DOCSLIB.ORG

Progress in Protein Structure Prediction

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Progress in Protein Structure Prediction © 2001 Nature Publishing Group http://structbio.nature.com meeting review Progress in protein structure prediction Alexey G. Murzin The series of four CASP experiments has helped to transform the field of protein structure prediction. The state of the art in protein structure prediction has undoubtedly changed, but has there been progress over the years? “Now, here, you see, it takes all the running acid residues, but all earlier attempts to gories. In CASP4, there were just 43 tar- you can do, to keep in the same place. If you compute the structure ab initio using gets, the same number as in CASP3. On want to get somewhere else, you must run at nothing else but the sequence and the laws the positive side, 40 of the CASP4 targets least twice as fast as that!” of physics and chemistry had very limited had their experimental structures deter- The Red Queen to Alice in Through the if any success. However, there were other mined in time and thus were available for Looking-Glass by Lewis Carroll methods available that used additional the assessment, four more than were avail- information. The observation of homolo- able in CASP3. Unlike many of the conferences in the past gous proteins having very similar struc- year, this meeting did not have ‘2000’ in its tures allowed the comparative modeling Assessment of predictions name. The CASP4* organizers, John of protein structure by sequence homolo- The assessors’ burden of interpreting the Moult (CARB, University of Maryland, gy to known structure. The notion that results was heavier than ever this year. To USA), Tim Hubbard (Sanger Centre, Nature may operate with a limited reper- deal with it, each of the three principal Cambridge, UK), Krzysztof Fidelis and toire of protein folds gave rise to the fold assessors requested the assistance of Adam Zemla (both of the Prediction recognition methods searching for a cor- his/her colleagues. Anna Tramontano Center, Lawrence Livermore National relation between a given sequence and a (IRBM, Pomezia, Italy), the co-organizer Laboratory, USA), did not need to follow known fold. A substantial fraction of the of two FEBS advanced courses on the the millennium year fashion to attract due target proteins were expected to have new Frontiers of Protein Structure Prediction attention. CASP4 was more than just a sci- protein folds thus providing the opportu- in 1995 and 1997, was in charge of the entific conference. It was a convention of nity of a double blind test of the ab initio comparative modeling assessment. dedicated people who spent their summer methods by eliminating possible insights Manfred Sippl (University of Salzburg, working on the predictions of a new batch from the fold recognition methods. Austria), the leader of one of the most suc- of protein structures and then gathered The boundaries between the categories cessful predictors teams of the three previ- together to take one more step in the were somewhat arbitrary and have ous CASPs, carried out the fold © http://structbio.nature.com Group 2001 Nature Publishing ongoing CASP experiment. This experi- changed from one CASP experiment to recognition assessment with help of his ment, designed by John Moult and col- another. Also, the CASP process stimulat- group. The former CASP2 assessor in the leagues seven years ago, has been run ed the development of new approaches to ab initio methods category Arthur Lesk every other year, in 1994, 1996, 1998 and protein structure predictions, for exam- (University of Cambridge, UK) made a 2000. The details of all CASP experiments ple, the knowledge-based methods that comeback as the principal assessor in the are available online (http://Prediction could predict across the different cate- renamed category of new fold methods. Center.llnl.gov/). The CASP4 proceedings gories. To make the assessment as fair as The renaming from ab initio was prompt- will be published in a special issue of the possible to all prediction methods, after ed by the development of new prediction journal Proteins later this year, where the CASP2, the prediction formats were uni- methods that assemble a protein fold from proceedings of the first three CASP fied for all categories, and the category of small parts using both the first principles experiments have been published1–3. each target was decided upon the assess- and empirical rules derived from known ment of submitted predictions. The structures. The assessors had just two Description of the experiment assessment was carried out by indepen- months before the final meeting to com- The main objective of CASP was to subject dent assessors invited to analyze the pre- plete their analyses of more than 11,000 the available prediction methods to a dictions in each category and to nominate predictions submitted by 163 predictor blind test. The participants in the experi- the best predictors for the presentation of teams and automated servers. Although ment were asked to predict a number of their methods at the final meeting. the assessment was facilitated by the structures that were about to be deter- The original CASP design has with- numerical evaluation data generated in mined by X-ray crystallography and NMR stood the test of time. It has not changed the Prediction Center and by the assessors’ spectroscopy. The target protein significantly since the first CASP experi- own software, it was partly manual labor sequences were solicited from experimen- ment, neither has the format, timing or and extremely time-consuming (M. Sippl talists. The targets were divided into three location of the final CASP meetings. The estimated that the fold recognition assess- different categories: comparative model- experiment has been a success among the ment had required ~25 person-weeks ing, fold recognition and ab initio meth- predictor community, each time attract- working time). In the fold recognition and ods. ing more and more participants and gen- new fold methods categories, manual This division reflected the status of the erating more and more predictions (Table inspection was needed for the identifica- field at the beginning of the experiment in 1). In contrast, target collection, which tion of partially correct predictions that 1994. At that time, it had been well recog- depends on the genorosity of experimen- do not stand out in the numerical evalua- nized that the structure of a protein is fully talists, failed again to reach an optimal tion tables and plots. In the comparative determined by the sequence of its amino total number of 100 targets in all cate- modeling category, where almost all pre- 110 nature structural biology • volume 8 number 2 • february 2001 © 2001 Nature Publishing Group http://structbio.nature.com meeting review Table 1 CASP process in numbers CASP number Year Number of targets Number of predictor teams Total number of predictions CASP1 1994 33 35 135 CASP2 1996 42 72 947 CASP3 1998 43 98 3,807 CASP4 2000 43 163 11,136 dicted structures were sufficiently close to targets, neither are the statistical results of major developments outside the CASP the experimental structure, the focus of different CASPs. process that transformed the field of pro- manual inspection was on the prediction tein structure prediction, particularly in of fine details. Development in structure prediction the fold recognition category, arguably the The assessors also were asked to address The CASP process has brought a strong ele- most interesting in the field. Unlike the the following question: has there been ment of competition to the field, in partic- ab initio/new fold methods category, suc- progress in comparison to the earlier ular to the fold recognition category, where cessful predictions in the fold recognition CASPs? Two of the three principal asses- the number of correctly assigned folds is a category could result in substantially com- sors answered positively. The comparative simple criterion for a team’s success. In the plete structures; unlike comparative mod- modeling assessor was less certain, as there earlier CASPs, only a few prediction teams eling, correct fold assignment was far was no substantial difference in the quality consistently made a substantial number of from trivial. In the early days of CASP, the of CASP4 and CASP3 models. But the correct assignments. In CASP4, there were fold recognition category was dominated same could probably be said about the pre- many more teams that showed similar by methods that thread the sequence in dictions in the other two categories. At first good performances over a wide range of question through a library of known pro- glance the CASP4 and CASP3 results look targets, so this time the assessors measured tein folds, and the terms ‘threading’ and quite similar. In both CASP3 and CASP4, the team’s success by quality rather than ‘fold recognition’ were synonymous. In there were many correct predictions for quantity of the team’s correct predictions. CASP2, a major blow to the superiority of some targets, whereas for other targets In pursuit of the best result rather than the threading methods came from the knowl- there were just a few good predictions, and best method, the approaches used by dif- edge-based methods brought up by the a few targets were missed completely. ferent teams have begun to converge over creation of databases of proteins of known The non-uniform distribution of suc- the years. Many predictors used combina- structure, such as SCOP (the Structural cessful predictions could be explained by tions of different techniques rather than a Classification of Proteins database). The the variation of prediction difficulty. With single method to improve their perfor- coming of multiple sequence alignment- increasing prediction difficulty, both the mances. based similarity searches, like PSI-BLAST, number of correct predictions and the The CASP4 predictors were also able to made a big impact in CASP3.
Load more

Recommended publications
  • Predicting and Characterising Protein-Protein Complexes
    Predicting and Characterising Protein-Protein Complexes Iain Hervé Moal June 2011 Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute and Department of Biochemistry and Molecular Biology, University College London A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Biochemistry at the University College London. 2 I, Iain Hervé Moal, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Abstract Macromolecular interactions play a key role in all life processes. The con- struction and annotation of protein interaction networks is pivotal for the understanding of these processes, and how their perturbation leads to dis- ease. However the extent of the human interactome and the limitations of the experimental techniques which can be brought to bear upon it necessit- ate theoretical approaches. Presented here are computational investigations into the interactions between biological macromolecules, focusing on the structural prediction of interactions, docking, and their kinetic and thermo- dynamic characterisation via empirical functions. Firstly, the use of normal modes in docking is investigated. Vibrational analysis of proteins are shown to indicate the motions which proteins are intrinsically disposed to under- take, and the use of this information to model flexible deformations upon protein-protein binding is evaluated. Subsequently SwarmDock, a docking algorithm which models flexibility as a linear combination of normal modes, is presented and benchmarked on a wide variety of test cases. This algorithm utilises state of the art energy functions and metaheuristics to navigate the free energy landscape.
    [Show full text]
  • 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.
    [Show full text]
  • Development of Novel Strategies for Template-Based Protein Structure Prediction
    Imperial College London Department of Life Sciences PhD Thesis Development of novel strategies for template-based protein structure prediction Stefans Mezulis supervised by Prof. Michael Sternberg Prof. William Knottenbelt September 2016 Abstract The most successful methods for predicting the structure of a protein from its sequence rely on identifying homologous sequences with a known struc- ture and building a model from these structures. A key component of these homology modelling pipelines is a model combination method, responsible for combining homologous structures into a coherent whole. Presented in this thesis is poing2, a model combination method using physics-, knowledge- and template-based constraints to assemble proteins us- ing information from known structures. By combining intrinsic bond length, angle and torsional constraints with long- and short-range information ex- tracted from template structures, poing2 assembles simplified protein models using molecular dynamics algorithms. Compared to the widely-used model combination tool MODELLER, poing2 is able to assemble models of ap- proximately equal quality. When supplied only with poor quality templates or templates that do not cover the majority of the query sequence, poing2 significantly outperforms MODELLER. Additionally presented in this work is PhyreStorm, a tool for quickly and accurately aligning the three-dimensional structure of a query protein with the Protein Data Bank (PDB). The PhyreStorm web server provides comprehensive, current and rapid structural comparisons to the protein data bank, providing researchers with another tool from which a range of biological insights may be drawn. By partitioning the PDB into clusters of similar structures and performing an initial alignment to the representatives of each cluster, PhyreStorm is able to quickly determine which structures should be excluded from the alignment.
    [Show full text]
  • Centre for Bioinformatics Imperial College London
    Centre for Bioinformatics Imperial College London Inaugural Report January 2003 Centre Director: Prof Michael Sternberg www.imperial.ac.uk/bioinformatics [email protected] Support Service Head: Dr Sarah Butcher www.codon.bioinformatics.ic.ac.uk [email protected] Centre for Bioinformatics - Imperial College London - Inaugural Report - January 2003 1 Contents Summary..................................................................................................................... 3 1. Background and Objectives .................................................................................... 4 1.1 Background ....................................................................................................... 4 1.2 Objectives of the Centre for Bioinformatics ....................................................... 4 1.3 Objectives of the Bioinformatics Support Service.............................................. 5 1.4 Historical Notes ................................................................................................. 5 2. Management ........................................................................................................... 6 3. Biographies of the Team......................................................................................... 7 4. Bioinformatics Research at Imperial ....................................................................... 8 4.1 Affiliates of the Centre ....................................................................................... 8 4.2 Research Grants
    [Show full text]
  • Centre for Bioinformatics Imperial College London Second Report 31
    Centre for Bioinformatics Imperial College London Second Report 31 May 2004 Centre Director: Prof Michael Sternberg www.imperial.ac.uk/bioinformatics [email protected] Support Service Head: Dr Sarah Butcher www.codon.bioinformatics.ic.ac.uk [email protected] Centre for Bioinformatics - Imperial College London - Second Report - May 2004 1 Contents Summary..................................................................................................................... 3 1. Background and Objectives .................................................................................. 4 1.1 Objectives of the Report........................................................................................ 4 1.2 Background........................................................................................................... 4 1.3 Objectives of the Centre for Bioinformatics........................................................... 5 1.4 Objectives of the Bioinformatics Support Service ................................................. 5 2. Management ......................................................................................................... 6 3. Biographies of the Team....................................................................................... 7 4. Bioinformatics Research at Imperial ..................................................................... 8 4.1 Affiliates of the Centre........................................................................................... 8 4.2 Research..............................................................................................................
    [Show full text]
  • Spinout Equinox Pharma Speeds up and Reduces the Cost of Drug Discovery
    Spinout Equinox Pharma speeds up and reduces the cost of drug discovery pinout Equinox Pharma1 provides a service to the pharma and biopharma industries that helps in the development of new drugs. The company, established by researchers from Imperial College London in IMPACT SUMMARY 2008, is using its own proprietary software to help speed up and reduce the cost of discovery processes. S Spinout Equinox Pharma provides services to the agri- tech and pharmaceuticals industries to accelerate the Equinox is based on BBSRC-funded collaborative research The technology also has applications in the agrochemical discovery of molecules that could form the basis of new conducted by Professors Stephen Muggleton2, Royal sector, and is being used by global agri-tech company products such as drugs or herbicides. Academy Chair in Machine Learning, Mike Sternberg3, Chair Syngenta. The company arose from BBSRC bioinformatics and of Structural Bioinformatics, and Paul Freemont4, Chair of structural biology research at Imperial College London. Protein Crystallography, at Imperial College London. “The power of a logic-based approach is that it can Technology commercialisation company NetScientific propose chemically-novel molecules that can have Ltd has invested £250K in Equinox. The technology developed by the company takes a logic- enhanced properties and are able to be the subject of novel Equinox customers include major pharmaceutical based approach to discover novel drugs, using computers to ‘composition of matter’ patents,” says Muggleton. companies such as Japanese pharmaceutical company learn from a set of biologically-active molecules. It combines Astellas and agro chemical companies such as knowledge about traditional drug discovery methods and Proving the technology Syngenta, as well as SMEs based in the UK, Europe, computer-based analyses, and focuses on the key stages in A three-year BBSRC grant in 2003 funded the work of Japan and the USA.
    [Show full text]
  • Virtual Screening of Human Class-A Gpcrs Using Ligand Profiles Built on Multiple Ligand-Receptor Interactions
    Article Virtual Screening of Human Class-A GPCRs Using Ligand Profiles Built on Multiple Ligand–Receptor Interactions Wallace K.B. Chan 1,2 and Yang Zhang 1,3 1 - Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA 2 - Department of Pharmacology, University of Michigan, Ann Arbor, MI 48109, USA 3 - Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Correspondence to Yang Zhang: Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218, USA. [email protected] https://doi.org/10.1016/j.jmb.2020.07.003 Edited by Michael Sternberg Abstract G protein-coupled receptors (GPCRs) are a large family of integral membrane proteins responsible for cellular signal transductions. Identification of therapeutic compounds to regulate physiological processes is an important first step of drug discovery. We proposed MAGELLAN, a novel hierarchical virtual-screening (VS) pipeline, which starts with low-resolution protein structure prediction and structure-based binding-site identification, followed by homologous GPCR detections through structure and orthosteric binding-site comparisons. Ligand profiles constructed from the homologous ligand–GPCR complexes are then used to thread through compound databases for VS. The pipeline was first tested in a large-scale retrospective screening experiment against 224 human Class A GPCRs, where MAGELLAN achieved a median enrichment factor (EF) of 14.38, significantly higher than that using individual ligand profiles. Next, MAGELLAN was examined on 5 and 20 GPCRs from two public VS databases (DUD-E and GPCR-Bench) and resulted in an average EF of 9.75 and 13.70, respectively, which compare favorably with other state-of- the-art docking- and ligand-based methods, including AutoDock Vina (with EF = 1.48/3.16 in DUD-E and GPCR-Bench), DOCK 6 (2.12/3.47 in DUD-E and GPCR-Bench), PoLi (2.2 in DUD-E), and FINDSITEC- comb2.0 (2.90 in DUD-E).
    [Show full text]
  • A Composite Approach to Protein Structure Evolution
    COMPARATIVE QUANTITATIVE GENETICS OF PROTEIN STRUCTURES: A COMPOSITE APPROACH TO PROTEIN STRUCTURE EVOLUTION by Jose Sergio Hleap Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Dalhousie University Halifax, Nova Scotia December 2015 c Copyright by Jose Sergio Hleap, 2015 To my parents for all the support and the drive they instilled on me To my girlfriend for all the love and support during this process To my supervisor for the guidance provided And last but not least, To whomever had the patience to read this document ii Table of Contents Abstract ...................................... vi List of Abbreviations Used .......................... vii Acknowledgements ............................... x Chapter 1 Introduction .......................... 1 Chapter 2 Morphometrics of protein structures: Geometric mor- phometric approach to protein structure evaluation .. 4 2.1GMmethodsinproteinstructures.................... 8 2.1.1 Abstractingaproteinstructureasashape........... 8 2.1.2 GPSvsProteinstructurealignment............... 9 2.1.3 FormDifferenceinproteinstructures.............. 14 2.2 Applicability of GM-like methods .................... 20 2.2.1 Statistical analysis of the α-Amylase evolutionary variation . 21 2.2.2 StatisticalanalysisoftheNPC1proteinsimulation...... 29 Chapter 3 Defining structural and evolutionary modules in pro- teins: A community detection approach to explore sub- domain architecture ...................... 34 3.1 Landmark definition ........................... 37 3.2 Contact
    [Show full text]
  • Drosophila Melanogaster
    ORTHOLOGOUS PAIR TRANSFER AND HYBRID BAYES METHODS TO PREDICT THE PROTEIN-PROTEIN INTERACTION NETWORK OF THE ANOPHELES GAMBIAE MOSQUITOES By Qiuxiang Li SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY AT IMPERIAL COLLEGE LONDON SOUTH KENSINGTON, LONDON SW7 2AZ 2008 °c Copyright by Qiuxiang Li, 2008 IMPERIAL COLLEGE LONDON DIVISION OF CELL & MOLECULAR BIOLOGY The undersigned hereby certify that they have read and recommend to the Faculty of Natural Sciences for acceptance a thesis entitled \Orthologous pair transfer and hybrid Bayes methods to predict the protein-protein interaction network of the Anopheles gambiae mosquitoes" by Qiuxiang Li in partial ful¯llment of the requirements for the degree of Doctor of Philosophy. Dated: 2008 Research Supervisors: Professor Andrea Crisanti Professor Stephen H. Muggleton Examining Committee: Dr. Michael Tristem Dr. Tania Dottorini ii IMPERIAL COLLEGE LONDON Date: 2008 Author: Qiuxiang Li Title: Orthologous pair transfer and hybrid Bayes methods to predict the protein-protein interaction network of the Anopheles gambiae mosquitoes Division: Cell & Molecular Biology Degree: Ph.D. Convocation: March Year: 2008 Permission is herewith granted to Imperial College London to circulate and to have copied for non-commercial purposes, at its discretion, the above title upon the request of individuals or institutions. Signature of Author THE AUTHOR RESERVES OTHER PUBLICATION RIGHTS, AND NEITHER THE THESIS NOR EXTENSIVE EXTRACTS FROM IT MAY BE PRINTED OR OTHERWISE REPRODUCED WITHOUT THE AUTHOR'S WRITTEN PERMISSION. THE AUTHOR ATTESTS THAT PERMISSION HAS BEEN OBTAINED FOR THE USE OF ANY COPYRIGHTED MATERIAL APPEARING IN THIS THESIS (OTHER THAN BRIEF EXCERPTS REQUIRING ONLY PROPER ACKNOWLEDGEMENT IN SCHOLARLY WRITING) AND THAT ALL SUCH USE IS CLEARLY ACKNOWLEDGED.
    [Show full text]
  • Erepo-ORP: Exploring the Opportunity Space to Combat Orphan Diseases with Existing Drugs
    Databases/ Web Servers eRepo-ORP: Exploring the Opportunity Space to Combat Orphan Diseases with Existing Drugs Michal Brylinski 1,2, Misagh Naderi 1, Rajiv Gandhi Govindaraj 1 and Jeffrey Lemoine 1,3 1 - Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA 2 - Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA 3 - Division of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA 70803, USA Correspondence to Michal Brylinski: Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA. [email protected] https://doi.org/10.1016/j.jmb.2017.12.001 Edited by Michael Sternberg Abstract About 7000 rare, or orphan, diseases affect more than 350 million people worldwide. Although these conditions collectively pose significant health care problems, drug companies seldom develop drugs for orphan diseases due to extremely limited individual markets. Consequently, developing new treatments for often life-threatening orphan diseases is primarily contingent on financial incentives from governments, special research grants, and private philanthropy. Computer-aided drug repositioning is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Here, we present eRepo-ORP, a comprehensive resource constructed by a large-scale repositioning of existing drugs to orphan diseases with a collection of structural bioinformatics tools, including eThread, eFindSite, and eMatchSite. Specifically, a systematic exploration of 320,856 possible links between known drugs in DrugBank and orphan proteins obtained from Orphanet reveals as many as 18,145 candidates for repurposing. In order to illustrate how potential therapeutics for rare diseases can be identified with eRepo-ORP, we discuss the repositioning of a kinase inhibitor for Ras-associated autoimmune leukoproliferative disease.
    [Show full text]
  • Annual Review 2003
    The Wellcome Trust is an independent – 30 September 2003 1 October 2002 Review Annual Trust Wellcome The research-funding charity, established under the will of Sir Henry Wellcome in 1936. Annual It is funded from a private endowment, which is managed with long-term stability and growth in mind. Review Its mission is to foster and promote research with the aim of improving human and animal health. Its work covers four areas: 2003 Knowledge – improving our understanding of human and animal biology in health and disease, and of the past and present role of medicine in society. Resources – providing exceptional researchers with the infrastructural and career support they need to fulfil their potential. Translation – ensuring maximum health benefits are gained from biomedical research. Public engagement – raising awareness of the medical, ethical and social implications of biomedical science. www.wellcome.ac.uk Annual Review 2003 Contents 2 From the Director 6 Making a difference 8 Financial summary 10 Knowledge 16 Resources Advancing knowledge and Contributing to a long-term and understanding in the biomedical vibrant research environment. sciences and their impact on society – past, present and future. 22 Translation 28 Public engagement Advancing the translation of Trust- Engaging with the public through funded research into health benefits. informed dialogue. The cover images are of objects displayed at the ‘Medicine Man’ exhibition at the British Museum in 2003.The exhibition commemorated the 150th anniversary of the birth of Henry Wellcome. Front cover Back cover, left Back cover, right Yoruban figures representing Protective amulet said to Tobacco resuscitator kit. 34 A year at the Trust deceased twins, 1870–1910.
    [Show full text]
  • ELIXIR UK Node
    ELIXIR‐UK the UK Node of the European Life Sciences Infrastructure for Biological Information www.elixir‐europe.org elixir‐uk.org The USA 2 EUROPE 3 UK: Poor data quality hindering government open data programme • “A Computer Weekly analysis of 50 spending data releases by the Cabinet Office since May 2010 has shown they were so marred by "dirty data" and inconsistent computer encoding, systematic scrutiny would require advanced computer programming skills.” Thursday 28 August 2014 http://www.computerweekly.com/news/2240227682/Poor‐data‐quality‐ hindering‐government‐open‐data‐transparency‐programme 4 Establishing a fresh UK activity Oxford University Computational Genomics Analysis and Training (CGAT) €1.6million The University of Manchester Seed fund The Oxford e‐Research Centre European Bioinformatics Institute (EMBL‐EBI) in kind University of Cardiff & NERC EOS Centre funding The Genome Analysis Centre (TGAC) University College London University of Birmingham University of Edinburgh Queen Mary, London University of Cambridge University of Liverpool Centre for Genomic Medicine Harness existing expertise Core Staff TeSS Staff Train across the Spectrum Technical Life Science Infrastructure Researchers Service Providers TCRS TCIT Training Training Coordinator, Coordinator, Research Infrastructure Science Technology Lee Larcombe Aleks Pawlik ELIXIR‐UK working across Europe: Training Coordination Group Mission: to establish an interacting ELIXIR wide training community & to ensure coherency in the delivery of training related to ELIXIR activities. TrCG members: Chair: Rita Hendricusdottir BE Katrijn Vannerum CZ Daniel Svozil DK Peter Longreen EE Hedi Peterson FI Eija Korpelainen FR Julie Thompson IL Michal Linial IT Allegra Via NL Celia van Gelder NO Ståle Nygård PT Pedro Fernandes SI Brane L.
    [Show full text]