Improving the Gene Ontology Resource to Facilitate More Informative Analysis and Interpretation of Alzheimer’S Disease Data
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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 ............................................................................................................................................................................................................................................................................................ -
Unicarbkb: Building a Standardised and Scalable Informatics Platform for Glycosciences Research
Platforms EOI: UniCarbKB: Building a Standardised and Scalable Informatics Platform for Glycosciences Research 20 September 2019 at 13:02 Project title UniCarbKB: Building a Standardised and Scalable Informatics Platform for Glycosciences Research Field of Research code(s) 03 CHEMICAL SCIENCES 06 BIOLOGICAL SCIENCES 08 INFORMATION AND COMPUTING SCIENCES 11 MEDICAL AND HEALTH SCIENCES EOI Lead Name Matthew Campbell EOI lead Research Group Institute for Glycomics EOI lead Organisation Institute for Glycomics, Griffith University EOI lead Email Collaborator details Name Research Group Organisation Malcolm Wolski eResearch Services Griffith University Project description Over the last few years the glycomics knowledge platform UniCarbKB has contributed to the growth of international glycoinformatics resources by providing access to data collections, web services and software applications supported by biocuration activities. However, UniCarbKB has continued to develop as a single monolithic resource. This proposed project will improve the growth and scalability of UniCarbKB by breaking down the platform into smaller and composable pieces. This will be achieved through the adoption of a more decentralised approach and a microservice architecture that will allow components of the platform to scale at different rates. This architecture will allow us is to build an extendable and cross-disciplinary resource by integrating existing data with new analysis tools. The adoption of international resources (e.g. NIH GlyGen), will allow not just mapping of glycan data to genes and proteins but also pathways, gene ontology, publications and a wide variety of data from EBI, NCBI and other sources. Additionally, we propose to integrate glycan array data and develop ontologies that can serve as tools for integration and subsequently analysis of glycan-associated Existing technology Adopt The proposed project will adopt a multi-tier application architecture in which applications will be developed and deployed as microservices. -
Hierarchical Classification of Gene Ontology Terms Using the Gostruct
Hierarchical classification of Gene Ontology terms using the GOstruct method Artem Sokolov and Asa Ben-Hur Department of Computer Science, Colorado State University Fort Collins, CO, 80523, USA Abstract Protein function prediction is an active area of research in bioinformatics. And yet, transfer of annotation on the basis of sequence or structural similarity remains widely used as an annotation method. Most of today's machine learning approaches reduce the problem to a collection of binary classification problems: whether a protein performs a particular function, sometimes with a post-processing step to combine the binary outputs. We propose a method that directly predicts a full functional annotation of a protein by modeling the structure of the Gene Ontology hierarchy in the framework of kernel methods for structured-output spaces. Our empirical results show improved performance over a BLAST nearest-neighbor method, and over algorithms that employ a collection of binary classifiers as measured on the Mousefunc benchmark dataset. 1 Introduction Protein function prediction is an active area of research in bioinformatics [25]; and yet, transfer of annotation on the basis of sequence or structural similarity remains the standard way of assigning function to proteins in newly sequenced organisms [20]. The Gene Ontology (GO), which is the current standard for annotating gene products and proteins, provides a large set of terms arranged in a hierarchical fashion that specify a gene-product's molecular function, the biological process it is involved in, and its localization to a cellular component [12]. GO term prediction is therefore a hierarchical classification problem, made more challenging by the thousands of annotation terms available. -
Gene Ontology Analysis with Cytoscape
Introduction to Systems Biology Exercise 6: Gene Ontology Analysis Overview: Gene Ontology (GO) is a useful resource in bioinformatics and systems biology. GO defines a controlled vocabulary of terms in biological process, molecular function, and cellular location, and relates the terms in a somewhat-organized fashion. This exercise outlines the resources available under Cytoscape to perform analyses for the enrichment of gene annotation (GO terms) in networks or sub-networks. In this exercise you will: • Learn how to navigate the Cytoscape Gene Ontology wizard to apply GO annotations to Cytoscape nodes • Learn how to look for enriched GO categories using the BiNGO plugin. What you will need: • The BiNGO plugin, developed by the Computational Biology Division, Dept. of Plant Systems Biology, Flanders Interuniversitary Institute for Biotechnology (VIB), described in Maere S, et al. Bioinformatics. 2005 Aug 15;21(16):3448-9. Epub 2005 Jun 21. • galFiltered.sif used in the earlier exercises and found under sampleData. Please download any missing files from http://www.cbs.dtu.dk/courses/27041/exercises/Ex3/ Download and install the BiNGO plugin, as follows: 1. Get BiNGO from the course page (see above) or go to the BiNGO page at http://www.psb.ugent.be/cbd/papers/BiNGO/. (This site also provides documentation on BiNGO). 2. Unzip the contents of BiNGO.zip to your Cytoscape plugins directory (make sure the BiNGO.jar file is in the plugins directory and not in a subdirectory). 3. If you are currently running Cytoscape, exit and restart. First, we will load the Gene Ontology data into Cytoscape. 1. Start Cytoscape, under Edit, Preferences, make sure your default species, “defaultSpeciesName”, is set to “Saccharomyces cerevisiae”. -
To Find Information About Arabidopsis Genes Leonore Reiser1, Shabari
UNIT 1.11 Using The Arabidopsis Information Resource (TAIR) to Find Information About Arabidopsis Genes Leonore Reiser1, Shabari Subramaniam1, Donghui Li1, and Eva Huala1 1Phoenix Bioinformatics, Redwood City, CA USA ABSTRACT The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource of Arabidopsis biology for plant scientists. TAIR curates and integrates information about genes, proteins, gene function, orthologs gene expression, mutant phenotypes, biological materials such as clones and seed stocks, genetic markers, genetic and physical maps, genome organization, images of mutant plants, protein sub-cellular localizations, publications, and the research community. The various data types are extensively interconnected and can be accessed through a variety of Web-based search and display tools. This unit primarily focuses on some basic methods for searching, browsing, visualizing, and analyzing information about Arabidopsis genes and genome, Additionally we describe how members of the community can share data using TAIR’s Online Annotation Submission Tool (TOAST), in order to make their published research more accessible and visible. Keywords: Arabidopsis ● databases ● bioinformatics ● data mining ● genomics INTRODUCTION The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource for the biology of Arabidopsis thaliana (Huala et al., 2001; Garcia-Hernandez et al., 2002; Rhee et al., 2003; Weems et al., 2004; Swarbreck et al., 2008, Lamesch, et al., 2010, Berardini et al., 2016). The TAIR database contains information about genes, proteins, gene expression, mutant phenotypes, germplasms, clones, genetic markers, genetic and physical maps, genome organization, publications, and the research community. In addition, seed and DNA stocks from the Arabidopsis Biological Resource Center (ABRC; Scholl et al., 2003) are integrated with genomic data, and can be ordered through TAIR. -
Use and Misuse of the Gene Ontology Annotations
Nature Reviews Genetics | AOP, published online 13 May 2008; doi:10.1038/nrg2363 REVIEWS Use and misuse of the gene ontology annotations Seung Yon Rhee*, Valerie Wood‡, Kara Dolinski§ and Sorin Draghici|| Abstract | The Gene Ontology (GO) project is a collaboration among model organism databases to describe gene products from all organisms using a consistent and computable language. GO produces sets of explicitly defined, structured vocabularies that describe biological processes, molecular functions and cellular components of gene products in both a computer- and human-readable manner. Here we describe key aspects of GO, which, when overlooked, can cause erroneous results, and address how these pitfalls can be avoided. The accumulation of data produced by genome-scale FIG. 1b). These characteristics of the GO structure enable research requires explicitly defined vocabularies to powerful grouping, searching and analysis of genes. describe the biological attributes of genes in order to allow integration, retrieval and computation of the Fundamental aspects of GO annotations data1. Arguably, the most successful example of system- A GO annotation associates a gene with terms in the atic description of biology is the Gene Ontology (GO) ontologies and is generated either by a curator or project2. GO is widely used in biological databases, automatically through predictive methods. Genes are annotation projects and computational analyses (there associated with as many terms as appropriate as well as are 2,960 citations for GO in version 3.0 of the ISI Web of with the most specific terms available to reflect what is Knowledge) for annotating newly sequenced genomes3, currently known about a gene. -
Wormbase Curation Interfaces and Tools
Wormbase Curation Interfaces And Tools Xiaodong Wang, Paul Sternberg and the WormBase Consortium Division of Biology 156-29, California Institute of Technology, Pasadena, CA 91125, USA Abstract Cellular Component GO curation form Antibody OA Gene ontology OA Curating biological information from the published literature can be time- and labor-intensive especially without automated tools. WormBase1 has adopted several curation interfaces and tools, most of which were built in-house, to help curators recognize and extract data more efficiently from the literature. These tools range from simple computer interfaces for data entry to employing scripts that take advantage of complex text extraction algorithms, which automatically identify specific objects in a paper and presents them to the curator for curation. By using these in-house tools, we Go term are also able to tailor the tool to the individual needs and preferences of the curator. For example, C.elegans protein Cellular Gene Ontology Cellular Component and gene-gene interaction curators employ the text mining Component software Textpresso2 to indentify, retrieve, and extract relevant sentences from the full text of an Category terms article. The curators then use a web-based curation form to enter the data into our local database. Wormbase has developed our own Ontology Annotator tool based on the publicly available Phenote ontology annotation curation interface (developed by the Berkeley Bioinformatics Open-Source Ontology Annotator Projects (BBOP)), which we have adapted with datatype specific configurations. Currently, we have Autocomple4on field eight data s, including antibody, GO, gene regulation, interaction, molecule, people, phenotype and with ontology Transgene OA transgene are using or will use OA for routine literature curation. -
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. -
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. -
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. -
ISB Newsletter
ISB June 2012 . INTERNATIONAL SOCIETY ISB FOR BIOCURATION June, 2012 In this issue: - Save the Date: ISB2013 Save the Date - B3CB Workshop Report - ISB Membership Renewal for ISB2013! - 2012 Elections: ISB Executive Committee The 6th International Biocuration Conference - Jack Leunissen will be held on April 7-10, - Latest Advances from ISB Research Teams 2013, at Churchill College, - Job Opportunities Cambridge, UK. Training Activities: B3CB Workshop Report B3CB was a workshop aimed at developing common resources and infrastructures for training of experts as well as users of bioinformatics tools and resources. It was held in Uppsala (Sweden), and chaired by Terri Attwood (Manchester University, ISB Executive Committee member). Pascale Gaudet introduced the ISB. We believe that this initiative will help the ISB provide more support for training, a part of the our mission statement. The workshop brought together members of the executive committees of several ISB Membership Renewal organizations in Bioinformatics, Biotechnology, Biocuration and Computational Biology (B3CB): For many of us, membership fees are due - EMBnet (Global Bioinformatics Network) – Terri Attwood this month! Notices for renewal of ISB - ISCB (International Society for Computational Biology) – Reinhard Schneider Memberships will be reaching inboxes in - APBioNet (Asia-Pacific Bioinformatics Network) – Christian Schönbach the coming days. Your ISB Membership - ASBCB (African Society for Bioinformatics & Computational Biology) – Nicky Mulder goes to support activities such as the - SoIBio (Iberoamerican Society for Bioinformatics) – Oswaldo Trelles International Biocuration Conference - ISB (International Society for Biocuration) – Pascale Gaudet (travel support was provided for 10 - EBI/ELIXIR (European Bioinformatics Institute) – Cath Brooksbank attendees to ISB2012), BioDBCore (in - NBIC (Netherlands Bioinformatics Centre) – Celia van Gelder collaboration with BioSharing), and to - SeqAhead (Next-Gen Sequencing Data Analysis Network) – Erik Bongcam-Rudloff establish links with other groups.