DOCSLIB.ORG

Downloaded Directly from the TCGA

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Downloaded Directly from the TCGA UC San Diego UC San Diego Electronic Theses and Dissertations Title Building bioinformatic tools for massive repurposing of multi-omic data in the Sequence Read Archive Permalink https://escholarship.org/uc/item/62g7m386 Author Tsui, Brian Yik Tak Publication Date 2019 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA SAN DIEGO Building bioinformatic tools for massive repurposing of multi-omic data in the Sequence Read Archive A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Bioinformatics and Systems Biology by Brian Yik Tak Tsui Committee in charge: Professor Hannah Carter, Chair Professor Jill Mesirov, Co-Chair Professor Ruben Abagyan Professor Terry Gaasterland Professor Nathan Lewis 2019 Copyright Brian Yik Tak Tsui, 2019 All rights reserved. The Dissertation of Brian Yik Tak Tsui is approved, and it is acceptable in quality and form for publication on microfilm and electronically: University of California San Diego 2019 iii DEDICATION I want to thank all my friends and family members who have made my Ph.D. journey fun and enjoyable. iv TABLE OF CONTENTS SIGNATURE PAGE ...................................................................................................................... iii DEDICATION ............................................................................................................................... iv TABLE OF CONTENTS ................................................................................................................ v LIST OF FIGURES ...................................................................................................................... vii LIST OF TABLES ......................................................................................................................... ix ACKNOWLEDGEMENTS ............................................................................................................ x VITA ............................................................................................................................................. xii ABSTRACT OF THE DISSERTATION ..................................................................................... xiv INTRODUCTION ........................................................................................................................... 1 References ................................................................................................................................... 3 CHAPTER 1: Extracting allelic read counts from 250,000 human sequencing runs in Sequence Read Archive ................................................................................................................................... 4 1.1 Abstract .................................................................................................................................. 4 1.2 Introduction ........................................................................................................................... 5 1.3 Results ................................................................................................................................... 8 1.4 Discussion ............................................................................................................................ 15 1.5 Materials and Methods ........................................................................................................ 18 1.6 Figures ................................................................................................................................. 21 1.7 Tables .................................................................................................................................. 27 1.8 Supplementary code and data availability ........................................................................... 29 1.9 Acknowledgements ............................................................................................................. 30 1.10 References ......................................................................................................................... 31 CHAPTER 2: SkyMap JupyterHub: A cloud platform to query and analyze >900,000 re-processed public sequencing experiments from >20,000 studies .................................................................. 34 2.1 Abstract ................................................................................................................................ 34 v 2.2 Introduction ......................................................................................................................... 35 2.3 Results ................................................................................................................................. 37 2.4 Discussion ............................................................................................................................ 44 2.5 Materials and Methods ........................................................................................................ 46 2.6 Figures ................................................................................................................................. 50 2.8 Table .................................................................................................................................... 63 2.10 Acknowledgements ........................................................................................................... 64 2.11 References ......................................................................................................................... 65 CHAPTER 3: Creating a scalable deep learning based Named Entity Recognition Model for biomedical textual data by repurposing BioSample free-text annotations .................................... 68 3.1 Abstract ................................................................................................................................ 68 3.2 Introduction ......................................................................................................................... 69 3.3 Method and materials .......................................................................................................... 72 3.4 Results ................................................................................................................................. 78 3.5 Discussion ............................................................................................................................ 80 3.6 Figures ................................................................................................................................. 82 3.7 Tables .................................................................................................................................. 90 3.8 Supplemental Figures .......................................................................................................... 91 3.9 Supplementary Table ........................................................................................................... 94 3.10 Acknowledgement ............................................................................................................. 97 3.11 References ......................................................................................................................... 98 EPILOGUE .................................................................................................................................. 101 vi LIST OF FIGURES Figure 1.1. Number of sequencing runs are increasing exponentially in the SRA ........................ 21 Figure 1.2. Simple pipeline for extracting >300,000 human sequencing runs from the SRA. ..... 22 Figure 1.3. Distribution of the processed SRA data. ..................................................................... 23 Figure 1.4. Targeted reference remains accurate for sequence alignment. ................................... 24 Figure 1.5. Effect of PCR duplicates removal ............................................................................... 25 Figure 1.6. RNA-seq can recover variants extracted from WXS .................................................. 26 Figure 2.1. Overview ..................................................................................................................... 50 Figure 2.2. The landscape of reprocessed SRA data ..................................................................... 51 Figure 2.3. Variant extraction using our JuypterHub. ................................................................... 52 Figure 2.4. Expression analysis using our JupyterHub. ................................................................ 53 Figure 2.5. Large-scale meta-analysis using reprocessed RNA-seq data. ..................................... 54 Figure S2.1: SRA data landscape. ................................................................................................. 55 Figure S2.2: Microbe read lengths. ............................................................................................... 56 Figure S2.3: Workflow diagram. ................................................................................................... 57 Figure S2.4: Distribution of microbe abundance in the SRA. ....................................................... 58 Figure S2.5: Plotly cloud user interface. ....................................................................................... 59 Figure S2.6: Reference
Load more

Recommended publications
  • ISCB's Initial Reaction to the New England Journal of Medicine
    MESSAGE FROM ISCB ISCB’s Initial Reaction to The New England Journal of Medicine Editorial on Data Sharing Bonnie Berger, Terry Gaasterland, Thomas Lengauer, Christine Orengo, Bruno Gaeta, Scott Markel, Alfonso Valencia* International Society for Computational Biology, Inc. (ISCB) * [email protected] The recent editorial by Drs. Longo and Drazen in The New England Journal of Medicine (NEJM) [1] has stirred up quite a bit of controversy. As Executive Officers of the International Society of Computational Biology, Inc. (ISCB), we express our deep concern about the restric- tive and potentially damaging opinions voiced in this editorial, and while ISCB works to write a detailed response, we felt it necessary to promptly address the editorial with this reaction. While some of the concerns voiced by the authors of the editorial are worth considering, large parts of the statement purport an obsolete view of hegemony over data that is neither in line with today’s spirit of open access nor furthering an atmosphere in which the potential of data can be fully realized. ISCB acknowledges that the additional comment on the editorial [2] eases some of the polemics, but unfortunately it does so without addressing some of the core issues. We still feel, however, that we need to contrast the opinion voiced in the editorial with what we consider the axioms of our scientific society, statements that lead into a fruitful future of data-driven science: • Data produced with public money should be public in benefit of the science and society • Restrictions to the use of public data hamper science and slow progress OPEN ACCESS • Open data is the best way to combat fraud and misinterpretations Citation: Berger B, Gaasterland T, Lengauer T, Orengo C, Gaeta B, Markel S, et al.
    [Show full text]
  • Downloaded from [16, 18]
    UC San Diego UC San Diego Electronic Theses and Dissertations Title Predicting growth optimization strategies with metabolic/expression models Permalink https://escholarship.org/uc/item/6nr2539t Author Liu, Joanne Publication Date 2017 Supplemental Material https://escholarship.org/uc/item/6nr2539t#supplemental Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, SAN DIEGO Predicting growth optimization strategies with metabolic/expression models A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Bioinformatics and Systems Biology by Joanne K. Liu Committee in charge: Professor Karsten Zengler, Chair Professor Nathan Lewis, Co-Chair Professor Michael Burkart Professor Terry Gaasterland Professor Bernhard Palsson Professor Milton Saier 2017 Copyright Joanne K. Liu, 2017 All rights reserved. The dissertation of Joanne K. Liu is approved, and it is acceptable in quality and form for publication on micro- film and electronically: Co-Chair Chair University of California, San Diego 2017 iii DEDICATION To my mom and dad, who I cannot thank enough for supporting me throughout my education, and to The One. iv EPIGRAPH Essentially, all models are wrong, but some are useful. |George E. P. Box v TABLE OF CONTENTS Signature Page.................................. iii Dedication..................................... iv Epigraph.....................................v Table of Contents................................
    [Show full text]
  • ACM-BCB 2016 the 7Th ACM Conference on Bioinformatics
    ACM-BCB 2016 The 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics October 2-5, 2016 Organizing Committee General Chairs: Steering Committee: Ümit V. Çatalyürek, Georgia Institute of Technology Aidong Zhang, State University of NeW York at Buffalo, Genevieve Melton-Meaux, University of Minnesota Co-Chair May D. Wang, Georgia Institute of Technology and Program Chairs: Emory University, Co-Chair John Kececioglu, University of Arizona Srinivas Aluru, Georgia Institute of Technology Adam Wilcox, University of Washington Tamer Kahveci, University of Florida Christopher C. Yang, Drexel University Workshop Chair: Ananth Kalyanaraman, Washington State University Tutorial Chair: Mehmet Koyuturk, Case Western Reserve University Demo and Exhibit Chair: Robert (Bob) Cottingham, Oak Ridge National Laboratory Poster Chairs: Lin Yang, University of Florida Dongxiao Zhu, Wayne State University Registration Chair: Preetam Ghosh, Virginia CommonWealth University Publicity Chairs Daniel Capurro, Pontificia Univ. Católica de Chile A. Ercument Cicek, Bilkent University Pierangelo Veltri, U. Magna Graecia of Catanzaro Student Travel Award Chairs May D. Wang, Georgia Institute of Technology and Emory University JaroslaW Zola, University at Buffalo, The State University of NeW York Student Activity Chair Marzieh Ayati, Case Western Reserve University Dan DeBlasio, Carnegie Mellon University Proceedings Chairs: Xinghua Mindy Shi, U of North Carolina at Charlotte Yang Shen, Texas A&M University Web Admins: Anas Abu-Doleh, The
    [Show full text]
  • Gaëlle GARET Classification Et Caractérisation De Familles Enzy
    No d’ordre : 000 ANNÉE 2015 60 THÈSE / UNIVERSITÉ DE RENNES 1 sous le sceau de l’Université Européenne de Bretagne pour le grade de DOCTEUR DE L’UNIVERSITÉ DE RENNES 1 Mention : Informatique École doctorale Matisse présentée par Gaëlle GARET préparée à l’unité de recherche Inria/Irisa – UMR6074 Institut de Recherche en Informatique et Système Aléatoires Composante universitaire : ISTIC Thèse à soutenir à Rennes Classification et le 16 décembre 2014 devant le jury composé de : caractérisation Jean-Christophe JANODET Professeur à l’Université d’Evry-Val-d’Essonne / Rapporteur de familles enzy- Amedeo NAPOLI Directeur de recherche au Loria, Nancy / Rapporteur Colin DE LA HIGUERA matiques à l’aide Professeur à l’Université de Nantes / Examinateur Olivier RIDOUX Professeur à l’Université de Rennes 1 / Examinateur de méthodes for- Mirjam CZJZEK Directrice de recherche CNRS, Roscoff / Examinatrice Jacques NICOLAS melles Directeur de recherche à Inria, Rennes / Directeur de thèse François COSTE Chargé de recherche à Inria, Rennes / Co-directeur de thèse Ainsi en était-il depuis toujours. Plus les hommes accumulaient des connaissances, plus ils prenaient la mesure de leur ignorance. Dan Brown, Le Symbole perdu Tu me dis, j’oublie. Tu m’enseignes, je me souviens. Tu m’impliques, j’apprends. Benjamin Franklin Remerciements Je remercie tout d’abord la région Bretagne et Inria qui ont permis de financer ce projet de thèse. Merci à Jean-Christophe Janodet et Amedeo Napoli qui ont accepté de rapporter cette thèse et à Olivier Ridoux, Colin De La Higuera et Mirjam Czjzek pour leur participation au jury. J’aimerais aussi dire un grand merci à mes deux directeurs de thèse : Jacques Ni- colas et François Coste, qui m’ont toujours apporté leur soutien tant dans le domaine scientifique que personnel.
    [Show full text]
  • I S C B N E W S L E T T
    ISCB NEWSLETTER FOCUS ISSUE {contents} President’s Letter 2 Member Involvement Encouraged Register for ISMB 2002 3 Registration and Tutorial Update Host ISMB 2004 or 2005 3 David Baker 4 2002 Overton Prize Recipient Overton Endowment 4 ISMB 2002 Committees 4 ISMB 2002 Opportunities 5 Sponsor and Exhibitor Benefits Best Paper Award by SGI 5 ISMB 2002 SIGs 6 New Program for 2002 ISMB Goes Down Under 7 Planning Underway for 2003 Hot Jobs! Top Companies! 8 ISMB 2002 Job Fair ISCB Board Nominations 8 Bioinformatics Pioneers 9 ISMB 2002 Keynote Speakers Invited Editorial 10 Anna Tramontano: Bioinformatics in Europe Software Recommendations11 ISCB Software Statement volume 5. issue 2. summer 2002 Community Development 12 ISCB’s Regional Affiliates Program ISCB Staff Introduction 12 Fellowship Recipients 13 Awardees at RECOMB 2002 Events and Opportunities 14 Bioinformatics events world wide INTERNATIONAL SOCIETY FOR COMPUTATIONAL BIOLOGY A NOTE FROM ISCB PRESIDENT This newsletter is packed with information on development and dissemination of bioinfor- the ISMB2002 conference. With over 200 matics. Issues arise from recommendations paper submissions and over 500 poster submis- made by the Society’s committees, Board of sions, the conference promises to be a scientific Directors, and membership at large. Important feast. On behalf of the ISCB’s Directors, staff, issues are defined as motions and are discussed EXECUTIVE COMMITTEE and membership, I would like to thank the by the Board of Directors on a bi-monthly Philip E. Bourne, Ph.D., President organizing committee, local organizing com- teleconference. Motions that pass are enacted Michael Gribskov, Ph.D., mittee, and program committee for their hard by the Executive Committee which also serves Vice President work preparing for the conference.
    [Show full text]
  • Workshop Focuses on DNA Sequence Annotation
    Workshop Focuses on DNA Sequence Annotation By Richard Mural, Life Sciences Division, Oak Ridge National Laboratory Introduction Automatic annotation of large amounts of genomic DNA sequence clearly is and will continue to be a formidable challenge. This problem will be addressed properly only by developing very efficient computational tools for initial sequence annotation, treating the annotations as hypotheses, and testing and verifying them in the laboratory. Additionally, if the generated annotations are to be of maximum usefulness, results must be stored in an easily retrievable and queryable form in well-curated databases. The "If you sequence it, the community will annotate it" approach is unlikely to produce desired results, and new paradigms and possibly new organizational models will need to be implemented to present genomic sequence in its most useful form. Annotation Meeting The Fifth International Conference on Intelligent Systems for Molecular Biology held June 21 25, 1997, in Porto Carras, Greece, ended with a workshop on Automatic Annotation of Genome Sequence Data. Eight workshop speakers addressed three basic questions: What are the challenges in automatic annotation? What are the best technologies for doing this job? What is the best division of labor between biology and computer science? Introductory remarks by session chairman Chris Sander [European Molecular Biology Laboratory European Bioinformatics Institute (EBI)] made clear that no one yet has the experience to know the right way to proceed with automatic annotation. Richard Durbin (Sanger Cantre) stressed an often-repeated theme that proper annotation will require wet-laboratory work as well as computational annotation. He also stressed the need for curated databases.
    [Show full text]
  • Studying the Regulatory Landscape of Flowering Plants
    Studying the Regulatory Landscape of Flowering Plants Jan Van de Velde Promoter: Prof. Dr. Klaas Vandepoele Co-Promoter: Prof. Dr. Jan Fostier Ghent University Faculty of Sciences Department of Plant Biotechnology and Bioinformatics VIB Department of Plant Systems Biology Comparative and Integrative Genomics Research funded by a PhD grant of the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT Vlaanderen). Dissertation submitted in fulfilment of the requirements for the degree of Doctor in Sciences:Bioinformatics. Academic year: 2016-2017 Examination Commitee Prof. Dr. Geert De Jaeger (chair) Faculty of Sciences, Department of Plant Biotechnology and Bioinformatics, Ghent University Prof. Dr. Klaas Vandepoele (promoter) Faculty of Sciences, Department of Plant Biotechnology and Bioinformatics, Ghent University Prof. Dr. Jan Fostier (co-promoter) Faculty of Engineering and Architecture, Department of Information Technology (INTEC), Ghent University - iMinds Prof. Dr. Kerstin Kaufmann Institute for Biochemistry and Biology, Potsdam University Prof. Dr. Pieter de Bleser Inflammation Research Center, Flanders Institute of Biotechnology (VIB) and Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium Dr. Vanessa Vermeirssen Faculty of Sciences, Department of Plant Biotechnology and Bioinformatics, Ghent University Dr. Stefanie De Bodt Crop Science Division, Bayer CropScience SA-NV, Functional Biology Dr. Inge De Clercq Department of Animal, Plant and Soil Science, ARC Centre of Excellence in Plant Energy Biology, La Trobe University and Faculty of Sciences, Department of Plant Biotechnology and Bioinformatics, Ghent University iii Thank You! Throughout this PhD I have received a lot of support, therefore there are a number of people I would like to thank. First of all, I would like to thank Klaas Vandepoele, for his support and guidance.
    [Show full text]
  • CV Burkhard Rost
    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 Photo: © Eckert & Heddergott, TUM Web www.rostlab.org Fax +49-89-289-19-414 Document: CV Burkhard Rost TU München Affiliation: Columbia University TOC: • Tabulated curriculum vitae • Grants • List of publications Highlights by numbers: • 186 invited talks in 29 countries (incl. TedX) • 250 publications (187 peer-review, 168 first/last author) • Google Scholar 2016/01: 30,502 citations, h-index=80, i10=179 • PredictProtein 1st Internet server in mol. biol. (since 1992) • 8 years ISCB President (International Society for Computational Biology) • 143 trained (29% female, 50% foreigners from 32 nations on 6 continents) 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.
    [Show full text]
  • Workshop on Human Language Technology and Knowledge
    AI Magazine Volume 23 Number 2 (2002) (© AAAI) Workshop Reports Knowledge Media Institute at The Workshop on Human Open University in England, gave the keynote entitled “Supporting Organizational Learning through the Language Technology and Enrichment of Documents.” Accord- ing to Domingue, only a small per- Knowledge Management centage of corporate training is ever applied within the workplace because organizations tend to use school- based methods of learning in con- trast to organizational learning based Mark T. Maybury on theories of learning in the work- place. Domingue described knowl- edge sharing by enriching web docu- ments with informal and formal representations, a process that cap- tures the context in which a docu- The Workshop on Human Language technologies that could enable ment is created and applied. Technology and Knowledge Manage- knowledge management functions Domingue demonstrated how this ment was held on July 6 and 7 in such as the following: enrichment facilitates retrieval and Toulouse, France, in conjunction Expert discovery: Modeling, cata- comprehension. with the meeting of the Joint Associ- loging, and tracking of distributed In addition, the group heard an ation for Computational Linguistics organizations and communities of invited talk from Hans Uszkoreit and European Association for Com- experts (DFKI Saarbruecken), scientific direc- putational Linguistics (ACL / EACL Knowledge discovery: Identifica- tor at the German Research Center ’01). Human language technologies tion and classification of knowledge for Artificial Intelligence (DFKI), head promise solutions to challenges in from unstructured multimedia data of DFKI Language Technology Lab, human-computer interaction, infor- Knowledge sharing: Awareness of, and professor of computational lin- mation access, and knowledge man- and access to, enterprise expertise guistics at the Department of Com- agement.
    [Show full text]
  • Proceedings of the Eighteenth International Conference on Machine Learning., 282 – 289
    Abstracts of papers, posters and talks presented at the 2008 Joint RECOMB Satellite Conference on REGULATORYREGULATORY GENOMICS GENOMICS - SYSTEMS BIOLOGY - DREAM3 Oct 29-Nov 2, 2008 MIT / Broad Institute / CSAIL BMP follicle cells signaling EGFR signaling floor cells roof cells Organized by Manolis Kellis, MIT Andrea Califano, Columbia Gustavo Stolovitzky, IBM Abstracts of papers, posters and talks presented at the 2008 Joint RECOMB Satellite Conference on REGULATORYREGULATORY GENOMICS GENOMICS - SYSTEMS BIOLOGY - DREAM3 Oct 29-Nov 2, 2008 MIT / Broad Institute / CSAIL Organized by Manolis Kellis, MIT Andrea Califano, Columbia Gustavo Stolovitzky, IBM Conference Chairs: Manolis Kellis .................................................................................. Associate Professor, MIT Andrea Califano ..................................................................... Professor, Columbia University Gustavo Stolovitzky....................................................................Systems Biology Group, IBM In partnership with: Genome Research ..............................................................................editor: Hillary Sussman Nature Molecular Systems Biology ............................................... editor: Thomas Lemberger Journal of Computational Biology ...............................................................editor: Sorin Istrail Organizing committee: Eleazar Eskin Trey Ideker Eran Segal Nir Friedman Douglas Lauffenburger Ron Shamir Leroy Hood Satoru Miyano Program Committee: Regulatory Genomics:
    [Show full text]
  • Reverse Engineering Gene Regulatory Networks for Elucidating Transcriptome Organisation, Gene Function and Gene Regulation in Mammalian Systems
    Ph.D. Thesis Reverse Engineering Gene Regulatory Networks for elucidating transcriptome organisation, gene function and gene regulation in mammalian systems Director of studies: Candidate: Dr. Diego di Bernardo Vincenzo Belcastro External supervisor: Degree in Computer Science Prof. Mario di Bernardo ARC: Telethon Institute of Genetics and Medicine Year 2007/2010 Contents LIST OF TABLES vii LIST OF FIGURES viii 1 Introduction 3 2 Introduction to reverse-engineering 7 2.1 Microarray technology and microarray data repositories . 12 2.2 Reverse-engineering . 14 2.2.1 Bayesian networks . 14 2.2.2 Association Networks . 19 2.2.3 Ordinary differential equations (ODEs) . 25 2.2.4 Other Approaches . 27 3 Comparison of Reverse-engineering algorithms 29 3.1 Gene network inference algorithms . 30 3.1.1 BANJO . 31 3.1.2 ARACNe . 34 3.1.3 NIR . 37 Contents iii 3.2 In-silico and experimental data . 44 3.2.1 Generation of `In silico' data . 44 3.2.2 Experimental data . 50 3.2.3 Assessing the performance of algorithms . 53 3.3 Results: `in silico' evaluation . 57 3.3.1 Application of parallel NIR . 60 3.4 Results: Experimental evaluation . 61 3.4.1 Reverse Engineering the IRMA network . 62 3.5 Discussion and Conclusion . 67 4 Reverse-engineering gene networks from massive and hetero- geneous gene expression profiles 69 4.1 Introduction . 70 4.2 A new algorithm for reverse-engineering . 71 4.2.1 Normalisation of Gene Expression Profiles . 71 4.2.2 Mutual Information . 72 4.3 Application to simulated data . 75 4.3.1 Simulated Dataset .
    [Show full text]
  • ISCB's Initial Reaction to New England Journal of Medicine Editorial On
    F1000Research 2016, 5(ISCB Comm J):157 Last updated: 10 MAR 2016 EDITORIAL ISCB’s initial reaction to New England Journal of Medicine editorial on data sharing [version 1; referees: not peer reviewed] Alfonso Valencia, Scott Markel, Bruno Gaeta, Terry Gaasterland, Thomas Lengauer, Bonnie Berger, Christine Orengo International Society for Computational Biology, Inc. (ISCB), Bethesda, MD, USA First published: 11 Feb 2016, 5(ISCB Comm J):157 (doi: Not Peer Reviewed v1 10.12688/f1000research.8051.1) Latest published: 11 Feb 2016, 5(ISCB Comm J):157 (doi: This article is an Editorial and has not been 10.12688/f1000research.8051.1) subject to external peer review. Abstract This message is a response from the ISCB in light of the recent the New Discuss this article England Journal of Medicine (NEJM) editorial around data sharing. Comments (0) This article is included in the Messages from the ISCB channel. Corresponding author: Alfonso Valencia ([email protected]) How to cite this article: Valencia A, Markel S, Gaeta B et al. ISCB’s initial reaction to New England Journal of Medicine editorial on data sharing [version 1; referees: not peer reviewed] F1000Research 2016, 5(ISCB Comm J):157 (doi: 10.12688/f1000research.8051.1) Copyright: © 2016 Valencia A et al. This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Grant information: The author(s) declared that no grants were involved in supporting this work. Competing interests: No competing interests were disclosed.
    [Show full text]