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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 ............................................................................................................................................................................................................................................................................................ -
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. -
UC Davis UC Davis Previously Published Works
UC Davis UC Davis Previously Published Works Title Longer first introns are a general property of eukaryotic gene structure. Permalink https://escholarship.org/uc/item/9j42z8fm Journal PloS one, 3(8) ISSN 1932-6203 Authors Bradnam, Keith R Korf, Ian Publication Date 2008-08-29 DOI 10.1371/journal.pone.0003093 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Longer First Introns Are a General Property of Eukaryotic Gene Structure Keith R. Bradnam*, Ian Korf Genome Center, University of California Davis, Davis, California, United States of America Abstract While many properties of eukaryotic gene structure are well characterized, differences in the form and function of introns that occur at different positions within a transcript are less well understood. In particular, the dynamics of intron length variation with respect to intron position has received relatively little attention. This study analyzes all available data on intron lengths in GenBank and finds a significant trend of increased length in first introns throughout a wide range of species. This trend was found to be even stronger when using high-confidence gene annotation data for three model organisms (Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster) which show that the first intron in the 59 UTR is - on average - significantly longer than all downstream introns within a gene. A partial explanation for increased first intron length in A. thaliana is suggested by the increased frequency of certain motifs that are present in first introns. The phenomenon of longer first introns can potentially be used to improve gene prediction software and also to detect errors in existing gene annotations. -
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. -
The Biogrid Interaction Database
D470–D478 Nucleic Acids Research, 2015, Vol. 43, Database issue Published online 26 November 2014 doi: 10.1093/nar/gku1204 The BioGRID interaction database: 2015 update Andrew Chatr-aryamontri1, Bobby-Joe Breitkreutz2, Rose Oughtred3, Lorrie Boucher2, Sven Heinicke3, Daici Chen1, Chris Stark2, Ashton Breitkreutz2, Nadine Kolas2, Lara O’Donnell2, Teresa Reguly2, Julie Nixon4, Lindsay Ramage4, Andrew Winter4, Adnane Sellam5, Christie Chang3, Jodi Hirschman3, Chandra Theesfeld3, Jennifer Rust3, Michael S. Livstone3, Kara Dolinski3 and Mike Tyers1,2,4,* 1Institute for Research in Immunology and Cancer, Universite´ de Montreal,´ Montreal,´ Quebec H3C 3J7, Canada, 2The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada, 3Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA, 4School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK and 5Centre Hospitalier de l’UniversiteLaval´ (CHUL), Quebec,´ Quebec´ G1V 4G2, Canada Received September 26, 2014; Revised November 4, 2014; Accepted November 5, 2014 ABSTRACT semi-automated text-mining approaches, and to en- hance curation quality control. The Biological General Repository for Interaction Datasets (BioGRID: http://thebiogrid.org) is an open access database that houses genetic and protein in- INTRODUCTION teractions curated from the primary biomedical lit- Massive increases in high-throughput DNA sequencing erature for all major model organism species and technologies (1) have enabled an unprecedented level of humans. As of September 2014, the BioGRID con- genome annotation for many hundreds of species (2–6), tains 749 912 interactions as drawn from 43 149 pub- which has led to tremendous progress in the understand- lications that represent 30 model organisms. -
NIH-GDS: Genomic Data Sharing
NIH-GDS: Genomic Data Sharing National Institutes of Health Data type Explain whether the research being considered for funding involves human data, non- human data, or both. Information to be included in this section: • Type of data being collected: human, non-human, or both human & non-human. • Type of genomic data to be shared: sequence, transcriptomic, epigenomic, and/or gene expression. • Level of the genomic data to be shared: Individual-level, aggregate-level, or both. • Relevant associated data to be shared: phenotype or exposure. • Information needed to interpret the data: study protocols, survey tools, data collection instruments, data dictionary, software (including version), codebook, pipeline metadata, etc. This information should be provided with unrestricted access for all data levels. Data repository Identify the data repositories to which the data will be submitted, and for human data, whether the data will be available through unrestricted or controlled-access. For human genomic data, investigators are expected to register all studies in the database of Genotypes and Phenotypes (dbGaP) by the time data cleaning and quality control measures begin in addition to submitting the data to the relevant NIH-designated data repository (e.g., dbGaP, Gene Expression Omnibus (GEO), Sequence Read Archive (SRA), the Cancer Genomics Hub) after registration. Non-human data may be made available through any widely used data repository, whether NIH- funded or not, such as GEO, SRA, Trace Archive, Array Express, Mouse Genome Informatics, WormBase, the Zebrafish Model Organism Database, GenBank, European Nucleotide Archive, or DNA Data Bank of Japan. Data in unrestricted-access repositories (e.g., The 1000 Genomes Project) are publicly available to anyone. -
SGD and the Alliance of Genome Resources Stacia R
SGD and the Alliance of Genome Resources Stacia R. Engel, Edith D. Wong, Robert S. Nash, Felix Gondwe, Kevin A. MacPherson, Patrick Ng, Suzi Aleksander, Stuart Miyasato, J. Michael Cherry, and The SGD Project Department of Genetics, Stanford University, Stanford, CA 94305, USA The yeast research community has long enjoyed the support provided by the Saccharomyces Genome Database (SGD), and has flourished because of its existence, making great breakthroughs and technological advances, and contributing countless key insights to the fields of genetics and genomics over the past decades. SGD has recently joined forces with five other model organism databases (MODs) - WormBase, FlyBase, ZFIN, RGD, and MGI - plus the Gene Ontology Consortium (GOC) to form the Alliance of Genome Resources (the Alliance; alliancegenome.org). The Alliance website integrates expertly-curated information on model organisms and the functioning of cellular systems, and enables unified access to comparative genomics and genetics data, facilitating cross-species analyses. The site is undergoing rapid development as we work to harmonize various datatypes across the various organisms. Explore your favorite genes in the Alliance to find information regarding orthology sets, gene expression, gene function, mutant phenotypes, alleles, disease associations and more! The Alliance is supported by NIH NHGRI U24HG002223-19S1, NIH NHGRI U41HG001315 (SGD), NIH NHGRI P41HG002659 (ZFIN), NIH NHGRI U24HG002223 (WormBase), MRC-UK MR/L001020/1 (WormBase), NIH NHGRI U41HG000739 (FlyBase), NIH NHLBI HL64541 (RGD), NIH NHGRI HG000330 (MGD), and NIH NHGRI U41HG002273 (GOC, which also proVides funding to WB, MGD, SGD). Goal: develop and maintain sustainable genome information resources that facilitate the use of diverse model organisms to understand the genetic and genomic bases of human biology, health, and disease Yeast, human, and model organism orthologs Alleles and phenotype variants Disease associations Expression [email protected] @yeastgenome @alliancegenome [email protected]. -
The HUPO Proteomics Standards Initiative Meeting: Towards Common Standards for Exchanging Proteomics Data Hinxton, Cambridge, UK, 19–20 October 2002
Comparative and Functional Genomics Comp Funct Genom 2003; 4: 16–19. Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cfg.232 Feature Meeting Review: The HUPO Proteomics Standards Initiative meeting: towards common standards for exchanging proteomics data Hinxton, Cambridge, UK, 19–20 October 2002 Sandra Orchard, Paul Kersey, Henning Hermjakob* and Rolf Apweiler EMBL Outstation–European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK *Correspondence to: Abstract Henning Hermjakob, EMBL Outstation–European The Proteomics Standards Initiative (PSI) aims to define community standards Bioinformatics Institute, for data representation in proteomics and to facilitate data comparison, exchange Wellcome Trust Genome and verification. Initially the fields of protein–protein interactions (PPI) and mass Campus, Hinxton, Cambridge, spectroscopy have been targeted and the inaugural meeting of the PSI addressed the UK. questions of data storage and exchange in both of these areas. The PPI group rapidly E-mail: reached consensus as to the minimum requirements for a data exchange model; an [email protected] XML draft is now being produced. The mass spectroscopy group have achieved major advances in the definition of a required data model and working groups are currently taking these discussions further. A further meeting is planned in January 2003 to Received: 14 November 2002 advance both these projects. Copyright 2003 John Wiley & Sons, Ltd. Accepted: 14 November 2002 Keywords: proteomics; spectroscopy; protein–protein interactions Introduction process, before splitting into two working parties to address the issues facing their respective fields. The Proteomics Standards Initiative was estab- lished following a meeting in April 2002, jointly organized by HUPO and NAS, at which the urgent Protein–protein interactions (PPI) group need for standardization of proteomics data was recognized. -
The European Bioinformatics Institute in 2020: Building a Global Infrastructure of Interconnected Data Resources for the Life Sciences Charles E
Published online 8 November 2019 Nucleic Acids Research, 2020, Vol. 48, Database issue D17–D23 doi: 10.1093/nar/gkz1033 The European Bioinformatics Institute in 2020: building a global infrastructure of interconnected data resources for the life sciences Charles E. Cook *, Oana Stroe, Guy Cochrane ,EwanBirney and Rolf Apweiler European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK Received September 21, 2019; Revised October 18, 2019; Editorial Decision October 21, 2019; Accepted November 06, 2019 ABSTRACT ature. EMBL-EBI’s data resources collate, integrate, curate and make freely available to the public the world’s scientific Data resources at the European Bioinformatics In- data. stitute (EMBL-EBI, https://www.ebi.ac.uk/)archive, Our resources (www.ebi.ac.uk/services) include archival organize and provide added-value analysis of re- or deposition databases that store primary experimental search data produced around the world. This year’s data submitted by researchers, as well as knowledgebases update for EMBL-EBI focuses on data exchanges that integrate and add value to experimental data, with among resources, both within the institute and with many having both functions (1,2). All EMBL-EBI data re- a wider global infrastructure. Within EMBL-EBI, data sources, are open access and freely available to any user resources exchange data through a rich network of worldwide at any time, and EMBL-EBI strongly supports data flows mediated by automated systems. This net- the concept of FAIR data (findable, accessible, interopera- work ensures that users are served with as much ble, and resuable) (3). -
2003 Mulder Nucl Acids Res {22
The InterPro Database, 2003 brings increased coverage and new features Nicola J Mulder, Rolf Apweiler, Teresa K Attwood, Amos Bairoch, Daniel Barrell, Alex Bateman, David Binns, Margaret Biswas, Paul Bradley, Peer Bork, et al. To cite this version: Nicola J Mulder, Rolf Apweiler, Teresa K Attwood, Amos Bairoch, Daniel Barrell, et al.. The InterPro Database, 2003 brings increased coverage and new features. Nucleic Acids Research, Oxford University Press, 2003, 31 (1), pp.315-318. 10.1093/nar/gkg046. hal-01214149 HAL Id: hal-01214149 https://hal.archives-ouvertes.fr/hal-01214149 Submitted on 9 Oct 2015 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. # 2003 Oxford University Press Nucleic Acids Research, 2003, Vol. 31, No. 1 315–318 DOI: 10.1093/nar/gkg046 The InterPro Database, 2003 brings increased coverage and new features Nicola J. Mulder1,*, Rolf Apweiler1, Teresa K. Attwood3, Amos Bairoch4, Daniel Barrell1, Alex Bateman2, David Binns1, Margaret Biswas5, Paul Bradley1,3, Peer Bork6, Phillip Bucher7, Richard R. Copley8, Emmanuel Courcelle9, Ujjwal Das1, Richard Durbin2, Laurent Falquet7, Wolfgang Fleischmann1, Sam Griffiths-Jones2, Downloaded from Daniel Haft10, Nicola Harte1, Nicolas Hulo4, Daniel Kahn9, Alexander Kanapin1, Maria Krestyaninova1, Rodrigo Lopez1, Ivica Letunic6, David Lonsdale1, Ville Silventoinen1, Sandra E. -
PINOT: an Intuitive Resource for Integrating Protein-Protein Interactions James E
Tomkins et al. Cell Communication and Signaling (2020) 18:92 https://doi.org/10.1186/s12964-020-00554-5 METHODOLOGY Open Access PINOT: an intuitive resource for integrating protein-protein interactions James E. Tomkins1, Raffaele Ferrari2, Nikoleta Vavouraki1, John Hardy2,3,4,5,6, Ruth C. Lovering7, Patrick A. Lewis1,2,8, Liam J. McGuffin9* and Claudia Manzoni1,10* Abstract Background: The past decade has seen the rise of omics data for the understanding of biological systems in health and disease. This wealth of information includes protein-protein interaction (PPI) data derived from both low- and high-throughput assays, which are curated into multiple databases that capture the extent of available information from the peer-reviewed literature. Although these curation efforts are extremely useful, reliably downloading and integrating PPI data from the variety of available repositories is challenging and time consuming. Methods: We here present a novel user-friendly web-resource called PINOT (Protein Interaction Network Online Tool; available at http://www.reading.ac.uk/bioinf/PINOT/PINOT_form.html) to optimise the collection and processing of PPI data from IMEx consortium associated repositories (members and observers) and WormBase, for constructing, respectively, human and Caenorhabditis elegans PPI networks. Results: Users submit a query containing a list of proteins of interest for which PINOT extracts data describing PPIs. At every query submission PPI data are downloaded, merged and quality assessed. Then each PPI is confidence scored based on the number of distinct methods used for interaction detection and the number of publications that report the specific interaction. Examples of how PINOT can be applied are provided to highlight the performance, ease of use and potential utility of this tool. -
Annotation of Metabolic Genes in Caenorhabditis Elegans and Reconstruction of Icel1273
Annotation of Metabolic Genes in Caenorhabditis elegans and Reconstruction of iCEL1273 Page 1 1. Identification of C. elegans Metabolic Genes ............................................................. 4 KEGG .................................................................................................................................. 4 WormBase ........................................................................................................................... 4 UniProt ............................................................................................................................... 4 KOG .................................................................................................................................... 5 myKEGG ............................................................................................................................. 5 myTree................................................................................................................................. 6 Systematic Annotation by Manual Curation and Regression (SACURE) ........................... 7 Validation of SACURE ........................................................................................................ 8 Availability and potential applications of SACURE ........................................................... 9 2. Reconstruction of a Template C. elegans Metabolic Network: Biomass, Transport, and Demand/Sink Reactions ...........................................................................................