038/nmeth.3830. Standardised Benchmarking in the Quest for Orthologs Adrian M. Altenhoff1,2, Brigitte Boeckmann3, Salvador Capella-Gutierrez4,5,6, Daniel A. Dalquen7, Todd DeLuca8, Kristoffer Forslund9, Jaime Huerta-Cepas9, Benjamin Linard10, Cécile Pereira11,12, Leszek P. Pryszcz4, Fabian Schreiber13, Alan Sousa da Silva13, Damian Szklarczyk14,15, Clément-Marie Train1, Peer Bork9,16,17, Odile Lecompte18, Christian von Mering14,15, Ioannis Xenarios3,19,20, Kimmen Sjölander21, Lars Juhl Jensen22, Maria J.
    [Show full text]
  • Finding New Order in Biological Functions from the Network Structure of Gene Annotations
    Finding New Order in Biological Functions from the Network Structure of Gene Annotations Kimberly Glass 1;2;3∗, and Michelle Girvan 3;4;5 1Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA 2Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA 3Physics Department, University of Maryland, College Park, MD, USA 4Institute for Physical Science and Technology, University of Maryland, College Park, MD, USA 5Santa Fe Institute, Santa Fe, NM, USA The Gene Ontology (GO) provides biologists with a controlled terminology that describes how genes are associated with functions and how functional terms are related to each other. These term-term relationships encode how scientists conceive the organization of biological functions, and they take the form of a directed acyclic graph (DAG). Here, we propose that the network structure of gene-term annotations made using GO can be employed to establish an alternate natural way to group the functional terms which is different from the hierarchical structure established in the GO DAG. Instead of relying on an externally defined organization for biological functions, our method connects biological functions together if they are performed by the same genes, as indicated in a compendium of gene annotation data from numerous different experiments. We show that grouping terms by this alternate scheme is distinct from term relationships defined in the ontological structure and provides a new framework with which to describe and predict the functions of experimentally identified sets of genes. Tools and supplemental material related to this work can be found online at http://www.networks.umd.edu.
    [Show full text]
  • Aggregation and Correlation Toolbox for Analyses of Genome Tracks Justin Jee Yale University
    University of Massachusetts eM dical School eScholarship@UMMS Program in Bioinformatics and Integrative Biology Program in Bioinformatics and Integrative Biology Publications and Presentations 4-15-2011 ACT: aggregation and correlation toolbox for analyses of genome tracks Justin Jee Yale University Joel Rozowsky Yale University Kevin Y. Yip Yale University See next page for additional authors Follow this and additional works at: http://escholarship.umassmed.edu/bioinformatics_pubs Part of the Bioinformatics Commons, Computational Biology Commons, and the Systems Biology Commons Repository Citation Jee, Justin; Rozowsky, Joel; Yip, Kevin Y.; Lochovsky, Lucas; Bjornson, Robert; Zhong, Guoneng; Zhang, Zhengdong; Fu, Yutao; Wang, Jie; Weng, Zhiping; and Gerstein, Mark B., "ACT: aggregation and correlation toolbox for analyses of genome tracks" (2011). Program in Bioinformatics and Integrative Biology Publications and Presentations. Paper 26. http://escholarship.umassmed.edu/bioinformatics_pubs/26 This material is brought to you by eScholarship@UMMS. It has been accepted for inclusion in Program in Bioinformatics and Integrative Biology Publications and Presentations by an authorized administrator of eScholarship@UMMS. For more information, please contact [email protected]. ACT: aggregation and correlation toolbox for analyses of genome tracks Authors Justin Jee, Joel Rozowsky, Kevin Y. Yip, Lucas Lochovsky, Robert Bjornson, Guoneng Zhong, Zhengdong Zhang, Yutao Fu, Jie Wang, Zhiping Weng, and Mark B. Gerstein Comments © The Author(s) 2011. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non- Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non- commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
    [Show full text]
  • The Bioperl Toolkit: Perl Modules for the Life Sciences
    Downloaded from genome.cshlp.org on January 25, 2012 - Published by Cold Spring Harbor Laboratory Press The Bioperl Toolkit: Perl Modules for the Life Sciences Jason E. Stajich, David Block, Kris Boulez, et al. Genome Res. 2002 12: 1611-1618 Access the most recent version at doi:10.1101/gr.361602 Supplemental http://genome.cshlp.org/content/suppl/2002/10/20/12.10.1611.DC1.html Material References This article cites 14 articles, 9 of which can be accessed free at: http://genome.cshlp.org/content/12/10/1611.full.html#ref-list-1 Article cited in: http://genome.cshlp.org/content/12/10/1611.full.html#related-urls Email alerting Receive free email alerts when new articles cite this article - sign up in the box at the service top right corner of the article or click here To subscribe to Genome Research go to: http://genome.cshlp.org/subscriptions Cold Spring Harbor Laboratory Press Downloaded from genome.cshlp.org on January 25, 2012 - Published by Cold Spring Harbor Laboratory Press Resource The Bioperl Toolkit: Perl Modules for the Life Sciences Jason E. Stajich,1,18,19 David Block,2,18 Kris Boulez,3 Steven E. Brenner,4 Stephen A. Chervitz,5 Chris Dagdigian,6 Georg Fuellen,7 James G.R. Gilbert,8 Ian Korf,9 Hilmar Lapp,10 Heikki Lehva¨slaiho,11 Chad Matsalla,12 Chris J. Mungall,13 Brian I. Osborne,14 Matthew R. Pocock,8 Peter Schattner,15 Martin Senger,11 Lincoln D. Stein,16 Elia Stupka,17 Mark D. Wilkinson,2 and Ewan Birney11 1University Program in Genetics, Duke University, Durham, North Carolina 27710, USA; 2National Research Council of
    [Show full text]