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Original Article Text Mining in the Biocuration Workflow: Applications for Literature Curation at Wormbase, Dictybase and TAIR
Database, Vol. 2012, Article ID bas040, doi:10.1093/database/bas040 ............................................................................................................................................................................................................................................................................................. Original article Text mining in the biocuration workflow: applications for literature curation at WormBase, dictyBase and TAIR Kimberly Van Auken1,*, Petra Fey2, Tanya Z. Berardini3, Robert Dodson2, Laurel Cooper4, Donghui Li3, Juancarlos Chan1, Yuling Li1, Siddhartha Basu2, Hans-Michael Muller1, Downloaded from Rex Chisholm2, Eva Huala3, Paul W. Sternberg1,5 and the WormBase Consortium 1Division of Biology, California Institute of Technology, 1200 E. California Boulevard, Pasadena, CA 91125, 2Northwestern University Biomedical Informatics Center and Center for Genetic Medicine, 420 E. Superior Street, Chicago, IL 60611, 3Department of Plant Biology, Carnegie Institution, 260 Panama Street, Stanford, CA 94305, 4Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331 and 5Howard Hughes Medical Institute, California Institute of Technology, 1200 E. California Boulevard, Pasadena, CA 91125, USA http://database.oxfordjournals.org/ *Corresponding author: Tel: +1 609 937 1635; Fax: +1 626 568 8012; Email: [email protected] Submitted 18 June 2012; Revised 30 September 2012; Accepted 2 October 2012 ............................................................................................................................................................................................................................................................................................ -
ELIXIR Poster Numbers: P El001 - 037 Application Posters: P El034 - 037
POSTER LIST ORDERED ALPHABETICALLY BY POSTER TITLE GROUPED BY THEME/TRACK THEME/TRACK: ELIXIR Poster numbers: P_El001 - 037 Application posters: P_El034 - 037 Poster EasyChair Presenting Author list Title Abstract Theme/track Topics number number author P_El001 714 Joan Segura, Daniel Joan Segura 3DBIONOTES: Unifying molecular biology With the advent of next generation sequencing methods, the amount of proteomic and genomic information is growing faster than ever. Several projects have been undertaken to annotate the ELIXIR poster ELIXIR Tabas Madrid, Ruben information genomes of most important organisms, including human. For example, the GENECODE project seeks to enhance all human genes including protein-coding loci with alternatively splices Sanchez-Garcia, Jesús variants, non-coding loci and pseudogenes. Another example is the 1000 genomes, a repository of human genetic variations, including SNPs and structural variants, and their haplotype Cuenca, Carlos Oscar contexts. These projects feed most relevant biological databases as UNIPROT and ENSEMBL, extending the amount of available annotation for genes and proteins.Genomic and proteomic Sánchez Sorzano, Ardan annotations are a valuable contribution in the study of protein and gene functions. However, structural information is an essential key for a deeper understanding of the molecular properties Patwardhan and Jose that allow proteins and genes to perform specific tasks. Therefore, depicting genomic and proteomic information over structural data would offer a very complete picture in order to understand Maria Carazo how proteins and genes behave in the different cellular processes.In this work we present the second version of a web platform -3DBIONOTES- that aims to merge the different levels of molecular biology information, including genomics, proteomics and interactomics data into a unique graphical environment. -
Biocuration 2016 - Posters
Biocuration 2016 - Posters Source: http://www.sib.swiss/events/biocuration2016/posters 1 RAM: A standards-based database for extracting and analyzing disease-specified concepts from the multitude of biomedical resources Jinmeng Jia and Tieliu Shi Each year, millions of people around world suffer from the consequence of the misdiagnosis and ineffective treatment of various disease, especially those intractable diseases and rare diseases. Integration of various data related to human diseases help us not only for identifying drug targets, connecting genetic variations of phenotypes and understanding molecular pathways relevant to novel treatment, but also for coupling clinical care and biomedical researches. To this end, we built the Rare disease Annotation & Medicine (RAM) standards-based database which can provide reference to map and extract disease-specified information from multitude of biomedical resources such as free text articles in MEDLINE and Electronic Medical Records (EMRs). RAM integrates disease-specified concepts from ICD-9, ICD-10, SNOMED-CT and MeSH (http://www.nlm.nih.gov/mesh/MBrowser.html) extracted from the Unified Medical Language System (UMLS) based on the UMLS Concept Unique Identifiers for each Disease Term. We also integrated phenotypes from OMIM for each disease term, which link underlying mechanisms and clinical observation. Moreover, we used disease-manifestation (D-M) pairs from existing biomedical ontologies as prior knowledge to automatically recognize D-M-specific syntactic patterns from full text articles in MEDLINE. Considering that most of the record-based disease information in public databases are textual format, we extracted disease terms and their related biomedical descriptive phrases from Online Mendelian Inheritance in Man (OMIM), National Organization for Rare Disorders (NORD) and Orphanet using UMLS Thesaurus. -
Biocuration - Mapping Resources and Needs [Version 2; Peer Review: 2 Approved]
F1000Research 2020, 9(ELIXIR):1094 Last updated: 22 JUL 2021 RESEARCH ARTICLE Biocuration - mapping resources and needs [version 2; peer review: 2 approved] Alexandra Holinski 1, Melissa L. Burke 1, Sarah L. Morgan 1, Peter McQuilton 2, Patricia M. Palagi 3 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK 2Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, Oxfordshire, OX1 3QG, UK 3SIB Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland v2 First published: 04 Sep 2020, 9(ELIXIR):1094 Open Peer Review https://doi.org/10.12688/f1000research.25413.1 Latest published: 02 Dec 2020, 9(ELIXIR):1094 https://doi.org/10.12688/f1000research.25413.2 Reviewer Status Invited Reviewers Abstract Background: Biocuration involves a variety of teams and individuals 1 2 across the globe. However, they may not self-identify as biocurators, as they may be unaware of biocuration as a career path or because version 2 biocuration is only part of their role. The lack of a clear, up-to-date (revision) report profile of biocuration creates challenges for organisations like ELIXIR, 02 Dec 2020 the ISB and GOBLET to systematically support biocurators and for biocurators themselves to develop their own careers. Therefore, the version 1 ELIXIR Training Platform launched an Implementation Study in order 04 Sep 2020 report report to i) identify communities of biocurators, ii) map the type of curation work being done, iii) assess biocuration training, and iv) draw a picture of biocuration career development. 1. Tanya Berardini , Phoenix Bioinformatics, Methods: To achieve the goals of the study, we carried out a global Fremont, USA survey on the nature of biocuration work, the tools and resources that are used, training that has been received and additional training 2. -
The Uniprot Knowledgebase
Bringing bioinformatics into the classroom The UniProtIntroducing Knowledgebase UniProtKB Computer-Aided Drug Design A PRACTICAL GUIDE 1 Version: 30 November 2020 A Practical Guide to Computer-Aided Drug Design Designing tomorrow’s drugs Overview This Practical Guide outlines basic computational approaches used in drug discovery. It highlights how bioinformatics can be harnessed to design drug candidates, to predict their affinity for their targets, their fate inside the body, their toxicity and possible side-effects. Teaching Goals & Learning Outcomes This Guide introduces bioinformatics tools for designing candidate drug molecules, and for predicting their likely target protein(s) and their drug-like properties. On reading the Guide and completing the exercises, you will be able to: • design drug-candidate molecules using the structures of known drugs as templates, and dock them to known protein targets; • compare the protein target-binding strengths of drug candidates with those of known drugs; • calculate properties of drug candidates and infer whether they need chemical modification to make them more drug-like; • predict the protein(s) that a drug candidate is likely to target; • create molecular fingerprints for known drugs, and use these to quantify their similarity. simple computational methodologies to conceive and evaluate mol- 13 1 Introduction ecules for their potential to become drugs . Although macromolec- ular entities (e.g., like antibodies) can act as therapeutic agents, here we consider drugs as small organic molecules (less than ~100 Over the past century, the design and production of drugs has had atoms) that activate or inhibit the functions of proteins, ultimately a beneficial impact on life expectancy and quality1,2. -
Concepts, Historical Milestones and the Central Place of Bioinformatics in Modern Biology: a European Perspective
1 Concepts, Historical Milestones and the Central Place of Bioinformatics in Modern Biology: A European Perspective Attwood, T.K.1, Gisel, A.2, Eriksson, N-E.3 and Bongcam-Rudloff, E.4 1Faculty of Life Sciences & School of Computer Science, University of Manchester 2Institute for Biomedical Technologies, CNR 3Uppsala Biomedical Centre (BMC), University of Uppsala 4Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences 1UK 2Italy 3,4Sweden 1. Introduction The origins of bioinformatics, both as a term and as a discipline, are difficult to pinpoint. The expression was used as early as 1977 by Dutch theoretical biologist Paulien Hogeweg when she described her main field of research as bioinformatics, and established a bioinformatics group at the University of Utrecht (Hogeweg, 1978; Hogeweg & Hesper, 1978). Nevertheless, the term had little traction in the community for at least another decade. In Europe, the turning point seems to have been circa 1990, with the planning of the “Bioinformatics in the 90s” conference, which was held in Maastricht in 1991. At this time, the National Center for Biotechnology Information (NCBI) had been newly established in the United States of America (USA) (Benson et al., 1990). Despite this, there was still a sense that the nation lacked a “long-term biology ‘informatics’ strategy”, particularly regarding postdoctoral interdisciplinary training in computer science and molecular biology (Smith, 1990). Interestingly, Smith spoke here of ‘biology informatics’, not bioinformatics; and the NCBI was a ‘center for biotechnology information’, not a bioinformatics centre. The discipline itself ultimately grew organically from the needs of researchers to access and analyse (primarily biomedical) data, which appeared to be accumulating at alarming rates simultaneously in different parts of the world. -
Improving the Gene Ontology Resource to Facilitate More Informative Analysis and Interpretation of Alzheimer’S Disease Data
G C A T T A C G G C A T genes Article Improving the Gene Ontology Resource to Facilitate More Informative Analysis and Interpretation of Alzheimer’s Disease Data Barbara Kramarz 1 , Paola Roncaglia 2 , Birgit H. M. Meldal 2 , Rachael P. Huntley 1 , Maria J. Martin 2, Sandra Orchard 2, Helen Parkinson 2, David Brough 3, Rina Bandopadhyay 4, Nigel M. Hooper 3 and Ruth C. Lovering 1,* 1 UCL Institute of Cardiovascular Science, University College London, Rayne Building, 5 University Street, London WC1E 6JF, UK; [email protected] (B.K.); [email protected] (R.P.H.) 2 European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; [email protected] (P.R.); [email protected] (B.H.M.M.); [email protected] (M.J.M.); [email protected] (S.O.); [email protected] (H.P.) 3 Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, AV Hill Building, Oxford Road, Manchester M13 9PT, UK; [email protected] (D.B.); [email protected] (N.M.H.) 4 UCL Queen Square Institute of Neurology and Reta Lila Weston Institute of Neurological Studies, 1 Wakefield Street, London WC1N 1PJ, UK; [email protected] * Correspondence: [email protected] or [email protected]; Tel.: +44-207-679-6965 Received: 31 October 2018; Accepted: 23 November 2018; Published: 29 November 2018 Abstract: The analysis and interpretation of high-throughput datasets relies on access to high-quality bioinformatics resources, as well as processing pipelines and analysis tools. -
The Uniprot Knowledgebase BLAST
Introduction to bioinformatics The UniProt Knowledgebase BLAST UniProtKB Basic Local Alignment Search Tool A CRITICAL GUIDE 1 Version: 1 August 2018 A Critical Guide to BLAST BLAST Overview This Critical Guide provides an overview of the BLAST similarity search tool, Briefly examining the underlying algorithm and its rise to popularity. Several WeB-based and stand-alone implementations are reviewed, and key features of typical search results are discussed. Teaching Goals & Learning Outcomes This Guide introduces concepts and theories emBodied in the sequence database search tool, BLAST, and examines features of search outputs important for understanding and interpreting BLAST results. On reading this Guide, you will Be aBle to: • search a variety of Web-based sequence databases with different query sequences, and alter search parameters; • explain a range of typical search parameters, and the likely impacts on search outputs of changing them; • analyse the information conveyed in search outputs and infer the significance of reported matches; • examine and investigate the annotations of reported matches, and their provenance; and • compare the outputs of different BLAST implementations and evaluate the implications of any differences. finding short words – k-tuples – common to the sequences Being 1 Introduction compared, and using heuristics to join those closest to each other, including the short mis-matched regions Between them. BLAST4 was the second major example of this type of algorithm, From the advent of the first molecular sequence repositories in and rapidly exceeded the popularity of FastA, owing to its efficiency the 1980s, tools for searching dataBases Became essential. DataBase searching is essentially a ‘pairwise alignment’ proBlem, in which the and Built-in statistics. -
Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases
Genomics Proteomics Bioinformatics 18 (2020) 91–103 Genomics Proteomics Bioinformatics www.elsevier.com/locate/gpb www.sciencedirect.com PERSPECTIVE Quality Matters: Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases Qingyu Chen 1,*, Ramona Britto 2, Ivan Erill 3, Constance J. Jeffery 4, Arthur Liberzon 5, Michele Magrane 2, Jun-ichi Onami 6,7, Marc Robinson-Rechavi 8,9, Jana Sponarova 10, Justin Zobel 1,*, Karin Verspoor 1,* 1 School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3010, Australia 2 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK 3 Department of Biological Sciences, University of Maryland, Baltimore, MD 21250, USA 4 Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL 60607, USA 5 Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA 6 Japan Science and Technology Agency, National Bioscience Database Center, Tokyo 102-8666, Japan 7 National Institute of Health Sciences, Tokyo 158-8501, Japan 8 Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland 9 Department of Ecology and Evolution, University of Lausanne, CH-1015 Lausanne, Switzerland 10 Nebion AG, 8048 Zurich, Switzerland Received 8 December 2017; revised 24 October 2018; accepted 14 December 2018 Available online 9 July 2020 Handled by Zhang Zhang Introduction assembled, annotated, and ultimately submitted to primary nucleotide databases such as GenBank [2], European Nucleo- tide Archive (ENA) [3], and DNA Data Bank of Japan Biological databases represent an extraordinary collective vol- (DDBJ) [4] (collectively known as the International Nucleotide ume of work. -
Embnet.News Volume 4 Nr
embnet.news Volume 4 Nr. 3 Page 1 embnet.news Volume 4 Nr 3 (ISSN1023-4144) upon our core expertise in sequence analysis. Editorial Such debates can only be construed as healthy. Stasis can all too easily become stagnation. After some cliff-hanging As well as being the Christmas Bumper issue, this is also recounts and reballots at the AGM there have been changes the after EMBnet AGM Issue. The 11th Annual Business in all of EMBnet's committees. It is to be hoped that new Meeting was organised this year by the Italian Node (CNR- committee members will help galvanise us all into a more Bari) and took place up in the hills at Selva di Fasano at the active phase after a relatively quiet 1997. The fact that the end of September. For us northerners, the concept of "O for financial status of EMBnet is presently very healthy, will a beaker full of the warm south" so affected one of the certainly not impede this drive. Everyone agrees that delegates that he jumped (or was he pushed ?) fully clothed bioinformatics is one of science's growth areas and there is into the hotel swimming pool. Despite the balmy weather nobody better equipped than EMBnet to make solid and the excellent food and wine, we did manage to get a contributions to the field. solid day and a half of work done. The embnet.news editorial board: EMBnet is having to make some difficult choices about what its future direction and purpose should be. Our major source Alan Bleasby of funds is from the EU, but pretty much all countries which Rob Harper are eligible for EU funding have already joined the Robert Herzog organisation. -
Ontology: Tool for Broad Spectrum Knowledge Integration Barry Smith
Foreword to Chinese Translation Ontology: Tool for Broad Spectrum Knowledge Integration Barry Smith BFO: The Beginnings This book was first published in 2015. Its primary target audience was bio- and biomedical informaticians, reflecting the ways in which ontologies had become an established part of the toolset of bio- and biomedical informatics since (roughly) the completion of the Human Genome Project (HGP). As is well known, the success of HGP led to the transformation of biological and clinical sciences into information-driven disciplines and spawned a whole series of new disciplines with names like ‘proteomics’, ‘connectomics’ and ‘toxiocopharmacogenomics’. It was of course not only the human genome that was made available for research but also the genomes of other ‘model organisms’, such as mouse or fly. The remarkable similarities between these genomes and the human genome made it possible to carry out experiments on model organisms and use the results to draw conclusions relevant to our understanding of human health and disease. To bring this about, however, it was necessary to create a controlled vocabulary that could be used for describing salient features of model organisms in a species-neutral way, and to use the terms of this vocabulary to tag the sequence data for all salient organisms. It was with the purpose of creating such a vocabulary that the Gene Ontology (GO) was born in a Montreal hotel bar in 1998.1 Since then the GO has served as mediator between the new genomic data on the one hand, which is accessible only with the aid of computers, and what we might think of as the ‘old biology data’ captured using natural language by means of terms such as ‘cell division’ or ‘mitochondrion’ or ‘protein binding’. -
GOBLET Annual General Meeting
The Global Organisation for Bioinformatics Learning, Education & Training GOBLET Annual General Meeting TGAC, Norwich, UK, 6 November 2013 The meeting commenced with a roll-call of members present or represented by proxy, as follows: Present: Terri Attwood: EMBnet Christine Orengo: ISCB Christian Schönbach: APBioNet Segun Fatumo: ASBCB Celia van Gelder: NBIC, NL Cath Brooksbank: EMBL-EBI, UK Patricia Palagi: SIB, CH Pedro Fernandes: IGC, PT Annette McGrath: ABN, AU Vicky Schneider: TGAC, UK Manuel Corpas: Itico, UK Dan Maclean: TSL, UK Juliette Hayer: SGBC, SE Eija Korpelainen: CSC, FI Angela Davies: Nowgen, UK Aidan Budd: Individual Member, DE Represented by proxy: Pascale Gaudet: ISB (proxy EMBnet) Michelle Brazas: Bioinformatics.ca (proxy SIB) Sarah Blackford: SEB (proxy EMBnet) Judit Kumithini: CPGR, ZA (proxy SGBC) Chris Ponting: CGAT, UK (proxy TGAC) Susanna Sansone: BioSharing, UK (proxy TGAC) Gert Vriend: CMBI, NL (proxy NBIC) Observers: Rafael Jimenez: Itico, UK Allesandro Cestaro: Fondazione Edmund Mach (FEM), IT Claudio Donati: Fondazione Edmund Mach (FEM), IT Francis Rowland: EMBL-EBI, UK Darren Hughes: WT, UK Rebecca Twells: WT, UK Of the 26 organisations that signed the MoU, 21 had officially joined as bronze, silver or gold members; two (SeqAhead, BTN) weren’t able to join, as they have no funding mechanism to do so (and GOBLET is, anyway, the logical evolution of the BTN); two (EdGe, SoIBio) had indicated their membership fee level only after the election process had begun, so weren’t eligible to vote in this meeting; BITS had said that they’d give an indication of their fee level after their Board meeting in November, so also weren’t eligible to vote in this meeting; and CMBI had paid directly, without having signed the MoU.