Original Article Text Mining in the Biocuration Workflow: Applications for Literature Curation at Wormbase, Dictybase and TAIR
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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. -
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]. -
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. -
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. -
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. -
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. -
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 ........................................................................................... -
The Distribution of Lectins Across the Phylum Nematoda: a Genome-Wide Search
Int. J. Mol. Sci. 2017, 18, 91; doi:10.3390/ijms18010091 S1 of S12 Supplementary Materials: The Distribution of Lectins across the Phylum Nematoda: A Genome-Wide Search Lander Bauters, Diana Naalden and Godelieve Gheysen Figure S1. Alignment of partial calreticulin/calnexin sequences. Amino acids are represented by one letter codes in different colors. Residues needed for carbohydrate binding are indicated in red boxes. Sequences containing all six necessary residues are indicated with an asterisk. Int. J. Mol. Sci. 2017, 18, 91; doi:10.3390/ijms18010091 S2 of S12 Figure S2. Alignment of partial legume lectin-like sequences. Amino acids are represented by one letter codes in different colors. EcorL is a legume lectin originating from Erythrina corallodenron, used in this alignment to compare carbohydrate binding sites. The residues necessary for carbohydrate interaction are shown in red boxes. Nematode lectin-like sequences containing at least four out of five key residues are indicated with an asterisk. Figure S3. Alignment of possible Ricin-B lectin-like domains. Amino acids are represented by one letter codes in different colors. The key amino acid residues (D-Q-W) involved in carbohydrate binding, which are repeated three times, are boxed in red. Sequences that have at least one complete D-Q-W triad are indicated with an asterisk. Int. J. Mol. Sci. 2017, 18, 91; doi:10.3390/ijms18010091 S3 of S12 Figure S4. Alignment of possible LysM lectins. Amino acids are represented by one letter codes in different colors. Conserved cysteine residues are marked with an asterisk under the alignment. The key residue involved in carbohydrate binding in an eukaryote is boxed in red [1].