ArticleBMC bioinformatics2012
VarioML framework for comprehensive variation data representation and exchange.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Biomedical Informatics on the Cloud: A Treasure Hunt for Advancing Cardiovascular Medicine.Circulation research · 2018Review
- PON-P and PON-P2 predictor performance in CAGI challenges: Lessons learned.Human mutation · 2017Article
- Expanded national database collection and data coverage in the FINDbase worldwide database for clinically relevant genomic variation allele frequencies.Nucleic acids research · 2017Article
- Human genotype-phenotype databases: aims, challenges and opportunities.Nature reviews. Genetics · 2015Review
- Standard development at the Human Variome Project.Database : the journal of biological databases and curation · 2015Article
- Next generation phenotyping using the unified medical language system.JMIR medical informatics · 2014Article
- Variation Ontology for annotation of variation effects and mechanisms.Genome research · 2014Article
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Authors and funding
17 authors.
Funding
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Abstract
backgroundSharing of data about variation and the associated phenotypes is a critical need, yet variant information can be arbitrarily complex, making a single standard vocabulary elusive and re-formatting difficult. Complex standards have proven too time-consuming to implement.
resultsThe GEN2PHEN project addressed these difficulties by developing a comprehensive data model for capturing biomedical observations, Observ-OM, and building the VarioML format around it. VarioML pairs a simplified open specification for describing variants, with a toolkit for adapting the specification into one's own research workflow. Straightforward variant data can be captured, federated, and exchanged with no overhead; more complex data can be described, without loss of compatibility. The open specification enables push-button submission to gene variant databases (LSDBs) e.g., the Leiden Open Variation Database, using the Cafe Variome data publishing service, while VarioML bidirectionally transforms data between XML and web-application code formats, opening up new possibilities for open source web applications building on shared data. A Java implementation toolkit makes VarioML easily integrated into biomedical applications. VarioML is designed primarily for LSDB data submission and transfer scenarios, but can also be used as a standard variation data format for JSON and XML document databases and user interface components.
conclusionsVarioML is a set of tools and practices improving the availability, quality, and comprehensibility of human variation information. It enables researchers, diagnostic laboratories, and clinics to share that information with ease, clarity, and without ambiguity.
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