Use and Misuse of the Gene Ontology Annotations
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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. -
Creating the Gene Ontology Resource: Design and Implementation
Resource Creating the Gene Ontology Resource: Design and Implementation The Gene Ontology Consortium2 The exponential growth in the volume of accessible biological information has generated a confusion of voices surrounding the annotation of molecular information about genes and their products. The Gene Ontology (GO) project seeks to provide a set of structured vocabularies for specific biological domains that can be used to describe gene products in any organism. This work includes building three extensive ontologies to describe molecular function, biological process, and cellular component, and providing a community database resource that supports the use of these ontologies. The GO Consortium was initiated by scientists associated with three model organism databases: SGD, the Saccharomyces Genome database; FlyBase, the Drosophila genome database; and MGD/GXD, the Mouse Genome Informatics databases. Additional model organism database groups are joining the project. Each of these model organism information systems is annotating genes and gene products using GO vocabulary terms and incorporating these annotations into their respective model organism databases. Each database contributes its annotation files to a shared GO data resource accessible to the public at http://www.geneontology.org/. The GO site can be used by the community both to recover the GO vocabularies and to access the annotated gene product data sets from the model organism databases. The GO Consortium supports the development of the GO database resource and provides tools enabling curators and researchers to query and manipulate the vocabularies. We believe that the shared development of this molecular annotation resource will contribute to the unification of biological information. As the amount of biological information has grown, it has examining microarray expression data, sequencing genotypes become increasingly important to describe and classify bio- from a population, or identifying all glycolytic enzymes is logical objects in meaningful ways. -
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. -
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 Impact of Databases on Model Organism Biology Sabina Leonelli
Re-Thinking Organisms: The Impact of Databases on Model Organism Biology Sabina Leonelli (corresponding author) ESRC Centre for Genomics in Society, University of Exeter Byrne House, St Germans Road, EX4 4PJ Exeter, UK. Tel: 0044 1392 269137 Fax: 0044 1392 269135 Email: [email protected] Rachel A. Ankeny School of History and Politics, University of Adelaide 423 Napier, Adelaide 5005 SA, AUSTRALIA. Tel: 0061 8 8303 5570 Fax: 0061 8 8303 3443 Email: [email protected] ‘Databases for model organisms promote data integration through the development and implementation of nomenclature standards, controlled vocabularies and ontologies, that allow data from different organisms to be compared and contrasted’ (Carole Bult 2002, 163) Abstract Community databases have become crucial to the collection, ordering and retrieval of data gathered on model organisms, as well as to the ways in which these data are interpreted and used across a range of research contexts. This paper analyses the impact of community databases on research practices in model organism biology by focusing on the history and current use of four community databases: FlyBase, Mouse Genome Informatics, WormBase and The Arabidopsis Information Resource. We discuss the standards used by the curators of these databases for what counts as reliable evidence, acceptable terminology, appropriate experimental set-ups and adequate materials (e.g., specimens). On the one hand, these choices are informed by the collaborative research ethos characterising most model organism communities. On the other hand, the deployment of these standards in databases reinforces this ethos and gives it concrete and precise instantiations by shaping the skills, practices, values and background knowledge required of the database users. -
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. -
Summary Visualisations of Gene Ontology Terms with GO-Figure!
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.02.408534; this version posted December 3, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Summary Visualisations of Gene Ontology Terms with GO-Figure! Maarten JMF Reijnders1*, Robert M Waterhouse1* 1Department of Ecology and Evolution, University of Lausanne, and Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland * Correspondence: [email protected] (MJMFR), [email protected] (RMW) Keywords: functional genomics, Python software, redundancy reduction, semantic similarity, GO term enrichment Abstract The Gene Ontology (GO) is a cornerstone of functional genomics research that drives discoveries through knowledge-informed computational analysis of biological data from large- scale assays. Key to this success is how the GO can be used to support hypotheses or conclusions about the biology or evolution of a study system by identifying annotated functions that are overrepresented in subsets of genes of interest. Graphical visualisations of such GO term enrichment results are critical to aid interpretation and avoid biases by presenting researchers with intuitive visual data summaries. Amongst current visualisation tools and resources there is a lack of standalone open-source software solutions that facilitate systematic comparisons of multiple lists of GO terms. To address this we developed GO-Figure!, an open-source Python software for producing user-customisable semantic similarity scatterplots of redundancy-reduced GO term lists. The lists are simplified by grouping together GO terms with similar functions using their quantified information contents and semantic similarities, with user-control over grouping thresholds. -
Genedb and Wikidata[Version 1; Peer Review: 1 Approved, 1 Approved with Reservations]
Wellcome Open Research 2019, 4:114 Last updated: 20 OCT 2020 SOFTWARE TOOL ARTICLE GeneDB and Wikidata [version 1; peer review: 1 approved, 1 approved with reservations] Magnus Manske , Ulrike Böhme , Christoph Püthe , Matt Berriman Parasites and Microbes, Wellcome Trust Sanger Institute, Cambridge, CB10 1SA, UK v1 First published: 01 Aug 2019, 4:114 Open Peer Review https://doi.org/10.12688/wellcomeopenres.15355.1 Latest published: 14 Oct 2019, 4:114 https://doi.org/10.12688/wellcomeopenres.15355.2 Reviewer Status Abstract Invited Reviewers Publishing authoritative genomic annotation data, keeping it up to date, linking it to related information, and allowing community 1 2 annotation is difficult and hard to support with limited resources. Here, we show how importing GeneDB annotation data into Wikidata version 2 allows for leveraging existing resources, integrating volunteer and (revision) report report scientific communities, and enriching the original information. 14 Oct 2019 Keywords GeneDB, Wikidata, MediaWiki, Wikibase, genome, reference, version 1 annotation, curation 01 Aug 2019 report report 1. Sebastian Burgstaller-Muehlbacher , This article is included in the Wellcome Sanger Medical University of Vienna, Vienna, Austria Institute gateway. 2. Andra Waagmeester , Micelio, Antwerp, Belgium Any reports and responses or comments on the article can be found at the end of the article. Corresponding author: Magnus Manske ([email protected]) Author roles: Manske M: Conceptualization, Methodology, Software, Writing – Original Draft Preparation; Böhme U: Data Curation, Validation, Writing – Review & Editing; Püthe C: Project Administration, Writing – Review & Editing; Berriman M: Resources, Writing – Review & Editing Competing interests: No competing interests were disclosed. Grant information: This work was supported by the Wellcome Trust through a Biomedical Resources Grant to MB [108443]. -
Representing Glycophenotypes: Semantic Unification of Glycobiology Resources for Disease Discovery
Representing glycophenotypes: semantic unification of glycobiology resources for disease discovery Authors: Jean-Philippe F. Gourdine1,2,3*, Matthew H. Brush1,3, Nicole A. Vasilevsky1,3, Kent Shefchek3,4, Sebastian Köhler3,5, Nicolas Matentzoglu3,6, Monica C. Munoz-Torres3,4, Julie A. McMurry3,4, Xingmin Aaron Zhang3,7, Melissa A. Haendel1,3,4 Affiliations: 1 Oregon Clinical & Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA. 2 Oregon Health & Science University Library, Portland, OR 97239, USA. 3 Monarch Initiative, monarchinitiative.org. 4 Linus Pauling institute, Oregon State University, Corvallis, OR, USA. 5 Charité Centrum für Therapieforschung, Charité-Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany. 6 European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK. 7 The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA. Correspondence: [email protected] Abstract While abnormalities related to carbohydrates (glycans) are frequent for patients with rare and undiagnosed diseases as well as in many common diseases, these glycan-related phenotypes (glycophenotypes) are not well represented in knowledge bases (KBs). If glycan related diseases were more robustly represented and curated with glycophenotypes, these could be used for molecular phenotyping to help realize the goals of precision medicine. Diagnosis of rare diseases by computational cross-species comparison of genotype- phenotype data has been facilitated by leveraging ontological representations of clinical phenotypes, using Human Phenotype Ontology (HPO), and model organism ontologies such as Mammalian Phenotype Ontology (MP) in the context of the Monarch Initiative. In this article, we discuss the importance and complexity of glycobiology, and review the structure of glycan-related content from existing KBs and biological ontologies. -
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.