Abstract
Low-cost and widely available Next-Generation Sequencing (NGS) is revolutionizing clinical practice, paving the way for the realization of precision medicine. Applying NGS to clinical practice requires establishing a complex loop involving sample collection and sequencing, computational processing of the NGS outputs to identify variants, and the interpretation of the variants to establish their significance for the condition being treated. The computational tools that perform variant calling have been extensively used in bioinformatics, but there are few attempts to integrate them in a comprehensive, production-grade, Cloud-native infrastructure able to scale to national levels. Furthermore, there are no established interfaces for closing the loop between NGS machines, computational infrastructure, and variant interpretation experts.
We present here the platform developed for the Greek National Precision Medicine Network for Oncology. The platform integrates bioinformatics tools and their orchestration, makes provisions for both experimental and clinical usage of variant calling pipelines, provides programmatic interfaces for integration with NGS machines and for analytics, and provides user interfaces for supporting variant interpretation. We also present benchmarking results and discuss how these results confirm the soundness of our architectural and implementation choices.
The work described here has received funding from the Greek General Secretariat for Research and Innovation in the context of the Hellenic Network of Precision Medicine on Cancer. See also https://oncopmnet.gr for more details.
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An exhaustive list of annotation databases used with VEP’s default configuration can be found here:
https://www.ensembl.org/info/docs/tools/vep/script/VEP_script_documentation.pdf.
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This plugin retrieves LOVD variation data from http://www.lovd.nl.
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References
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Fokkema, I.F., Taschner, P.E., Schaafsma, G.C., Celli, J., Laros, J.F., den Dunnen, J.T.: LOVD v.2.0: the next generation in gene variant databases. Hum. Mutat. 32(5), 557–563 (2011)
McLaren, W., et al.: The ensembl variant effect predictor. Genome Biol. 17(1), 1–14 (2016)
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Mouchakis, G. et al. (2021). A Cloud-Native NGS Data Processing and Annotation Platform. In: Rezig, E.K., et al. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH Poly 2021 2021. Lecture Notes in Computer Science(), vol 12921. Springer, Cham. https://doi.org/10.1007/978-3-030-93663-1_10
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DOI: https://doi.org/10.1007/978-3-030-93663-1_10
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