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      • Record: found
      • Abstract: found
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      Open Targets: a platform for therapeutic target identification and validation

      research-article
      Author(s): 1 , 2 , * , 1 , 3 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 3 , 1 , 2 , 1 , 4 , 1 , 4 , 1 , 3 , 1 , 4 , 1 , 4 , 1 , 3 , 1 , 4 , 1 , 3 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 5 , 1 , 4 , 1 , 4 , 1 , 4 , 6 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 ,   1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 5 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 4 , 1 , 2 , 4 , 1 , 4 , 1 , 4 , 5 , 1 , 4 , *
      Publication date (Electronic):
      Journal: Nucleic Acids Research
      Publisher: Oxford University Press

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          Abstract

          We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.

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          Most cited references23

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          Modeling sample variables with an Experimental Factor Ontology

          James P. Malone, Ele Holloway, Tomasz Adamusiak … (2010)
          Motivation: Describing biological sample variables with ontologies is complex due to the cross-domain nature of experiments. Ontologies provide annotation solutions; however, for cross-domain investigations, multiple ontologies are needed to represent the data. These are subject to rapid change, are often not interoperable and present complexities that are a barrier to biological resource users. Results: We present the Experimental Factor Ontology, designed to meet cross-domain, application focused use cases for gene expression data. We describe our methodology and open source tools used to create the ontology. These include tools for creating ontology mappings, ontology views, detecting ontology changes and using ontologies in interfaces to enhance querying. The application of reference ontologies to data is a key problem, and this work presents guidelines on how community ontologies can be presented in an application ontology in a data-driven way. Availability: http://www.ebi.ac.uk/efo Contact: malone@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
          Article 0 comments Cited 258 times – based on 0 reviews     Review now
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            STITCH 4: integration of protein–chemical interactions with user data

            Michael Kuhn, Damian Szklarczyk, Sune Pletscher-Frankild … (2013)
            STITCH is a database of protein–chemical interactions that integrates many sources of experimental and manually curated evidence with text-mining information and interaction predictions. Available at http://stitch.embl.de, the resulting interaction network includes 390 000 chemicals and 3.6 million proteins from 1133 organisms. Compared with the previous version, the number of high-confidence protein–chemical interactions in human has increased by 45%, to 367 000. In this version, we added features for users to upload their own data to STITCH in the form of internal identifiers, chemical structures or quantitative data. For example, a user can now upload a spreadsheet with screening hits to easily check which interactions are already known. To increase the coverage of STITCH, we expanded the text mining to include full-text articles and added a prediction method based on chemical structures. We further changed our scheme for transferring interactions between species to rely on orthology rather than protein similarity. This improves the performance within protein families, where scores are now transferred only to orthologous proteins, but not to paralogous proteins. STITCH can be accessed with a web-interface, an API and downloadable files.
            Article 0 comments Cited 178 times – based on 0 reviews     Review now
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              In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities.

              Carlota Rubio-Perez, David Tamborero, Michael P Schroeder … (2015)
              Large efforts dedicated to detect somatic alterations across tumor genomes/exomes are expected to produce significant improvements in precision cancer medicine. However, high inter-tumor heterogeneity is a major obstacle to developing and applying therapeutic targeted agents to treat most cancer patients. Here, we offer a comprehensive assessment of the scope of targeted therapeutic agents in a large pan-cancer cohort. We developed an in silico prescription strategy based on identification of the driver alterations in each tumor and their druggability options. Although relatively few tumors are tractable by approved agents following clinical guidelines (5.9%), up to 40.2% could benefit from different repurposing options, and up to 73.3% considering treatments currently under clinical investigation. We also identified 80 therapeutically targetable cancer genes.
              Article 0 comments Cited 162 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (hwp): nar
                Journal ID (publisher-id): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date (Print): 04 January 2017
                Publication date (Electronic): 08 December 2016
                Publication date PMC-release: 08 December 2016
                Volume: 45
                Issue: Database issue , Database issue
                Pages: D985-D994
                Affiliations
                [1 ]Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
                [2 ]GSK, Medicines Research Center, Gunnels Wood Road, Stevenage, SG1 2NY, UK
                [3 ]Biogen, Cambridge, MA 02142, USA
                [4 ]European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
                [5 ]Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
                [6 ]National Center for Protein Research, No. 38, Life Science Park Road, Changping District, 102206 Beijing, China
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +44 1223 494 650; Fax: +44 1223 494 468; Email: gautier.x.koscielny@ 123456gsk.com
                Correspondence may also be addressed to Ian Dunham. Tel: +44 1223 492 636; Fax: +44 1223 494 468; Email: dunham@ 123456ebi.ac.uk
                Article
                DOI: 10.1093/nar/gkw1055
                PMC ID: 5210543
                PubMed ID: 27899665
                SO-VID: 44324582-969e-46f8-930d-34122bef383c
                Copyright © © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date accepted : 03 November 2016
                Date revision received : 19 October 2016
                Date received : 19 August 2016
                Page count
                Pages: 10
                Categories
                Subject: Database Issue
                Custom metadata
                cover-date 04 January 2017

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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