Abstract
Current high-throughput sequencing technologies allow us to acquire entire genomes in a very short time and at a relatively sustainable cost, thus resulting in an increasing diffusion of genetic test capabilities, in specialized clinical laboratories and research centers. In contrast, it is still limited the impact of genomic information on clinical decisions, as an effective interpretation is a challenging task. From the technological point of view, genomic data are big in size, have a complex granular nature and strongly depend on the computational steps of the generation and processing workflows. This article introduces our work to create the openEHR Genomic Project and the set of genomic information models we developed to catch such complex structure and to preserve data provenance efficiently in a machine-readable format. The models support clinical actionability of data, by improving their quality, fostering interoperability and laying the basis for re-usability.
Keywords: genomic models; mutations; openEHR; structured data; variations.
Similar articles
- OpenEHR modeling for genomics in clinical practice.Int J Med Inform. 2018 Dec;120:147-156. doi: 10.1016/j.ijmedinf.2018.10.007. Epub 2018 Oct 17.PMID: 30409340
- Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv.Gigascience. 2019 Nov 1;8(11):giz095. doi: 10.1093/gigascience/giz095.PMID: 31675414 Free PMC article.
- Challenges in Design and Creation of Genetic openEHR-Archetype.Stud Health Technol Inform. 2018;247:835-839.PMID: 29678078
- [Development of antituberculous drugs: current status and future prospects].Kekkaku. 2006 Dec;81(12):753-74.PMID: 17240921 Review. Japanese.
- [Aiming for zero blindness].Nippon Ganka Gakkai Zasshi. 2015 Mar;119(3):168-93; discussion 194.PMID: 25854109 Review. Japanese.
No hay comentarios:
Publicar un comentario