Arteria: An automation system for a sequencing core facility.

Dahlberg J, Hermansson J, Sturlaugsson S, Lysenkova M, Smeds P, Ladenvall C, Guimera RV, Reisinger F, Hofmann O, Larsson P

Gigascience 8 (12) - [2019-12-01; online 2019-12-12]

In recent years, nucleotide sequencing has become increasingly instrumental in both research and clinical settings. This has led to an explosive growth in sequencing data produced worldwide. As the amount of data increases, so does the need for automated solutions for data processing and analysis. The concept of workflows has gained favour in the bioinformatics community, but there is little in the scientific literature describing end-to-end automation systems. Arteria is an automation system that aims at providing a solution to the data-related operational challenges that face sequencing core facilities. Arteria is built on existing open source technologies, with a modular design allowing for a community-driven effort to create plug-and-play micro-services. In this article we describe the system, elaborate on the underlying conceptual framework, and present an example implementation. Arteria can be reduced to 3 conceptual levels: orchestration (using an event-based model of automation), process (the steps involved in processing sequencing data, modelled as workflows), and execution (using a series of RESTful micro-services). This creates a system that is both flexible and scalable. Arteria-based systems have been successfully deployed at 3 sequencing core facilities. The Arteria Project code, written largely in Python, is available as open source software, and more information can be found at . We describe the Arteria system and the underlying conceptual framework, demonstrating how this model can be used to automate data handling and analysis in the context of a sequencing core facility.

Clinical Genomics Uppsala [Technology development]

NGI Uppsala (SNP&SEQ Technology Platform) [Technology development]

National Genomics Infrastructure [Technology development]

PubMed 31825479

DOI 10.1093/gigascience/giz135

Crossref 10.1093/gigascience/giz135

pii: 5673459
pmc: PMC6905352

Publications 9.5.0