Public Engagement Prizes 2020
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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. -
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. -
Methods in and Applications of the Sequencing of Short Non-Coding Rnas" (2013)
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2013 Methods in and Applications of the Sequencing of Short Non- Coding RNAs Paul Ryvkin University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Bioinformatics Commons, Genetics Commons, and the Molecular Biology Commons Recommended Citation Ryvkin, Paul, "Methods in and Applications of the Sequencing of Short Non-Coding RNAs" (2013). Publicly Accessible Penn Dissertations. 922. https://repository.upenn.edu/edissertations/922 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/922 For more information, please contact [email protected]. Methods in and Applications of the Sequencing of Short Non-Coding RNAs Abstract Short non-coding RNAs are important for all domains of life. With the advent of modern molecular biology their applicability to medicine has become apparent in settings ranging from diagonistic biomarkers to therapeutics and fields angingr from oncology to neurology. In addition, a critical, recent technological development is high-throughput sequencing of nucleic acids. The convergence of modern biotechnology with developments in RNA biology presents opportunities in both basic research and medical settings. Here I present two novel methods for leveraging high-throughput sequencing in the study of short non- coding RNAs, as well as a study in which they are applied to Alzheimer's Disease (AD). The computational methods presented here include High-throughput Annotation of Modified Ribonucleotides (HAMR), which enables researchers to detect post-transcriptional covalent modifications ot RNAs in a high-throughput manner. In addition, I describe Classification of RNAs by Analysis of Length (CoRAL), a computational method that allows researchers to characterize the pathways responsible for short non-coding RNA biogenesis. -
PDF of Connecting Science's 2017
Connecting Science Wellcome Genome Campus Hinxton, Cambridgeshire CB10 1RQ wellcomegenomecampus.org/connectingscience Annual Review 2017/18 2 02 Contents DIRECTO R’S INTRODUCTION Connecting Science in twelve months 04 – 05 Global ambitions Have you had your say on your DNA? 10 – 1 1 Increasing global impact by tailoring training needs 12 – 13 Worm hunting in Colombia 14 – 16 Catalysing collaboration Bringing together scientific partners 20 – 21 First global event for genetic counsellors 22 – 23 Decoding genomes together 24 – 26 Snapshot: May 2017 to April 2018 27 – 32 Democratising genomics CONNECTING SCIENCE Blood, sweat and success – Implementing a gender balance policy 36 – 37 2017/18 ANNUAL REVIEW Science communication: remembering to listen 38 – 39 Exploring our world in the Genome Gallery 40 – 41 The mission of Connecting Science is simple: to enable team, the commitment and support of our many partners and everyone to explore genomic science and its impact on collaborators, and the financial support and encouragement research, health and society. Behind those simple words is of Wellcome. I am personally very thankful for all of those Wellcome Genome Campus: considerable complexity. The science itself is complex and things, and know that this support and energy often translates ever-changing, with new technologies being continually into life- or career-changing experiences for the tens of A hub of knowledge, learning, developed and put into practice in both research and thousands of people we engage with directly every year. healthcare. The work that we do is also deceptively and engagement complex, reaching audiences from primary schools to I hope that some of the stories highlighted in this Annual research scientists, NHS staff to patients and their families; Review give you a sense of the excitement we have in all Creating spaces for thinking 46 – 47 it spans the globe, and everything we do involves constant that we do. -
The Metagenomic Data Life-Cycle: Standards and Best Practices Petra Ten Hoopen1, Robert D
GigaScience, 6, 2017, 1–11 doi: 10.1093/gigascience/gix047 Advance Access Publication Date: 16 June 2017 Review REVIEW Downloaded from https://academic.oup.com/gigascience/article-abstract/6/8/gix047/3869082 by guest on 01 October 2019 The metagenomic data life-cycle: standards and best practices Petra ten Hoopen1, Robert D. Finn1, Lars Ailo Bongo2, Erwan Corre3, Bruno Fosso4, Folker Meyer5, Alex Mitchell1, Eric Pelletier6,7,8, Graziano Pesole4,9, Monica Santamaria4, Nils Peder Willassen2 and Guy Cochrane1,∗ 1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom, 2UiT The Arctic University of Norway, Tromsø N-9037, Norway, 3CNRS-UPMC, FR 2424, Station Biologique, Roscoff 29680, France, 4Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, CNR, Bari 70126, Italy, 5Argonne National Laboratory, Argonne IL 60439, USA, 6Genoscope, CEA, Evry´ 91000, France, 7CNRS/UMR-8030, Evry´ 91000, France, 8Universite´ Evry´ val d’Essonne, Evry´ 91000, France and 9Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari “A. Moro,” Bari 70126, Italy ∗Correspondence address. Guy Cochrane, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK. Tel: +44(0)1223-494444; E-mail: [email protected] Abstract Metagenomics data analyses from independent studies can only be compared if the analysis workflows are described ina harmonized way. In this overview, we have mapped the landscape of data standards available for the description of essential steps in metagenomics: (i) material sampling, (ii) material sequencing, (iii) data analysis, and (iv) data archiving and publishing. Taking examples from marine research, we summarize essential variables used to describe material sampling processes and sequencing procedures in a metagenomics experiment. -
Comparing Tools for Non-Coding RNA Multiple Sequence Alignment Based On
Downloaded from rnajournal.cshlp.org on September 26, 2021 - Published by Cold Spring Harbor Laboratory Press ES Wright 1 1 TITLE 2 RNAconTest: Comparing tools for non-coding RNA multiple sequence alignment based on 3 structural consistency 4 Running title: RNAconTest: benchmarking comparative RNA programs 5 Author: Erik S. Wright1,* 6 1 Department of Biomedical Informatics, University of Pittsburgh (Pittsburgh, PA) 7 * Corresponding author: Erik S. Wright ([email protected]) 8 Keywords: Multiple sequence alignment, Secondary structure prediction, Benchmark, non- 9 coding RNA, Consensus secondary structure 10 Downloaded from rnajournal.cshlp.org on September 26, 2021 - Published by Cold Spring Harbor Laboratory Press ES Wright 2 11 ABSTRACT 12 The importance of non-coding RNA sequences has become increasingly clear over the past 13 decade. New RNA families are often detected and analyzed using comparative methods based on 14 multiple sequence alignments. Accordingly, a number of programs have been developed for 15 aligning and deriving secondary structures from sets of RNA sequences. Yet, the best tools for 16 these tasks remain unclear because existing benchmarks contain too few sequences belonging to 17 only a small number of RNA families. RNAconTest (RNA consistency test) is a new 18 benchmarking approach relying on the observation that secondary structure is often conserved 19 across highly divergent RNA sequences from the same family. RNAconTest scores multiple 20 sequence alignments based on the level of consistency among known secondary structures 21 belonging to reference sequences in their output alignment. Similarly, consensus secondary 22 structure predictions are scored according to their agreement with one or more known structures 23 in a family. -
Annual Scientific Report 2013 on the Cover Structure 3Fof in the Protein Data Bank, Determined by Laponogov, I
EMBL-European Bioinformatics Institute Annual Scientific Report 2013 On the cover Structure 3fof in the Protein Data Bank, determined by Laponogov, I. et al. (2009) Structural insight into the quinolone-DNA cleavage complex of type IIA topoisomerases. Nature Structural & Molecular Biology 16, 667-669. © 2014 European Molecular Biology Laboratory This publication was produced by the External Relations team at the European Bioinformatics Institute (EMBL-EBI) A digital version of the brochure can be found at www.ebi.ac.uk/about/brochures For more information about EMBL-EBI please contact: [email protected] Contents Introduction & overview 3 Services 8 Genes, genomes and variation 8 Molecular atlas 12 Proteins and protein families 14 Molecular and cellular structures 18 Chemical biology 20 Molecular systems 22 Cross-domain tools and resources 24 Research 26 Support 32 ELIXIR 36 Facts and figures 38 Funding & resource allocation 38 Growth of core resources 40 Collaborations 42 Our staff in 2013 44 Scientific advisory committees 46 Major database collaborations 50 Publications 52 Organisation of EMBL-EBI leadership 61 2013 EMBL-EBI Annual Scientific Report 1 Foreword Welcome to EMBL-EBI’s 2013 Annual Scientific Report. Here we look back on our major achievements during the year, reflecting on the delivery of our world-class services, research, training, industry collaboration and European coordination of life-science data. The past year has been one full of exciting changes, both scientifically and organisationally. We unveiled a new website that helps users explore our resources more seamlessly, saw the publication of ground-breaking work in data storage and synthetic biology, joined the global alliance for global health, built important new relationships with our partners in industry and celebrated the launch of ELIXIR. -
A Unicellular Relative of Animals Generates a Layer of Polarized Cells
RESEARCH ARTICLE A unicellular relative of animals generates a layer of polarized cells by actomyosin- dependent cellularization Omaya Dudin1†*, Andrej Ondracka1†, Xavier Grau-Bove´ 1,2, Arthur AB Haraldsen3, Atsushi Toyoda4, Hiroshi Suga5, Jon Bra˚ te3, In˜ aki Ruiz-Trillo1,6,7* 1Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), Barcelona, Spain; 2Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom; 3Section for Genetics and Evolutionary Biology (EVOGENE), Department of Biosciences, University of Oslo, Oslo, Norway; 4Department of Genomics and Evolutionary Biology, National Institute of Genetics, Mishima, Japan; 5Faculty of Life and Environmental Sciences, Prefectural University of Hiroshima, Hiroshima, Japan; 6Departament de Gene`tica, Microbiologia i Estadı´stica, Universitat de Barcelona, Barcelona, Spain; 7ICREA, Barcelona, Spain Abstract In animals, cellularization of a coenocyte is a specialized form of cytokinesis that results in the formation of a polarized epithelium during early embryonic development. It is characterized by coordinated assembly of an actomyosin network, which drives inward membrane invaginations. However, whether coordinated cellularization driven by membrane invagination exists outside animals is not known. To that end, we investigate cellularization in the ichthyosporean Sphaeroforma arctica, a close unicellular relative of animals. We show that the process of cellularization involves coordinated inward plasma membrane invaginations dependent on an *For correspondence: actomyosin network and reveal the temporal order of its assembly. This leads to the formation of a [email protected] (OD); polarized layer of cells resembling an epithelium. We show that this stage is associated with tightly [email protected] (IR-T) regulated transcriptional activation of genes involved in cell adhesion. -
Strategic Plan 2011-2016
Strategic Plan 2011-2016 Wellcome Trust Sanger Institute Strategic Plan 2011-2016 Mission The Wellcome Trust Sanger Institute uses genome sequences to advance understanding of the biology of humans and pathogens in order to improve human health. -i- Wellcome Trust Sanger Institute Strategic Plan 2011-2016 - ii - Wellcome Trust Sanger Institute Strategic Plan 2011-2016 CONTENTS Foreword ....................................................................................................................................1 Overview .....................................................................................................................................2 1. History and philosophy ............................................................................................................ 5 2. Organisation of the science ..................................................................................................... 5 3. Developments in the scientific portfolio ................................................................................... 7 4. Summary of the Scientific Programmes 2011 – 2016 .............................................................. 8 4.1 Cancer Genetics and Genomics ................................................................................ 8 4.2 Human Genetics ...................................................................................................... 10 4.3 Pathogen Variation .................................................................................................. 13 4.4 Malaria -
The Pfam Protein Families Database Marco Punta1,*, Penny C
D290–D301 Nucleic Acids Research, 2012, Vol. 40, Database issue Published online 29 November 2011 doi:10.1093/nar/gkr1065 The Pfam protein families database Marco Punta1,*, Penny C. Coggill1, Ruth Y. Eberhardt1, Jaina Mistry1, John Tate1, Chris Boursnell1, Ningze Pang1, Kristoffer Forslund2, Goran Ceric3, Jody Clements3, Andreas Heger4, Liisa Holm5, Erik L. L. Sonnhammer2, Sean R. Eddy3, Alex Bateman1 and Robert D. Finn3 1Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK, 2Stockholm Bioinformatics Center, Swedish eScience Research Center, Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Box 1031, SE-17121 Solna, Sweden, 3HHMI Janelia Farm Research Campus, 19700 Helix Drive, Ashburn, VA 20147, USA, 4Department of Physiology, Anatomy and Genetics, MRC Functional Genomics Unit, University of Oxford, Oxford, OX1 3QX, UK and 5Institute of Biotechnology and Department of Biological and Environmental Sciences, University of Helsinki, PO Box 56 Downloaded from (Viikinkaari 5), 00014 Helsinki, Finland Received October 7, 2011; Revised October 26, 2011; Accepted October 27, 2011 http://nar.oxfordjournals.org/ ABSTRACT INTRODUCTION Pfam is a widely used database of protein families, Pfam is a database of protein families, where families are currently containing more than 13 000 manually sets of protein regions that share a significant degree of curated protein families as of release 26.0. Pfam is sequence similarity, thereby suggesting homology. available via servers in the UK (http://pfam.sanger Similarity is detected using the HMMER3 (http:// hmmer.janelia.org/) suite of programs. .ac.uk/), the USA (http://pfam.janelia.org/) and Pfam contains two types of families: high quality, Sweden (http://pfam.sbc.su.se/). -
5 February 2020 Draft Programme
Genomic Practice for Genetic Counsellors Wellcome Genome Campus Hinxton, Cambridge, UK 3 - 5 February 2020 Draft Programme Monday 3 February 11:00 – 11:00 Registration with coffee 11:30 – 12:00 Welcome and introduction to the course 12:00 – 13:45 Session 1: The role of genomics in healthcare The role in the NHS Nicki Taverner Cardiff University and All Wales Medical Genetic Service, UK Catherine Houghton Liverpool Women's NHS Foundation Trust, UK Outside the NHS Gemma Chandratilake University of Cambridge, UK An international perspective – Melbourne Genomics Health Alliance Lyndon Gallacher Victorian Clinicial Genetics Service, Australia Q&A with speakers 13:45 – 14:45 Lunch 14:45 – 16:00 Session 2: Variant interpretation Introduction to a genome browser Gemma Chandratillake University of Cambridge, UK Variant interpretation: going from millions to one of interest that could be the answer Helen Firth Cambridge University Hospitals, UK 16:00 – 16:20 Afternoon tea 16:20 – 18.20 Workshop: Variant interpretation using DECIPHER {and other approaches} Julia Foreman WSI, UK + Gemma Chandratillake University of Cambridge, UK 18:29 – 19:00 Consolidating learning for Day 1, Q+A Led by programme committee 19:00 Dinner Tuesday 4 February 09:00 – 10:00 Session 3: Cancer genomics Cancer Genomics: bridging from the tumour to the germline in variant interpretation Clare Turnbull The Institute of Cancer Research, UK 10:00 – 12:00 Workshop: Cancer variant interpretation Heather Pierce Cambridge University Hospitals, UK 12:00 – 13:00 Functional studies -
MEROPS: the Peptidase Database Neil D
Published online 5 November 2009 Nucleic Acids Research, 2010, Vol. 38, Database issue D227–D233 doi:10.1093/nar/gkp971 MEROPS: the peptidase database Neil D. Rawlings*, Alan J. Barrett and Alex Bateman The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, UK Received September 11, 2009; Revised October 12, 2009; Accepted October 14, 2009 ABSTRACT which are grouped into clans. A family contains proteins that can be shown to be related by sequence comparison Peptidases, their substrates and inhibitors are of alone, whereas a clan contains proteins where the great relevance to biology, medicine and biotech- sequences are so distantly related that similarity can nology. The MEROPS database (http://merops only be seen by comparing structures. Sequence analysis .sanger.ac.uk) aims to fulfil the need for an is restricted to that portion of the protein directly respon- integrated source of information about these. sible for peptidase or inhibitor activity which is termed the The database has a hierarchical classification ‘peptidase unit’ or the ‘inhibitor unit’, respectively. in which homologous sets of peptidases and A peptidase or inhibitor unit will normally correspond protein inhibitors are grouped into protein species, to a structural domain, and some proteins will contain which are grouped into families, which are in turn more than one peptidase or inhibitor domain. Examples grouped into clans. The classification framework is are potato virus Y polyprotein which contains three peptidase units, each in a different family, and turkey used for attaching information at each level. ovomucoid, which contains three inhibitor units all in An important focus of the database has become the same family.