Arteria: An automation system for a sequencing core facility.

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Nucleotide sequencing generates vast amounts of data, necessitating automated solutions for processing and analysis.
• Existing scientific literature lacks comprehensive descriptions of end-to-end automation systems for sequencing data.
• Sequencing core facilities encounter significant data-related operational challenges.

Purpose of the Study:

• To describe Arteria, an automation system designed to address data challenges in sequencing core facilities.
• To elaborate on the conceptual framework and implementation of the Arteria system.
• To demonstrate the utility of Arteria for automating data handling and analysis in a core facility setting.

Main Methods:

• Arteria utilizes existing open-source technologies with a modular design for plug-and-play micro-services.
• The system operates on three conceptual levels: orchestration (event-based automation), process (workflow modeling), and execution (RESTful micro-services).
• The Arteria Project code is primarily written in Python and is available as open-source software.

Main Results:

• Arteria offers a flexible and scalable solution for automating sequencing data.
• The system has been successfully deployed in three sequencing core facilities.
• The modular design facilitates community-driven development of micro-services.

Conclusions:

• Arteria provides a robust framework for automating data handling and analysis in sequencing core facilities.
• The system's conceptual levels enable efficient and adaptable data management.
• The open-source nature of Arteria promotes collaborative development and widespread adoption.