DOCSLIB.ORG

Introduction to Genomes with Ensembl

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Introduction to Genomes with Ensembl Introduction to Genomes with Ensembl Dr. Giulietta M. Spudich Ensembl Outreach Team1 of 24 Objectives What information about a gene can I find? What about a region of the genome? How do I navigate the data? 2 of 31 Introduction 1977: 1st genome to be sequenced (5 kb) 2004: finished human sequence (3 gb) Large amounts of raw DNA sequence data Genome Sequencing Fragment BAC clones Assemble Sequence Assemble Scaffolds Contigs Genome sequence CGCCGGGAGAAGCGTGAGGGGACAGATTTGTGACCGGCGCGGTTTTTGTCAG CTTACTCCGGCCAAAAAAGAACTGCACCTCTGGAGCGGACTTATTTACCAAGCA TTGGAGGAATATCGTAGGTAAAAATGCCTATTGGATCCAAAGAGAGGCCAACATT GCACTGCTGCGCCTCTGCTGCGCCTCGGGTGTCTTTTGCGGCGGTGGGTCGC CGCCGGGAGAAGCGTGAGGGGACAGATTTGTGACCGGCGCGGTTTTTGTCAG CTTACTCCGGCCAAAAAAGAACTGCACCTCTGGAGCGGACTTATTTACCAAGCA TTGGAGGAATATCGTAGGTAAAAATGCCTATTGGATCCAAAGAGAGGCCAACATT TTTTGAAATTTTTAAGACACGCTGCAACAAAGCAGATTTAGGACCAATAAGTCTT AATTGGTTTGAAGAACTTTCTTCAGAAGCTCCACCCTATAATTCTGAACCTGCAG ACTAAAATGGATCAAGCAGATGATGTTTCCTGTCCACTTCTAAATTCTTGTCTTAG AAGAATCTGAACATAAAAACAACAATTACGAACCAAACCTATTTAAAACTCCACAA AGGAAACCATCTTATAATCAGCTGGCTTCAACTCCAATAATATTCAAAGAGCAAG GGCTGACTCTGCCGCTGTACCAATCTCCTGTAAAAGAATTAGATAAATTCAAATT AGACTTAGGAAGGAATGTTCCCAATAGTAGACTAAAAGTCTTCGCACAGTGAAAT CGCCGGGAGAAGCGTGAGGGGACAGATTTGTGACCGGCGCGGTTTTTGTCAG CTTACTCCGGCCAAAAAAGAACTGCACCTCTGGAGCGGACTTATTTACCAAGCA TTGGAGGAATATCGTAGGTAAAAATGCCTATTGGATCCAAAGAGAGGCCAACATT ACTAAAATGGATCAAGCAGATGATGTTTCCTGTCCACTTCTAAATTCTTGTCTTAG AATTGGTTTGAAGAACTTTCTTCAGAAGCTCCACCCTATAATTCTGAACCTGCAG TGAAAGTCCTGTTGTTCTACAATGTACACATGTAACACCACAAAGAGATAAGTCA The Ensembl genome browser: making it interesting Regulation Conserved sequence Gene Allele • Splice variants, proteins, non-coding RNA • Small and large scale sequence variation, phenotype associations • Whole genome alignments, protein trees • Potential promoters and enhancers, DNA methylation • User upload, custom data Figure adapted from the ENCODE project www.nature.com/nature/focus/encode/ 6 21 May 2012 Genome Browsers • Ensembl Genome Browsers http://www.ensemblgenomes.org • NCBI Map Viewer http://www.ncbi.nlm.nih.gov/mapview/ • UCSC Genome Browser http://genome.ucsc.edu 7 of 31 Ensembl is Used Worldwide Top users: UK US Canada China France Germany Italy Japan Spain 8 of 31 Data Volume Challenge • UniProtKB/Swiss-Prot (reviewed) Q8IU82 536,029 (25,871 human) protein sequences • UniProtKB/TrEMBL 22,128,511 (217,918) NCBI RefSeq (reviewed) 15,744,232 (24,539) NP_006570 NM_006579 www.uniprot.org 9 of 24 A consensus set of protein coding sequences • Reaching a consensus coding sequence set for human and mouse. • 26,473 (human) 22,187 (mouse) (*as of Sept 2011) • If you see a “CCDS ID”, the coding sequence is agreed upon. Genome Res. 2009 Jul;19(7):1316-23. Epub 2009 Jun 4 10 of 31 What are the gold transcripts? UTR Coding Intron 11 of 31 VEGA/Havana (human, mouse, z-fish) • Automatic annotation pipeline: Gene building all at once (whole genome) Ensembl • Manual curation: reviewed by experts VEGA: Vertebrate Genome Annotation Havana 12 of 31 Genes and Transcripts in Ensembl High Quality: • CCDS transcripts • Ensembl/Havana merged (gold) transcripts 13 of 31 Ensembl/Havana • Transcripts are from: Ensembl Havana Ensembl/Havana Both (“gold”) Havana (00_) Ensembl (20_) Havana (00_) 14 of 31 Gene Names in Ensembl • ENSG### Ensembl Gene ID • ENST### Ensembl Transcript ID • ENSP### Ensembl Peptide ID • ENSE### Ensembl Exon ID • For non-human species a suffix is added: MUS for M. musculus ENSMUSG### DAR (Danio rerio) for zebrafish: ENSDARG### 15 of 31 Ensembl Features • The gene set. • Comparative analysis • Variation and regulation • BioMart (data export) • Display of external data (DAS) • Programmatic access via the Perl API • Open Source 16 of 31 Objectives What information about a gene can I find? What about a region of the genome? How do I navigate the data? See our coursebook for walk-throughs and exercises using our browser: http://www.ensembl.org/info/website/tutorials/coursebook.pdf 17 of 31 Variation • Nucleotide level • Single nucleotide polymorphism (SNP) • Small insertions and deletions (InDels) • Microsatellites (short tandem repeats) • Structural • Copy number variations (CNV) • Large insertions and deletions Sequence displays Gene: Sequence Transcript:cDNA Transcript: Exons Comparative Genomics 69 species in e!67 Ensembl tools Phenotype for a gene How is all this information organised? • Ensembl Views (Website) • Ensembl Database (open source) • BioMart „DataMining tool‟ 23 of 31 Help and documentation • Comments and questions? [email protected] • Mailing lists [email protected], [email protected] • Course online www.ensembl.info/ecourse • Our tutorials page www.ensembl.org/info/website/tutorials • YouTube channel www.youtube.com/user/EnsemblHelpdesk Follow us • Facebook www.facebook.com/Ensembl.org • Twitter https://twitter.com/Ensembl • Come visit our blog! www.ensembl.info Publications http://www.ensembl.org/info/about/publications.html • Flicek, P. et. al. Ensembl 2012 Nucleic Acids Res 40:D84-90 (2012) http://nar.oxfordjournals.org/content/40/D1/D84.long • Xosé M. Fernández-Suárez and Michael K. Schuster Using the Ensembl Genome Server to Browse Genomic Sequence Data. Current Protocols in Bioinformatics 1.15.1-1.15.48 (2010) www.ncbi.nlm.nih.gov/pubmed/20521244 • Giulietta M Spudich and Xosé M Fernández-Suárez Touring Ensembl: A practical guide to genome browsing BMC Genomics 11:295 (2010) www.biomedcentral.com/1471-2164/11/295 Ensembl Team Ensembl Paul Flicek (EBI), Steve Searle (Wellcome Trust Sanger Institute) Software Andy Yates, Stephen Keenan, Monika Komorowska, Rhoda Kinsella, Thomas Maurel, Kieron Taylor Comparative Javier Herrero, Kathryn Beal, Stephen Fitzgerald, Leo Gordon, Matthieu Muffato, Miguel Pignatelli Genomics Regulation Ian Dunham, Ikhlak Ahmed, Nathan Johnson, Thomas Juettemann, Steven Wilder Variation Fiona Cunningham, Laurent Gil, Sarah Hunt, Will McLaren, Graham Ritchie, Anja Thormann Analysis and Bronwen Aken, Amonida Zadissa, Dan Barrell, Susan Fairley, Carlos Garcίa Girón, Thibaut Annotation Hourlier, Andreas Kähäri, Rishi Nag, Magali Ruffier, Simon White Anne Parker, Ridwan Amode, Simon Brent, Bethan Pritchard, Harpreet Riat, Dan Sheppard, Steve Web Team Trevanion Outreach Giulietta M. Spudich, Jeff Almeida-King, Denise Carvalho-Silva, Bert Overduin, Michael Schuster Paul Kersey, Paul Derwent, Jay Humphrey, Arnaud Kerhornou, Eugene Kulesha, Nick Langridge, Ensembl Uma Maheswari, Mark McDowall, Michael Nuhn, Helder Pedro, Claudia Rato da Silva, Dan Genomes Staines, Iliana Toneva Ensembl Ewan Birney, Richard Durbin, Paul Flicek, Jen Harrow, Tim Hubbard, Glenn Proctor, Steve Searle Strategy .
Load more

Recommended publications
  • Multi-Class Protein Classification Using Adaptive Codes
    Journal of Machine Learning Research 8 (2007) 1557-1581 Submitted 8/06; Revised 4/07; Published 7/07 Multi-class Protein Classification Using Adaptive Codes Iain Melvin∗ [email protected] NEC Laboratories of America Princeton, NJ 08540, USA Eugene Ie∗ [email protected] Department of Computer Science and Engineering University of California San Diego, CA 92093-0404, USA Jason Weston [email protected] NEC Laboratories of America Princeton, NJ 08540, USA William Stafford Noble [email protected] Department of Genome Sciences Department of Computer Science and Engineering University of Washington Seattle, WA 98195, USA Christina Leslie [email protected] Center for Computational Learning Systems Columbia University New York, NY 10115, USA Editor: Nello Cristianini Abstract Predicting a protein’s structural class from its amino acid sequence is a fundamental problem in computational biology. Recent machine learning work in this domain has focused on develop- ing new input space representations for protein sequences, that is, string kernels, some of which give state-of-the-art performance for the binary prediction task of discriminating between one class and all the others. However, the underlying protein classification problem is in fact a huge multi- class problem, with over 1000 protein folds and even more structural subcategories organized into a hierarchy. To handle this challenging many-class problem while taking advantage of progress on the binary problem, we introduce an adaptive code approach in the output space of one-vs- the-rest prediction scores. Specifically, we use a ranking perceptron algorithm to learn a weight- ing of binary classifiers that improves multi-class prediction with respect to a fixed set of out- put codes.
    [Show full text]
  • Establishing Incentives and Changing Cultures to Support Data Access
    EXPERT ADVISORY GROUP ON DATA ACCESS ESTABLISHING INCENTIVES AND CHANGING CULTURES TO SUPPORT DATA ACCESS May 2014 ACKNOWLEDGEMENT This is a report of the Expert Advisory Group on Data Access (EAGDA). EAGDA was established by the MRC, ESRC, Cancer Research UK and the Wellcome Trust in 2012 to provide strategic advice on emerging scientific, ethical and legal issues in relation to data access for cohort and longitudinal studies. The report is based on work undertaken by the EAGDA secretariat at the Group’s request. The contributions of David Carr, Natalie Banner, Grace Gottlieb, Joanna Scott and Katherine Littler at the Wellcome Trust are gratefully acknowledged. EAGDA would also like to thank the representatives of the MRC, ESRC and Cancer Research UK for their support and input throughout the project. Most importantly, EAGDA owes a considerable debt of gratitude to the many individuals from the research community who contributed to this study through feeding in their expert views via surveys, interviews and focus groups. The Expert Advisory Group on Data Access Martin Bobrow (Chair) Bartha Maria Knoppers James Banks Mark McCarthy Paul Burton Andrew Morris George Davey Smith Onora O'Neill Rosalind Eeles Nigel Shadbolt Paul Flicek Chris Skinner Mark Guyer Melanie Wright Tim Hubbard 1 EXECUTIVE SUMMARY This project was developed as a key component of the workplan of the Expert Advisory Group on Data Access (EAGDA). EAGDA wished to understand the factors that help and hinder individual researchers in making their data (both published and unpublished) available to other researchers, and to examine the potential need for new types of incentives to enable data access and sharing.
    [Show full text]
  • Transformative Models to Promote Prescription Drug Innovation and Access: a Landscape Analysis
    Yale Journal of Health Policy, Law, and Ethics Volume 19 Issue 2 Article 2 2020 Transformative Models to Promote Prescription Drug Innovation and Access: A Landscape Analysis Phebe Hong Harvard Law School Aaron S. Kesselheim Professor of medicine at Harvard Medical School/Brigham and Women’s Hospital. Ameet Sarpatwari Assistant professor of medicine at Harvard Medical School/Brigham and Women’s Hospital. Follow this and additional works at: https://digitalcommons.law.yale.edu/yjhple Part of the Health Law and Policy Commons, and the Legal Ethics and Professional Responsibility Commons Recommended Citation Phebe Hong, Aaron S. Kesselheim & Ameet Sarpatwari, Transformative Models to Promote Prescription Drug Innovation and Access: A Landscape Analysis, 19 YALE J. HEALTH POL'Y L. & ETHICS (2020). Available at: https://digitalcommons.law.yale.edu/yjhple/vol19/iss2/2 This Article is brought to you for free and open access by Yale Law School Legal Scholarship Repository. It has been accepted for inclusion in Yale Journal of Health Policy, Law, and Ethics by an authorized editor of Yale Law School Legal Scholarship Repository. For more information, please contact [email protected]. Hong et al.: Transformative Models to Promote Prescription Drug Innovation and Transformative Models to Promote Prescription Drug Innovation and Access: A Landscape Analysis Phebe Hong, Aaron S. Kesselheim & Ameet Sarpatwari* Abstract: The patent-based pharmaceutical innovation system in the US does not incentivize the development of drugs with the greatest impact on patient or public health. It has also led to drug prices that patients and health care systems cannot afford. Three alternate approaches to promoting pharmaceutical innovation have been proposed to address these shortcomings.
    [Show full text]
  • A Database of Globular Protein Structural Domains: Clustering of Representative Family Members Into Similar Folds R Sowdhamini, Stephen D Rufino and Tom L Blundell
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Research Paper 209 A database of globular protein structural domains: clustering of representative family members into similar folds R Sowdhamini, Stephen D Rufino and Tom L Blundell Background: A database of globular domains, derived from a non-redundant set Address: Imperial Cancer Research Fund Unit of of proteins, is useful for the sequence analysis of aligned domains, for structural Structural Molecular Biology, Department of Crystallography, Birkbeck College, Malet Street, comparisons, for understanding domain stability and flexibility and for fold London WC1E 7HX, UK. recognition procedures. Domains are defined by the program DIAL and classified structurally using the procedure SEA. Correspondence: Tom L Blundell e-mail: [email protected] Results: The DIAL-derived domain database (DDBASE) consists of 436 protein Key words: clustering, comparison, chains involving 695 protein domains. Of these, 206 are ␣-class, 191 are ␤- protein domains, protein folds, structural similarity, class and 294 ␣ and ␤ class. The domains, 63% from multidomain proteins and superfamilies 73% less than 150 residues in length, were clustered automatically using both Received: 19 Jan 1996 single-link cluster analysis and hierarchical clustering to give a quantitative Revisions requested: 13 Feb 1996 estimate of similarity in the domain-fold space. Revisions received: 01 Mar 1996 Accepted: 01 Mar 1996 ␣ ␤ Conclusions: Highly populated and well described folds (doubly wound / , Published: 13 May 1996 singly wound ␣/␤ barrels, globins ␣, large Greek-key ␤ and flavin-binding ␣/␤) Electronic identifier: 1359-0278-001-00209 are recognized at a SEA cut-off score of 0.55 in single-link clustering and at 0.65 Folding & Design 13 May 1996, 1:209–220 in hierarchical clustering, although functionally related families are usually clearly distinguished at more stringent values.
    [Show full text]
  • Governance of Data Access
    EXPERT ADVISORY GROUP ON DATA ACCESS GOVERNANCE OF DATA ACCESS June 2015 EAGDA Report: Governance of Data Access EXECUTIVE SUMMARY Whilst it is widely accepted that data should be shared for secondary research uses where these can be balanced with the maintenance of participant privacy, there has thus far been little oversight or coordination of policies, resources and infrastructure across research funders to ensure data access is efficient, effective and proportionate. Funders have different policies and processes for maximizing the value from the datasets generated by their researchers, and EAGDA wished to identify whether and how improvements to governance for data access and the dissemination of good practice across the funders could enhance this value. We identified several good practice exemplars for funders and the research community, but also evidence of significant variability between studies that, in practice, lead to inefficiencies in access to and use of data for secondary purposes. We suggest that an approach that is more transparent and coordinated among research funders would provide benefits, both in terms of increasing the efficiency of data access and in ensuring that good governance approaches can get on with the job of ensuring the right balance between protecting research participants’ interests and enabling access to data for further research. This report makes several recommendations to funders, many of which are interlinked. These focus on areas in which specific funder action could improve the governance of data access for human cohort studies: RECOMMENDATIONS 1. Research funders should require explicit data sharing and management plans as part of grant applications (even if these plans conclude that data sharing is not appropriate); and should ensure that these issues are adjudicated before funding for new studies, or renewal of existing studies, is agreed.
    [Show full text]
  • HMMER User's Guide
    HMMER User’s Guide Biological sequence analysis using profile hidden Markov models http://hmmer.wustl.edu/ Version 2.2; August 2001 Sean Eddy Howard Hughes Medical Institute and Dept. of Genetics Washington University School of Medicine 660 South Euclid Avenue, Box 8232 Saint Louis, Missouri 63110, USA [email protected] With contributions by Ewan Birney ([email protected]) Copyright (C) 1992-2001, Washington University in St. Louis. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are retained on all copies. The HMMER software package is a copyrighted work that may be freely distributed and modified under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. Some versions of HMMER may have been obtained under specialized commercial licenses from Washington University; for details, see the files COPYING and LICENSE that came with your copy of the HMMER software. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Appendix for a copy of the full text of the GNU General Public License. 1 Contents 1 Tutorial 6 1.1 The programs in HMMER . 6 1.2 Files used in the tutorial . 7 1.3 Searching a sequence database with a single profile HMM . 7 HMM construction with hmmbuild ............................. 7 HMM calibration with hmmcalibrate ........................... 8 Sequence database search with hmmsearch ........................
    [Show full text]
  • A Novel and Effective Scoring Scheme for Structure Classification And
    A novel and effective scoring scheme for structure classification and pairwise similarity measurement Rezaul Karim∗, Md. Momin Al Aziz†,Swakkhar Shatabda‡ and M. Sohel Rahman∗ ∗Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology †Department of Computer Science, University of Manitoba, Canada ‡Department of Computer Science and Engineering, United International University Abstract—Protein tertiary structure defines its functions, clas- However, a major drawback of these approaches with re- sification and binding sites. Similar structural characteristics gards to the global structure similarity comparison is that these between two proteins often lead to the similar characteristics are much sensitive to local structure dissimilarity. Furthermore, thereof. Determining structural similarity accurately in real these approaches need to find an optimal alignment before time is a crucial research issue. In this paper, we present computing the score despite that often there are no remarkable a novel and effective scoring scheme that is dependent on alignment possible. Also finding proper alignment between two novel features extracted from protein alpha carbon distance matrices. Our scoring scheme is inspired from pattern recog- protein structures is computationally expensive if the size of nition and computer vision. Our method is significantly better the protein is large [9]. In spite of these inherent limitations than the current state of the art methods in terms of fam- and drawbacks, there exist a number of such methods in the ily match of pairs of protein structures and other statistical literature and the most notable ones among these are DALI measurements. The effectiveness of our method is tested on [10], [11], CE [12], TM Align [13], [14] and SP Align [15].
    [Show full text]
  • Genomic Anatomy of the Tyrp1 (Brown) Deletion Complex
    Genomic anatomy of the Tyrp1 (brown) deletion complex Ian M. Smyth*, Laurens Wilming†, Angela W. Lee*, Martin S. Taylor*, Phillipe Gautier*, Karen Barlow†, Justine Wallis†, Sancha Martin†, Rebecca Glithero†, Ben Phillimore†, Sarah Pelan†, Rob Andrew†, Karen Holt†, Ruth Taylor†, Stuart McLaren†, John Burton†, Jonathon Bailey†, Sarah Sims†, Jan Squares†, Bob Plumb†, Ann Joy†, Richard Gibson†, James Gilbert†, Elizabeth Hart†, Gavin Laird†, Jane Loveland†, Jonathan Mudge†, Charlie Steward†, David Swarbreck†, Jennifer Harrow†, Philip North‡, Nicholas Leaves‡, John Greystrong‡, Maria Coppola‡, Shilpa Manjunath‡, Mark Campbell‡, Mark Smith‡, Gregory Strachan‡, Calli Tofts‡, Esther Boal‡, Victoria Cobley‡, Giselle Hunter‡, Christopher Kimberley‡, Daniel Thomas‡, Lee Cave-Berry‡, Paul Weston‡, Marc R. M. Botcherby‡, Sharon White*, Ruth Edgar*, Sally H. Cross*, Marjan Irvani¶, Holger Hummerich¶, Eleanor H. Simpson*, Dabney Johnson§, Patricia R. Hunsicker§, Peter F. R. Little¶, Tim Hubbard†, R. Duncan Campbell‡, Jane Rogers†, and Ian J. Jackson*ʈ *Medical Research Council Human Genetics Unit, Edinburgh EH4 2XU, United Kingdom; †Wellcome Trust Sanger Institute, and ‡Medical Research Council Rosalind Franklin Centre for Genome Research, Hinxton CB10 1SA, United Kingdom; §Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831; and ¶Department of Biochemistry, Imperial College, London SW7 2AZ, United Kingdom Communicated by Liane B. Russell, Oak Ridge National Laboratory, Oak Ridge, TN, January 9, 2006 (received for review September 15, 2005) Chromosome deletions in the mouse have proven invaluable in the deletions also provided the means to produce physical maps of dissection of gene function. The brown deletion complex com- genetic markers. Studies of this kind have been published for prises >28 independent genome rearrangements, which have several loci, including albino (Tyr), piebald (Ednrb), pink-eyed been used to identify several functional loci on chromosome 4 dilution (p), and the brown deletion complex (2–6).
    [Show full text]
  • Genomethics Study End of Project Report and Evidence of Impact and Reach, 2010-2016
    The GenomEthics Study End of Project Report and Evidence of Impact and Reach, 2010-2016 Dr Anna Middleton Principal Staff Scientist, Ethics Researcher for the DDD Study www.GenomEthics.org The GenomEthics Study Dr Anna Middleton Principal Staff Scientist, Ethics Researcher for the DDD Study End of Project Report and Evidence of Impact and Reach, 2010-2016 DDD Ethics Project Index GenomEthics Project Background 1 1. Design of the GenomEthics study 1 Creation of the study 1 Compliance with Data Protection Legislation 2 Creation of a Lone Worker Policy 2 REC approval 2 Design of the Online Survey 2 2. Year 1: 2011-2012 3 3. Year 2: 2012-2013 11 4. Year 3: 2013-2014 11 5. Year 4: 2014-2015 12 6. Year 5: 2015-2016 12 GenomEthics Impact and Reach of Work 16 Guide to Interpreting Altmetric Scores and Data 17 1. Peer Reviewed Journal Articles 19 2. Mentions of the GenomEthics Study in Selected Peer Reviewed Journals and Policy 28 3. Peer Reviewed Conference Presentations 31 4. Invited Presentations, Seminars, Book Chapters and Teaching that Includes Work on the GenomEthics Study 35 5. Video, Museum Exhibits and Teaching Materials Based on Outcomes of the GenomeEthics Study 46 6. News Coverage and Media Referring to the GenomEthics Study 52 7. Blog and Online Journal Articles Referring to the GenomEthics Study 58 8. Social Media 82 Appendices 90 1. Appendix A : Original Protocol for Ethics and Whole Genome Studies (written in 2011) 91 2. Appendix B: Data Protection Registration 111 3. Appendix C: Lone Worker Policy 120 4.
    [Show full text]
  • 8:45 Arrival and Registration 9:15 Introduction: Edinburgh Genomics
    Whole Genome Sequencing Symposium 29th September 2017. The Roslin Institute, Easter Bush Campus, The University of Edinburgh. 8:45 Arrival and registration 9:15 Introduction: Edinburgh Genomics – a world leading genomics and bioinformatics facility delivering data globally to collaborators & customers. Prof. Tim Aitman, Professor of Molecular Pathology and Genetics, Director of the Centre for Genomic and Experimental Medicine at the University of Edinburgh. Director of Edinburgh Genomics Clinical Division 9:45 The Lothian Birth Cohorts of 1921 and 1936, now with whole-genome sequencing! Prof. Ian Deary, Professor of Differential Psychology, Director of the Centre for Cognitive Ageing and Cognitive Epidemiology at the University of Edinburgh. 10:15 Next generation plant and animal breeding leveraging Whole-Genome Sequencing Prof. David Hume, Professor of Mammalian Functional Genomic, Research Director of the Royal (Dick) School of Veterinary Studies at the University of Edinburgh. Chair, National Institutes of Bioscience. 10:45 Break 11:00 Using whole genome sequencing to understand the genetic complexity of psychiatric illness Dr Pippa Thomson, Deanery of Molecular, Genetic and Population Health Science, Centre for Genomic and Experimental Medicine, Edinburgh Neuroscience at the University of Edinburgh 11:30 Towards an informatics ecosystem for processing and interpreting large scale sequencing projects Dr. Jen Harrow, Program Manager Population Manager, Illumina 12:00 Refreshments and lunch 12:40 The UK 100,000 Genomes Project Tim Hubbard, Head of Genome Analysis, Genomics England 13:25 Phasing the whole genome on a short read sequencing platform Laurie Scott, FAS Lead 10x Genomics 13:55 Neglected genomes: animal diversity revealed. Prof. Mark Blaxter, Professor of Evolutionary Genomics, Institute of Evolutionary Biology, The University of Edinburgh.
    [Show full text]
  • Breakout 7: What Do National and International Health Data
    Breakout 7: What do national and international health data researchers need in a post-COVID-19 world? • Chairs: • Andrew Morris, Director, HDR UK • Emily Jefferson, Professor of Health Data Science and Director of Health Informatics Centre at University of Dundee • Panellists: • Gerry Reilly, Chief Technology Officer, HDR UK • Steve Kern, Deputy Director Quantitative Sciences, Bill & Melinda Gates Foundation • Tim Hubbard, Professor of Bioinformatics at King’s College, Head of Genome Analysis at Genomics England This session will start at 14:50 BST. Please use the Q&A function to ask questions to speakers. You are welcome to comment using the chat function, but we cannot guarantee this will be monitored. Introduction to HDRUK Innovation Gateway 25/06/2020 Gerry Reilly – Chief Technology Officer, HDRUK The Gateway: fundamental to the world’s health data research, trusted by patients, public and practitioners • We are on a journey and this is just the beginning • We first tested the concept with a Minimum Viable Product • Work has now started on the next iterations of the Gateway, engaging data custodians, patients, public and practitioners October 2019 January 2020 Jan - Mar 2020 April 2020 October 2020 RFP for Minimum Rapid Gateway Gateway Technology Viable Development Phase 2 Phase 2 Partner Product Task starts Milestone 1 | 3 Demo Gateway Phase 2 – milestone 1 the beginning of a journey Collections Gap analysis Semantic search Gateway Update Cohort v1 Cohort v2 Data quality tool Milestone 1 ends 28 Apr 2 June June July Aug Sept Oct 31 Oct Data Access v2 TRE integration v1 Data Access v3 TRE integration v2 Data Access v1 Tech Metadata onboarding Dashboards v1 Data recommendation Dashboards v2 partnership start Development will continue until 30 April 2022, with requirements being refined as we test and learn with our communities | 5 Thank you FRAMEWORKS FOR INTERNATIONAL COLLABORATION IN GLOBAL HEALTH DATA SHARING Solutions for everyday and emergencies Steven E.
    [Show full text]
  • Genomics England Publication Policy
    Genomics England Publication Policy Document Record ID Key Work stream Office of the Chief Scientist Programme Director Mark Caulfield Status Final Document Owner Dina Halai Version 3.8 Document Author Mark Caulfield, Tom Fowler, Jeanna Version Date 18/09/17 Mahon-Pearson, Nick Maltby, Tim Hubbard, Clare Turnbull, Sir John Bell 1 Document History The controlled copy of this document is maintained in the Genomics England internal document management system. Any copies of this document held outside of that system, in whatever format (for example, paper, email attachment), are considered to have passed out of control and should be checked for currency and validity. This document is uncontrolled when printed. 1.1 Version History Version Date Description 3.1 17/03/16 Updated to include Publication Committee Chair’s comments and annexes 3.2 21/03/16 Updated with Team Science publication reference 3.3 22/03/16 Formatted into GeL template and minor edits 3.4 05/04/16 Initial policy to inform discussions with Chair of Publication Committee 3.5 27/04/16 Amended section 9 3.6 18/05/16 Amended following review by Genomics England Board 3.7 14/07/16 Funders comments taken in 3.8 18/09/17 Amended section 7; acknowledging the use of patient data 1.2 Reviewers This document must be reviewed by the following: Name Title Version Mark Caulfield Chief Scientist 3.6 Tom Fowler Deputy Chief Scientist & Director of Public Health 3.4 Mark Bale Head of Science Partnerships 3.4 Nick Maltby General Counsel and Company Secretary 3.5 Tim Hubbard Head of Genome Analysis 3.4 Clare Turnbull Chief Scientific Officer for Cancer 3.4 Sir John Bell Chair, Genomics England Publications Committee 3.6 1.3 Approvers This document must be approved by the following: Name Responsibility Date Version Mark Caulfield Chief Scientist 3.6 Sir John Bell Chair, Genomics England Publications 3.6 Committee GENOMICS ENGLAND PUBLICATION POLICY 1 Contents 1 Document History .........................................................................................................................
    [Show full text]