Genomics Nation a Benchmark of the Size and Strengths of the UK Genomics Sector
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Uniprot at EMBL-EBI's Role in CTTV
Barbara P. Palka, Daniel Gonzalez, Edd Turner, Xavier Watkins, Maria J. Martin, Claire O’Donovan European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK UniProt at EMBL-EBI’s role in CTTV: contributing to improved disease knowledge Introduction The mission of UniProt is to provide the scientific community with a The Centre for Therapeutic Target Validation (CTTV) comprehensive, high quality and freely accessible resource of launched in Dec 2015 a new web platform for life- protein sequence and functional information. science researchers that helps them identify The UniProt Knowledgebase (UniProtKB) is the central hub for the collection of therapeutic targets for new and repurposed medicines. functional information on proteins, with accurate, consistent and rich CTTV is a public-private initiative to generate evidence on the annotation. As much annotation information as possible is added to each validity of therapeutic targets based on genome-scale experiments UniProtKB record and this includes widely accepted biological ontologies, and analysis. CTTV is working to create an R&D framework that classifications and cross-references, and clear indications of the quality of applies to a wide range of human diseases, and is committed to annotation in the form of evidence attribution of experimental and sharing its data openly with the scientific community. CTTV brings computational data. together expertise from four complementary institutions: GSK, Biogen, EMBL-EBI and Wellcome Trust Sanger Institute. UniProt’s disease expert curation Q5VWK5 (IL23R_HUMAN) This section provides information on the disease(s) associated with genetic variations in a given protein. The information is extracted from the scientific literature and diseases that are also described in the OMIM database are represented with a controlled vocabulary. -
13 Genomics and Bioinformatics
Enderle / Introduction to Biomedical Engineering 2nd ed. Final Proof 5.2.2005 11:58am page 799 13 GENOMICS AND BIOINFORMATICS Spencer Muse, PhD Chapter Contents 13.1 Introduction 13.1.1 The Central Dogma: DNA to RNA to Protein 13.2 Core Laboratory Technologies 13.2.1 Gene Sequencing 13.2.2 Whole Genome Sequencing 13.2.3 Gene Expression 13.2.4 Polymorphisms 13.3 Core Bioinformatics Technologies 13.3.1 Genomics Databases 13.3.2 Sequence Alignment 13.3.3 Database Searching 13.3.4 Hidden Markov Models 13.3.5 Gene Prediction 13.3.6 Functional Annotation 13.3.7 Identifying Differentially Expressed Genes 13.3.8 Clustering Genes with Shared Expression Patterns 13.4 Conclusion Exercises Suggested Reading At the conclusion of this chapter, the reader will be able to: & Discuss the basic principles of molecular biology regarding genome science. & Describe the major types of data involved in genome projects, including technologies for collecting them. 799 Enderle / Introduction to Biomedical Engineering 2nd ed. Final Proof 5.2.2005 11:58am page 800 800 CHAPTER 13 GENOMICS AND BIOINFORMATICS & Describe practical applications and uses of genomic data. & Understand the major topics in the field of bioinformatics and DNA sequence analysis. & Use key bioinformatics databases and web resources. 13.1 INTRODUCTION In April 2003, sequencing of all three billion nucleotides in the human genome was declared complete. This landmark of modern science brought with it high hopes for the understanding and treatment of human genetic disorders. There is plenty of evidence to suggest that the hopes will become reality—1631 human genetic diseases are now associated with known DNA sequences, compared to the less than 100 that were known at the initiation of the Human Genome Project (HGP) in 1990. -
Genomics and Its Impact on Science and Society: the Human Genome Project and Beyond
DOE/SC-0083 Genomics and Its Impact on Science and Society The Human Genome Project and Beyond U.S. Department of Energy Genome Research Programs: genomics.energy.gov A Primer ells are the fundamental working units of every living system. All the instructions Cneeded to direct their activities are contained within the chemical DNA (deoxyribonucleic acid). DNA from all organisms is made up of the same chemical and physical components. The DNA sequence is the particular side-by-side arrangement of bases along the DNA strand (e.g., ATTCCGGA). This order spells out the exact instruc- tions required to create a particular organism with protein complex its own unique traits. The genome is an organism’s complete set of DNA. Genomes vary widely in size: The smallest known genome for a free-living organism (a bac- terium) contains about 600,000 DNA base pairs, while human and mouse genomes have some From Genes to Proteins 3 billion (see p. 3). Except for mature red blood cells, all human cells contain a complete genome. Although genes get a lot of attention, the proteins DNA in each human cell is packaged into 46 chro- perform most life functions and even comprise the mosomes arranged into 23 pairs. Each chromosome is majority of cellular structures. Proteins are large, complex a physically separate molecule of DNA that ranges in molecules made up of chains of small chemical com- length from about 50 million to 250 million base pairs. pounds called amino acids. Chemical properties that A few types of major chromosomal abnormalities, distinguish the 20 different amino acids cause the including missing or extra copies or gross breaks and protein chains to fold up into specific three-dimensional rejoinings (translocations), can be detected by micro- structures that define their particular functions in the cell. -
100000 Genomes and Genomics England
100,000 Genomes & Genomics England Tim Hubbard Genomics England King’s College London, King’s Health Partners Wellcome Trust Sanger Institute Global Leaders in Genomic Medicine Washington 8-9th January 2014 UK Health System 101 • Four separate health services – NHS England – NHS Wales – NHS Scotland – Health & Social Care in Northern Ireland (HSC) • NHS (England) – ~1.4 million employees – ~£110 billion annual budget • Structure in England changed 1st April 2013 https://www.gov.uk/government/organisations/department-of-health Linking Health data to Research Clinical Data Healthcare Professional World Genotype Electronic Health Record Whole Genome Sequencing Phenotype Electronic Genomic Biology Health Data World Records Reference Genotype and genome sequence Phenotype ~3 gigabytes relationship capture EBI: repositories (petabytes of genome sequence data) Human sequence data Sanger: sequencing repositories (1000 genomes, uk10K) Steps in UK towards E-Health Research, Genomic Medicine • Health data to Research – 2006 Creation of OSCHR • Increase coordination between funders: MRC and NIHR – 2007 OSCHR E-health board • Enable research access to UK EHR data • Build capacity for research on EHR data • Genomics to Health – 2009 House of Lords report on Genomic Medicine – 2010 Creation of Human Genomic Strategy Group (HGSG) 2011: UK Life Sciences Strategy No10: http://www.number10.gov.uk/news/uk-life-sciences-get-government-cash-boost/ BIS/DH: http://www.dh.gov.uk/health/2011/12/nhs-adopting-innovation/ Linking Health data to Research Clinical Data -
Genetic Effects on Microsatellite Diversity in Wild Emmer Wheat (Triticum Dicoccoides) at the Yehudiyya Microsite, Israel
Heredity (2003) 90, 150–156 & 2003 Nature Publishing Group All rights reserved 0018-067X/03 $25.00 www.nature.com/hdy Genetic effects on microsatellite diversity in wild emmer wheat (Triticum dicoccoides) at the Yehudiyya microsite, Israel Y-C Li1,3, T Fahima1,MSRo¨der2, VM Kirzhner1, A Beiles1, AB Korol1 and E Nevo1 1Institute of Evolution, University of Haifa, Mount Carmel, Haifa 31905, Israel; 2Institute for Plant Genetics and Crop Plant Research, Corrensstrasse 3, 06466 Gatersleben, Germany This study investigated allele size constraints and clustering, diversity. Genome B appeared to have a larger average and genetic effects on microsatellite (simple sequence repeat number (ARN), but lower variance in repeat number 2 repeat, SSR) diversity at 28 loci comprising seven types of (sARN), and smaller number of alleles per locus than genome tandem repeated dinucleotide motifs in a natural population A. SSRs with compound motifs showed larger ARN than of wild emmer wheat, Triticum dicoccoides, from a shade vs those with perfect motifs. The effects of replication slippage sun microsite in Yehudiyya, northeast of the Sea of Galilee, and recombinational effects (eg, unequal crossing over) on Israel. It was found that allele distribution at SSR loci is SSR diversity varied with SSR motifs. Ecological stresses clustered and constrained with lower or higher boundary. (sun vs shade) may affect mutational mechanisms, influen- This may imply that SSR have functional significance and cing the level of SSR diversity by both processes. natural constraints. -
Gene Prediction and Genome Annotation
A Crash Course in Gene and Genome Annotation Lieven Sterck, Bioinformatics & Systems Biology VIB-UGent [email protected] ProCoGen Dissemination Workshop, Riga, 5 nov 2013 “Conifer sequencing: basic concepts in conifer genomics” “This Project is financially supported by the European Commission under the 7th Framework Programme” Genome annotation: finding the biological relevant features on a raw genomic sequence (in a high throughput manner) ProCoGen Dissemination Workshop, Riga, 5 nov 2013 Thx to: BSB - annotation team • Lieven Sterck (Ectocarpus, higher plants, conifers, … ) • Yao-cheng Lin (Fungi, conifers, …) • Stephane Rombauts (green alga, mites, …) • Bram Verhelst (green algae) • Pierre Rouzé • Yves Van de Peer ProCoGen Dissemination Workshop, Riga, 5 nov 2013 Annotation experience • Plant genomes : A.thaliana & relatives (e.g. A.lyrata), Poplar, Physcomitrella patens, Medicago, Tomato, Vitis, Apple, Eucalyptus, Zostera, Spruce, Oak, Orchids … • Fungal genomes: Laccaria bicolor, Melampsora laricis- populina, Heterobasidion, other basidiomycetes, Glomus intraradices, Pichia pastoris, Geotrichum Candidum, Candida ... • Algal genomes: Ostreococcus spp, Micromonas, Bathycoccus, Phaeodactylum (and other diatoms), E.hux, Ectocarpus, Amoebophrya … • Animal genomes: Tetranychus urticae, Brevipalpus spp (mites), ... ProCoGen Dissemination Workshop, Riga, 5 nov 2013 Why genome annotation? • Raw sequence data is not useful for most biologists • To be meaningful to them it has to be converted into biological significant knowledge -
Small Variants Frequently Asked Questions (FAQ) Updated September 2011
Small Variants Frequently Asked Questions (FAQ) Updated September 2011 Summary Information for each Genome .......................................................................................................... 3 How does Complete Genomics map reads and call variations? ........................................................................... 3 How do I assess the quality of a genome produced by Complete Genomics?................................................ 4 What is the difference between “Gross mapping yield” and “Both arms mapped yield” in the summary file? ............................................................................................................................................................................. 5 What are the definitions for Fully Called, Partially Called, Half-Called and No-Called?............................ 5 In the summary-[ASM-ID].tsv file, how is the number of homozygous SNPs calculated? ......................... 5 In the summary-[ASM-ID].tsv file, how is the number of heterozygous SNPs calculated? ....................... 5 In the summary-[ASM-ID].tsv file, how is the total number of SNPs calculated? .......................................... 5 In the summary-[ASM-ID].tsv file, what regions of the genome are included in the “exome”? .............. 6 In the summary-[ASM-ID].tsv file, how is the number of SNPs in the exome calculated? ......................... 6 In the summary-[ASM-ID].tsv file, how are variations in potentially redundant regions of the genome counted? ..................................................................................................................................................................... -
Functional Effects Detailed Research Plan
GeCIP Detailed Research Plan Form Background The Genomics England Clinical Interpretation Partnership (GeCIP) brings together researchers, clinicians and trainees from both academia and the NHS to analyse, refine and make new discoveries from the data from the 100,000 Genomes Project. The aims of the partnerships are: 1. To optimise: • clinical data and sample collection • clinical reporting • data validation and interpretation. 2. To improve understanding of the implications of genomic findings and improve the accuracy and reliability of information fed back to patients. To add to knowledge of the genetic basis of disease. 3. To provide a sustainable thriving training environment. The initial wave of GeCIP domains was announced in June 2015 following a first round of applications in January 2015. On the 18th June 2015 we invited the inaugurated GeCIP domains to develop more detailed research plans working closely with Genomics England. These will be used to ensure that the plans are complimentary and add real value across the GeCIP portfolio and address the aims and objectives of the 100,000 Genomes Project. They will be shared with the MRC, Wellcome Trust, NIHR and Cancer Research UK as existing members of the GeCIP Board to give advance warning and manage funding requests to maximise the funds available to each domain. However, formal applications will then be required to be submitted to individual funders. They will allow Genomics England to plan shared core analyses and the required research and computing infrastructure to support the proposed research. They will also form the basis of assessment by the Project’s Access Review Committee, to permit access to data. -
Genomics England and Sciencewise Evaluation of a Public Dialogue On
Genomics England and Sciencewise Evaluation of a public dialogue on Genomic Medicine: Time for a new social contract? Evaluation report June 2019 Quality Management URSUS Consulting Ltd has quality systems which have been assessed and approved to BS EN IS9001:2008 (certificate number GB2002687). Creation / Revision History Issue / revision: 3 Date: 19/6/2019 Prepared by: Anna MacGillivray Authorised by: Anna MacGillivray Project number: U.158 File reference: Genomics England/genomic medicine draft evaluation report 19.6.2019 URSUS CONSULTING LTD 57 Balfour Road London N5 2HD Tel. 07989 554 504 www.ursusconsulting.co.uk _________________________________________________________________________________________ URSUS CONSULTING GENOMICS ENGLAND AND SCIENCEWISE 2 Glossary of Acronyms ABI Association of British Insurers AI Artificial Intelligence APBI Association of British Pharmaceutical Industry BEIS (Department of) Business, Energy and Industrial Strategy BME Black and Minority Ethnic CMO Chief Medical Officer CSO (NHS) Chief Scientific Officer DA Devolved Administration DHSC Department of Health and Social Care FTE Full Time Equivalent GDPR General Data Protection Regulation GE Genomics England GG Generation Genome report GMS Genomic medicine service GMC General Medical Council NHS National Health Service OG Oversight Group REA Rapid Evidence Assessment SEG Socio economic group SGP Scottish Genomes Partnership SoS Secretary of State SSAC Scottish Science Advisory Committee SLT Senior Leadership Team UKRI UK Research and Innovation WGS Whole Genome Sequencing _________________________________________________________________________________________ URSUS CONSULTING GENOMICS ENGLAND AND SCIENCEWISE 3 EXECUTIVE SUMMARY Introduction This report of the independent evaluation of a public dialogue on Genomic Medicine: Time for a new social contract? has been prepared by URSUS Consulting Ltd on behalf of Genomics England (GE) and Sciencewise1. -
Rare Variant Contribution to Human Disease in 281,104 UK Biobank Exomes W 1,19 1,19 2,19 2 2 Quanli Wang , Ryan S
https://doi.org/10.1038/s41586-021-03855-y Accelerated Article Preview Rare variant contribution to human disease W in 281,104 UK Biobank exomes E VI Received: 3 November 2020 Quanli Wang, Ryan S. Dhindsa, Keren Carss, Andrew R. Harper, Abhishek N ag, I oa nn a Tachmazidou, Dimitrios Vitsios, Sri V. V. Deevi, Alex Mackay, EDaniel Muthas, Accepted: 28 July 2021 Michael Hühn, Sue Monkley, Henric O ls so n , S eb astian Wasilewski, Katherine R. Smith, Accelerated Article Preview Published Ruth March, Adam Platt, Carolina Haefliger & Slavé PetrovskiR online 10 August 2021 P Cite this article as: Wang, Q. et al. Rare variant This is a PDF fle of a peer-reviewed paper that has been accepted for publication. contribution to human disease in 281,104 UK Biobank exomes. Nature https:// Although unedited, the content has been subjectedE to preliminary formatting. Nature doi.org/10.1038/s41586-021-03855-y (2021). is providing this early version of the typeset paper as a service to our authors and Open access readers. The text and fgures will undergoL copyediting and a proof review before the paper is published in its fnal form. Please note that during the production process errors may be discovered which Ccould afect the content, and all legal disclaimers apply. TI R A D E T A R E L E C C A Nature | www.nature.com Article Rare variant contribution to human disease in 281,104 UK Biobank exomes W 1,19 1,19 2,19 2 2 https://doi.org/10.1038/s41586-021-03855-y Quanli Wang , Ryan S. -
The ELIXIR Core Data Resources: Fundamental Infrastructure for The
Supplementary Data: The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences The “Supporting Material” referred to within this Supplementary Data can be found in the Supporting.Material.CDR.infrastructure file, DOI: 10.5281/zenodo.2625247 (https://zenodo.org/record/2625247). Figure 1. Scale of the Core Data Resources Table S1. Data from which Figure 1 is derived: Year 2013 2014 2015 2016 2017 Data entries 765881651 997794559 1726529931 1853429002 2715599247 Monthly user/IP addresses 1700660 2109586 2413724 2502617 2867265 FTEs 270 292.65 295.65 289.7 311.2 Figure 1 includes data from the following Core Data Resources: ArrayExpress, BRENDA, CATH, ChEBI, ChEMBL, EGA, ENA, Ensembl, Ensembl Genomes, EuropePMC, HPA, IntAct /MINT , InterPro, PDBe, PRIDE, SILVA, STRING, UniProt ● Note that Ensembl’s compute infrastructure physically relocated in 2016, so “Users/IP address” data are not available for that year. In this case, the 2015 numbers were rolled forward to 2016. ● Note that STRING makes only minor releases in 2014 and 2016, in that the interactions are re-computed, but the number of “Data entries” remains unchanged. The major releases that change the number of “Data entries” happened in 2013 and 2015. So, for “Data entries” , the number for 2013 was rolled forward to 2014, and the number for 2015 was rolled forward to 2016. The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences 1 Figure 2: Usage of Core Data Resources in research The following steps were taken: 1. API calls were run on open access full text articles in Europe PMC to identify articles that mention Core Data Resource by name or include specific data record accession numbers. -
Open Targets Genetics
bioRxiv preprint doi: https://doi.org/10.1101/2020.09.16.299271; this version posted September 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 1 Open Targets Genetics: An open approach to systematically prioritize causal variants 2 and genes at all published GWAS trait-associated loci 3 4 Edward Mountjoy1,2, Ellen M. Schmidt1,2, Miguel Carmona2,3, Gareth Peat2,3, Alfredo Miranda2,3, 5 Luca Fumis2,3, James Hayhurst2,3, Annalisa Buniello2,3, Jeremy Schwartzentruber1,2,3, Mohd 6 Anisul Karim1,2, Daniel Wright1,2, Andrew Hercules2,3, Eliseo Papa4, Eric Fauman5, Jeffrey C. 7 Barrett1,2, John A. Todd6, David Ochoa2,3, Ian Dunham1,2,3, Maya Ghoussaini1,2,*. 8 9 1. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 10 1SA, UK 11 2. Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK 12 3. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), 13 Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK 14 4. Systems Biology, Biogen, Cambridge, MA, 02142, United States 15 5. Integrative Biology, Internal Medicine Research Unit, Pfizer Worldwide Research, 16 Development and Medical, Cambridge, MA 02139, United States 17 6. Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford 18 Biomedical Research Centre, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, 19 UK 20 * Corresponding author 21 22 23 24 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.16.299271; this version posted September 17, 2020.