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      An effective biomedical data migration tool from resource description framework to JSON

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          Abstract

          Resource Description Framework (RDF) is widely used for representing biomedical data in practical applications. With the increases of RDF-based applications, there is an emerging requirement of novel architectures to provide effective supports for the future RDF data explosion. Inspired by the success of the new designs in National Center for Biotechnology Information dbSNP (The Single Nucleotide Polymorphism Database) for managing the increasing data volumes using JSON (JavaScript Object Notation), in this paper we present an effective mapping tool that allows data migrations from RDF to JSON for supporting future massive data explosions and releases. We firstly introduce a set of mapping rules, which transform an RDF format into the JSON format, and then present the corresponding transformation algorithm. On this basis, we develop an effective and user-friendly tool called RDF2JSON, which enables automating the process of RDF data extractions and the corresponding JSON data generations.

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          Bio2RDF: towards a mashup to build bioinformatics knowledge systems.

          Francois Belleau, Marc-Alexandre Nolin, Nicole Tourigny … (2008)
          Presently, there are numerous bioinformatics databases available on different websites. Although RDF was proposed as a standard format for the web, these databases are still available in various formats. With the increasing popularity of the semantic web technologies and the ever growing number of databases in bioinformatics, there is a pressing need to develop mashup systems to help the process of bioinformatics knowledge integration. Bio2RDF is such a system, built from rdfizer programs written in JSP, the Sesame open source triplestore technology and an OWL ontology. With Bio2RDF, documents from public bioinformatics databases such as Kegg, PDB, MGI, HGNC and several of NCBI's databases can now be made available in RDF format through a unique URL in the form of http://bio2rdf.org/namespace:id. The Bio2RDF project has successfully applied the semantic web technology to publicly available databases by creating a knowledge space of RDF documents linked together with normalized URIs and sharing a common ontology. Bio2RDF is based on a three-step approach to build mashups of bioinformatics data. The present article details this new approach and illustrates the building of a mashup used to explore the implication of four transcription factor genes in Parkinson's disease. The Bio2RDF repository can be queried at http://bio2rdf.org.
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            BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models

            Chen Li, Marco Donizelli, Nicolas Rodriguez … (2010)
            Background Quantitative models of biochemical and cellular systems are used to answer a variety of questions in the biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in the use of models as well as the development of improved software systems and the availability of better, cheaper computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in these repositories should be extensively tested and encoded in community-supported and standardised formats. In addition, the models and their components should be cross-referenced with other resources in order to allow their unambiguous identification. Description BioModels Database http://www.ebi.ac.uk/biomodels/ is aimed at addressing exactly these needs. It is a freely-accessible online resource for storing, viewing, retrieving, and analysing published, peer-reviewed quantitative models of biochemical and cellular systems. The structure and behaviour of each simulation model distributed by BioModels Database are thoroughly checked; in addition, model elements are annotated with terms from controlled vocabularies as well as linked to relevant data resources. Models can be examined online or downloaded in various formats. Reaction network diagrams generated from the models are also available in several formats. BioModels Database also provides features such as online simulation and the extraction of components from large scale models into smaller submodels. Finally, the system provides a range of web services that external software systems can use to access up-to-date data from the database. Conclusions BioModels Database has become a recognised reference resource for systems biology. It is being used by the community in a variety of ways; for example, it is used to benchmark different simulation systems, and to study the clustering of models based upon their annotations. Model deposition to the database today is advised by several publishers of scientific journals. The models in BioModels Database are freely distributed and reusable; the underlying software infrastructure is also available from SourceForge https://sourceforge.net/projects/biomodels/ under the GNU General Public License.
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              The EBI RDF platform: linked open data for the life sciences

              Simon Jupp, James M. Malone, Jerven Bolleman … (2014)
              Motivation: Resource description framework (RDF) is an emerging technology for describing, publishing and linking life science data. As a major provider of bioinformatics data and services, the European Bioinformatics Institute (EBI) is committed to making data readily accessible to the community in ways that meet existing demand. The EBI RDF platform has been developed to meet an increasing demand to coordinate RDF activities across the institute and provides a new entry point to querying and exploring integrated resources available at the EBI. Availability: http://www.ebi.ac.uk/rdf Contact: jupp@ebi.ac.uk
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                Author and article information

                Journal
                Journal ID (nlm-ta): Database (Oxford)
                Journal ID (iso-abbrev): Database (Oxford)
                Journal ID (publisher-id): databa
                Title: Database: The Journal of Biological Databases and Curation
                Publisher: Oxford University Press
                ISSN (Electronic): 1758-0463
                Publication date Collection: 2019
                Publication date (Electronic): 25 July 2019
                Publication date PMC-release: 25 July 2019
                Volume: 2019
                Electronic Location Identifier: baz088
                Affiliations
                [1 ]School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
                [2 ]Zhejiang University of Science and Technology, Hangzhou, China
                [3 ]Department of Hematology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
                Author notes
                Corresponding author. Tel: +86-18686896700; Email: jianliu@ 123456hit.edu.cn
                Author information
                http://orcid.org/0000-0002-0137-7382
                Article
                Publisher ID: baz088
                DOI: 10.1093/database/baz088
                PMC ID: 6657663
                PubMed ID: 31343683
                SO-VID: df50e43b-0249-43cd-a92d-166b17f53d09
                Copyright © © The Author(s) 2019. Published by Oxford University Press.
                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 received : 21 March 2019
                Date revision received : 6 June 2019
                Date accepted : 9 June 2019
                Page count
                Pages: 09
                Funding
                Funded by: Fundamental Research Funds for the Central Universities 10.13039/501100012226
                Award ID: HIT.NSRIF.2017036
                Funded by: Heilongjiang Youth Development Foundation 10.13039/100012549
                Award ID: QC2015067
                Funded by: Heilongjiang Postdoctoral Fund
                Award ID: LBH-Z14089
                Funded by: China Postdoctoral Science Foundation 10.13039/501100002858
                Award ID: 2016T90294
                Award ID: 2015M581449
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 61872115
                Award ID: 61602130
                Funded by: National Key R&D Program of China
                Award ID: 2018YFC1603802
                Award ID: 2018YFC1603800
                Award ID: 2017YFC1200205
                Award ID: 2017YFC1200200
                Categories
                Subject: Original Article

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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