SORTA: a system for ontology-based re-coding and technical annotation of biomedical phenotype data

Pang, Chao; Sollie, Annet; Sijtsma, Anna; Hendriksen, Dennis; Charbon, Bart; Haan, Mark de; Boer, Tommy de; Kelpin, Fleur D.L.; Jetten, Jonathan; Velde, K. Joeri van der; Smidt, Nynke; Sijmons, Rolf H.; Hillege, Hans L.; Swertz, Morris A.

doi:10.1093/database/bav089

Cited by 34 publications

(31 citation statements)

References 18 publications

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“…Furthermore, the processing and implementation of algorithms through Opal, support the transformation of study specific variables into common formats. Another framework, namely the System for Ontology-based Re-coding and Technical Annotation (SORTA) [49], has been developed for the annotation of biomedical and phenotype data. SORTA overcomes the problem of semantic heterogeneity and matches original data values to a target scheme.…”

Section: Definition Of the Target Variables And Data Processingmentioning

confidence: 99%

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective

Κούρου

Pezoulas

Georga

et al. 2019

IEEE Rev. Biomed. Eng.

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In this review the critical parts and milestones for data harmonization, from the biomedical engineering perspective, are outlined. The need for data sharing between heterogeneous sources pave the way for cohort harmonization; thus, fostering data integration and interdisciplinary research. Unmet needs in chronic as well as in other diseases, can be addressed based on the integration of patient health records and the sharing of information of the clinical picture and outcome. The stratification of patients, the determination of various clinical and outcome features and the identification of novel biomarkers for the different phenotypes of the disease characterize the impact of cohort harmonization in patient-centered clinical research and in precision medicine. Subsequently, the establishment of matching techniques and ontologies for the creation of data schemas are also presented. The exploitation of web technologies and data-collection tools support the opportunities to achieve new levels of integration and interoperability. Ethical and legal issues which arise when sharing and harmonizing individual-level data are discussed in order to evaluate the harmonization potential. Use cases that shape and test the harmonization approach are explicitly analyzed along with their significant results on their research objectives. Finally, future trends and directions are discussed and critically reviewed towards a roadmap in cohort harmonization for clinical medicine.

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Section: Definition Of the Target Variables And Data Processingmentioning

confidence: 99%

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective

Κούρου

Pezoulas

Georga

et al. 2019

IEEE Rev. Biomed. Eng.

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“…HPO terms were directly matched to UKBB phenotypes when phenotypes in both systems had similar terminology. The direct phenotype matching was conducted using a semi-automatic mapping system which combines semantic and lexical similarity between word [32] followed by manual curation. When the HPO terms were not present, we performed an indirect matching by hand to find in the UKBB, the phenotype that best reflects the target HPO terms.…”

Section: Phenotypes Of Target Syndromesmentioning

confidence: 99%

A phenome-wide association study of four syndromic genes reveals pleiotropic effects of common and rare variants in the general population

Tcheandjieu

Aguirre²,

Gustafsson

et al. 2019

Preprint

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The clinical evaluation of a genetic syndrome relies upon recognition of a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. However, individuals displaying a few phenotypes of a complex syndrome may not meet criteria for clinical diagnosis or genetic testing. Here, we present a phenome-wide association study (PheWAS) approach to systematically explore pleiotropy of common and rare alleles in genes associated with four well-described syndromic diseases (Alagille (AS), Marfan (MS), DiGeorge (DS), and Noonan (NS) syndromes) in the general population. Using human phenotype ontology (HPO) terms, we systematically mapped 60 phenotypes related to AS, MS, DS and NS in 337,198 unrelated white British from the UK Biobank (UKBB) based on their hospital admission records, self-administrated questionnaires, and physiological measurements. We performed logistic regression adjusting for age, sex, and the first 5 genetic principal components, for each phenotype and each variant in the target genes (JAG1, TBX1, FBN1, PTPN11, NOTCH2, and MAP2K1) and performed a gene burden testing. Overall, we observed multiple phenotype-genotype correlations, such as the association between variation in JAG1, FBN1, PTPN11 and SOS2 with diastolic and systolic blood pressure; and pleiotropy among multiple variants in syndromic genes. For example, rs11066309 in PTPN11 was significantly associated with a lower body mass index, an increased risk of hypothyroidism and a smaller size for gestational age, all in concordance with NS-related phenotypes. Similarly, rs589668 in FBN1 was associated with an increase in body height and blood pressure, and a reduced body fat percentage as observed in Marfan syndrome. Our findings suggest that the spectrum of associations of common and rare variants in genes involved in syndromic diseases can be extended to individual phenotypes within the general population.

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“…For example, the Biobanking and Rare disease communities will be given end-user tools that utilize/generate such FAIR infrastructures to: guide discovery by researchers; help both biobankers and researchers to re-code their data to standard ontologies building on the SORTA system (Pang et al, 2015); assist to extend the MOLGENIS/BiobankConnect system (Pang et al, 2016); add FAIR interfaces to the BBMRI (Biobanking and BioMolecular resources Research Infrastructure) and RD-connect national and European biobank data and sample catalogues. There are also a core group of FAIR infrastructure authors who are creating large-scale indexing and discovery systems that will facilitate the automated identification and retrieval of relevant information, from any repository, in response to end-user queries, portending a day when currently unused-''lost''-data deposits once again provide return-on-investment through their discovery and reuse.…”

Section: Incentives and Barriers To Implementationmentioning

confidence: 99%

Interoperability and FAIRness through a novel combination of Web technologies

Wilkinson

Verborgh

Santos³

et al. 2017

PeerJ Computer Science

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Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity and security considerations. For example, clinical observations about genetic mutations in patients are highly sensitive, while observations of species diversity are generally not. The lack of uniformity in data models from one repository to another, and in the richness and availability of metadata descriptions, makes integration and analysis of these data a manual, time-consuming task with no scalability. Here we explore a set of resource-oriented Web design patterns for data discovery, accessibility, transformation, and integration that can be implemented by any general-or specialpurpose repository as a means to assist users in finding and reusing their data holdings. We show that by using off-the-shelf technologies, interoperability can be achieved atthe level of an individual spreadsheet cell. We note that the behaviours of this architecture compare favourably to the desiderata defined by the FAIR Data Principles, and can therefore represent an exemplar implementation of those principles. The proposed interoperability design patterns may be used to improve discovery and integration of both new and legacy data, maximizing the utility of all scholarly outputs.

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SORTA: a system for ontology-based re-coding and technical annotation of biomedical phenotype data

Cited by 34 publications

References 18 publications

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective

A phenome-wide association study of four syndromic genes reveals pleiotropic effects of common and rare variants in the general population

Interoperability and FAIRness through a novel combination of Web technologies

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