GA4GH Phenopackets: A Practical Introduction

Ladewig, Markus S.; Jacobsen, Julius O. B.; Wagner, Alex H.; Daniš, Daniel; Kassaby, Baha El; Gargano, Michael; Groza, Tudor; Baudis, Michael; Steinhaus, Robin; Seelow, Dominik; Bechrakis, Nikolaos E.; Mungall, Christopher J.; Schofield, Paul N.; Elemento, Olivier; Smith, Lindsay; McMurry, Julie A.; Muñoz-Torres, Monica; Haendel, Melissa; Robinson, Peter N.

doi:10.1002/ggn2.202200016

Cited by 9 publications

(10 citation statements)

References 35 publications

(38 reference statements)

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“…This includes the FAIR Data Point [16] and Vantage6 [22]. Further elements of the FDCube are the Investigation-Study-Assay (ISA) metadata framework [27,20] for capturing general study metadata, sample (including basic sample characteristics), and assay metadata, and the Phenopackets [21] standards for capturing phenotypic description of a patient/sample. The concept of the FDCube is illustrated in Fig 1 and detailed below from the perspective of a dataset owner and a researcher as a user of that dataset, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Thus, phenotype data need to be standardized as well, so that researchers and clinicians can more easily link phenotypes to experimental data. To achieve this, the Phenopackets framework [21] developed by Global Alliance for Genomics and Health (GA4GH) was adopted. This framework comprises a comprehensive data structure (model), using common ontology terms, to categorise and connect different types of phenotype data.…”

Section: Dataset Ownermentioning

confidence: 99%

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FAIR Data Cube, a FAIR data infrastructure for integrated multi-omics data analysis

Liao¹,

Niehues

Visser³

et al. 2023

Preprint

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We are witnessing an enormous growth in the amount of molecular profiling (-omics) data. The integration of multi-omics data is challenging. Moreover, human multi-omics data may be privacy-sensitive and misused to de-anonymize and (re-)identify individuals. Hence, most data is kept in secure and protected silos. Therefore, it remains a challenge to reuse these data without infringing the privacy of the individuals from which the data were derived. Federated analysis of FAIR data is a privacy-preserving solution to make optimal use of these multi-omics data and transform them into actionable knowledge. Results: The Netherlands X-omics Initiative is a National Roadmap Large-Scale Research Infrastructure aiming for efficient integration of data generated within X-omics and external datasets. To facilitate this, we developed the FAIR Data Cube (FDCube), which adopts and applies the FAIR principles and helps researchers to create FAIR data and metadata, facilitate reuse of their data, and make their data analysis workflows transparent. The FDCube also meets security-by-design and privacy-by-design principles.

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Section: Resultsmentioning

confidence: 99%

Section: Dataset Ownermentioning

confidence: 99%

FAIR Data Cube, a FAIR data infrastructure for integrated multi-omics data analysis

Liao¹,

Niehues

Visser³

et al. 2023

Preprint

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“…Phenopackets can interact with other standards for storing genetic alignment and variant level information (Table 1). A detailed practical introduction to the usage of phenopackets in the postnatal space has been previously published 47 …”

Section: Methods: How Do We Do This?mentioning

confidence: 99%

“…A detailed practical introduction to the usage of phenopackets in the postnatal space has been previously published. 47 Teams collaborating through GA4GH are creating a suite of interoperable standards around which an ecosystem of analysis tools can be created. Building on top of the Phenopacket and VCF standards, the Beacon protocol is an application programming interface that provides ways to structure and search on genomic and phenotypic datasets, without requiring access to raw data.…”

Section: Data Ingestion Structure and Interoperable Standardsmentioning

confidence: 99%

Improving prenatal diagnosis through standards and aggregation

Duyzend,

Cacheiro,

Jacobsen

et al. 2024

Prenatal Diagnosis

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Advances in sequencing and imaging technologies enable enhanced assessment in the prenatal space, with a goal to diagnose and predict the natural history of disease, to direct targeted therapies, and to implement clinical management, including transfer of care, election of supportive care, and selection of surgical interventions. The current lack of standardization and aggregation stymies variant interpretation and gene discovery, which hinders the provision of prenatal precision medicine, leaving clinicians and patients without an accurate diagnosis. With large amounts of data generated, it is imperative to establish standards for data collection, processing, and aggregation. Aggregated and homogeneously processed genetic and phenotypic data permits dissection of the genomic architecture of prenatal presentations of disease and provides a dataset on which data analysis algorithms can be tuned to the prenatal space. Here we discuss the importance of generating aggregate data sets and how the prenatal space is driving the development of interoperable standards and phenotype‐driven tools.

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“…A phenopacket can contain one or more Interpretation elements that specify interpretations of genomic findings. We refer to the online documentation and to our detailed introduction [ 23 ] for further details. We provide constants for one field that is used in the Interpretation .…”

Section: Methodsmentioning

confidence: 99%

Phenopacket-tools: Building and validating GA4GH Phenopackets

et al. 2023

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The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.

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GA4GH Phenopackets: A Practical Introduction

Cited by 9 publications

References 35 publications

FAIR Data Cube, a FAIR data infrastructure for integrated multi-omics data analysis

FAIR Data Cube, a FAIR data infrastructure for integrated multi-omics data analysis

Improving prenatal diagnosis through standards and aggregation

Phenopacket-tools: Building and validating GA4GH Phenopackets

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