VarioML framework for comprehensive variation data representation and exchange

Byrne, Myles; Fokkema, Ivo F.A.C.; Lancaster, Owen; Adamusiak, Tomasz; Ahonen-Bishopp, Anni; Atlan, David; Béroud, Christophe; Cornell, Michael; Dalgleish, Raymond; Devereau, A.; Patrinos, George P.; Swertz, Morris A.; Taschner, Peter E.M.; Þórisson, Guðmundur Á.; Vihinen, Mauno; Brookes, Anthony J.; Muilu, Juha

doi:10.1186/1471-2105-13-254

Cited by 17 publications

(12 citation statements)

References 37 publications

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“…VariO is compliant with the VarioML variation data exchange format (http://varioml.org/) (Byrne et al 2012), which is used, e.g., in the Café Variome exchange portal (http://cafevariome.org/) for variation data produced, e.g., in diagnostic laboratories. The use of VariO has been recommended by the Human Variome Project (Kohonen-Corish et al 2010).…”

Section: Applicationsmentioning

confidence: 99%

Variation Ontology for annotation of variation effects and mechanisms

Vihinen

2013

Genome Res.

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Ontology organizes and formally conceptualizes information in a knowledge domain with a controlled vocabulary having defined terms and relationships between them. Several ontologies have been used to annotate numerous databases in biology and medicine. Due to their unambiguous nature, ontological annotations facilitate systematic description and data organization, data integration and mining, and pattern recognition and statistics, as well as development of analysis and prediction tools. The Variation Ontology (VariO) was developed to allow the annotation of effects, consequences, and mechanisms of DNA, RNA, and protein variations. Variation types are systematically organized, and a detailed description of effects and mechanisms is possible. VariO is for annotating the variant, not the normal-state features or properties, and requires a reference (e.g., reference sequence, reference-state property, activity, etc.) compared to which the changes are indicated. VariO is versatile and can be used for variations ranging from genomic multiplications to single nucleotide or amino acid changes, whether of genetic or nongenetic origin. VariO annotations are position-specific and can be used for variations in any organism.

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Section: Applicationsmentioning

confidence: 99%

Variation Ontology for annotation of variation effects and mechanisms

Vihinen

2013

Genome Res.

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“…In 2012, VarioML was developed within the scope of the GEN2PHEN project framework (www.gen2phen.org), as a set of tools and practices improving the availability, quality and comprehensibility of human variation information (18). VarioML enables researchers and diagnostic laboratories to easily and unambiguously share variant information, by adapting the variant specification into one's own research workflow in a straightforward and a simple ‘push-button’ manner.…”

Section: New Featuresmentioning

confidence: 99%

Expanded national database collection and data coverage in the FINDbase worldwide database for clinically relevant genomic variation allele frequencies

et al. 2016

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FINDbase (http://www.findbase.org) is a comprehensive data repository that records the prevalence of clinically relevant genomic variants in various populations worldwide, such as pathogenic variants leading mostly to monogenic disorders and pharmacogenomics biomarkers. The database also records the incidence of rare genetic diseases in various populations, all in well-distinct data modules. Here, we report extensive data content updates in all data modules, with direct implications to clinical pharmacogenomics. Also, we report significant new developments in FINDbase, namely (i) the release of a new version of the ETHNOS software that catalyzes development curation of national/ethnic genetic databases, (ii) the migration of all FINDbase data content into 90 distinct national/ethnic mutation databases, all built around Microsoft's PivotViewer (http://www.getpivot.com) software (iii) new data visualization tools and (iv) the interrelation of FINDbase with DruGeVar database with direct implications in clinical pharmacogenomics. The abovementioned updates further enhance the impact of FINDbase, as a key resource for Genomic Medicine applications.

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“…Phenotypic terminology and formatting should be standardized for databases. 36 In the case of cancer, the submitter should be required to state whether the corresponding healthy and tumor tissue were tested. Metadata should include the percentage of tumor in the analyzed specimen (taking into account any microdissection that may have taken place).…”

Section: Recommended Standards For Layer-2 Gmdrsmentioning

confidence: 99%

Standards for Clinical Grade Genomic Databases

Yohe

Carter

Pfeifer

et al. 2015

Archives of Pathology &Amp; Laboratory Medicine

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Context.-Next-generation sequencing performed in a clinical environment must meet clinical standards, which requires reproducibility of all aspects of the testing. Clinical-grade genomic databases (CGGDs) are required to classify a variant and to assist in the professional interpretation of clinical next-generation sequencing. Applying quality laboratory standards to the reference databases used for sequence-variant interpretation presents a new challenge for validation and curation.Objectives.-To define CGGD and the categories of information contained in CGGDs and to frame recommendations for the structure and use of these databases in clinical patient care.Design.-Members of the College of American Pathologists Personalized Health Care Committee reviewed the literature and existing state of genomic databases and developed a framework for guiding CGGD development in the future.Results.-Clinical-grade genomic databases may provide different types of information. This work group defined 3 layers of information in CGGDs: clinical genomic variant repositories, genomic medical data repositories, and genomic medicine evidence databases. The layers are differentiated by the types of genomic and medical information contained and the utility in assisting with clinical interpretation of genomic variants. Clinical-grade genomic databases must meet specific standards regarding submission, curation, and retrieval of data, as well as the maintenance of privacy and security.Conclusion.-These organizing principles for CGGDs should serve as a foundation for future development of specific standards that support the use of such databases for patient care.

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VarioML framework for comprehensive variation data representation and exchange

Cited by 17 publications

References 37 publications

Variation Ontology for annotation of variation effects and mechanisms

Variation Ontology for annotation of variation effects and mechanisms

Expanded national database collection and data coverage in the FINDbase worldwide database for clinically relevant genomic variation allele frequencies

Standards for Clinical Grade Genomic Databases

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