Release of the GHGA Metadata Model v1.0 and White Paper
- 18 Sep 2023
- Karoline Mauer
Over the recent months, we have undertaken a refactoring of our GHGA Metadata Model, elevating it to version 1.0. This iteration places a strong emphasis on submissions and incorporates standardised terminologies into its core structure. We have introduced new entities, including "Condition" for categorising samples based on health status or treatments, and "Sequencing Process" for establishing links between samples and their associated research data or sequencing files.
We are dedicated to improving the submission process and therefore expanded the used controlled vocabularies. We continue to provide submission spreadsheets that are now automatically generated from the submission YAML file. Both the model and the spreadsheet remain steadfast in supporting the thorough organisation of non-personal metadata within the GHGA Archive.
The white paper describes the development of the GHGA Metadata Model. In addition to an elaborate explanation of the framework, our modules and the entities and properties grouped in them, we go into detail about the implemented ontologies and standards. The white paper also includes a section on data privacy of the collected metadata. In the last chapter, we put GHGA and the used ontologies for phenotype description, medical terminologies and omics experiments into the national and international context by comparing the implementation of standards with other consortia in Germany, Europe and the USA. We place a special focus on the differences and similarities between the metadata schemas of GHGA and EGA.
The metadata model will be continuously developed based on new community requirements. We will update the whitepaper should this become necessary.
Iyappan, Anandhi, Mauer, Karoline, Menges, Paul, Sürün, Bilge, Tremper, Galina, Parker, Simon, Kırlı, Koray, Kraus, Florian, Unni, Deepak, Schultze, Joachim L., Bork, Peer, Ulas, Thomas, Nahnsen, Sven, & The GHGA Consortium. (2023). Metadata Schema for the German Human Genome-Phenome Archive (Version 1). Zenodo. https://doi.org/10.5281/zenodo.8341224