Arteria: An automation system for a sequencing core facility

Abstract Background 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 proces...

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Published in:	Gigascience Vol. 8; no. 12
Main Authors:	Dahlberg, Johan, Hermansson, Johan, Sturlaugsson, Steinar, Lysenkova, Mariya, Smeds, Patrik, Ladenvall, Claes, Guimera, Roman Valls, Reisinger, Florian, Hofmann, Oliver, Larsson, Pontus
Format:	Journal Article
Language:	English
Published:	United States Oxford University Press 01.12.2019
Subjects:	Automation Bioinformatics Cancer research Data analysis Data processing Electronic Data Processing - methods High-Throughput Nucleotide Sequencing - methods Human error Humans Infrastructure Laboratories Medical research Modular design Nucleotides Open source software Public domain Python Science Sequences Software Technical Note Workflow automation orchestration workflows sequencing
ISSN:	2047-217X, 2047-217X
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Abstract	Abstract Background 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. Findings 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 https://arteria-project.github.io/ . Conclusions 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.
AbstractList	Background 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. Findings 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 https://arteria-project.github.io/ . Conclusions 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. 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 https://arteria-project.github.io/ . 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. Abstract Background 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. Findings 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 https://arteria-project.github.io/ . Conclusions 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. Background: 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. Findings: 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 https://arteria-project.github.io/. Conclusions: 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. 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.BACKGROUNDIn 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 https://arteria-project.github.io/ .FINDINGSArteria 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 https://arteria-project.github.io/ .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.CONCLUSIONSWe 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.
Author	Sturlaugsson, Steinar Hermansson, Johan Dahlberg, Johan Lysenkova, Mariya Ladenvall, Claes Smeds, Patrik Reisinger, Florian Hofmann, Oliver Larsson, Pontus Guimera, Roman Valls
AuthorAffiliation	1 Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University , Box 1432, BMC 751 44, Uppsala, Sweden 2 Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University , Rudbecklaboratoriet, 751 84, Uppsala, Sweden 3 University of Melbourne Center for Cancer Research, University of Melbourne , Victorian Comprehensive Cancer Centre, Level 10, UMCCR, 305 Grattan St, Melbourne VIC 3000, Australia
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Snippet	Abstract Background In recent years, nucleotide sequencing has become increasingly instrumental in both research and clinical settings. This has led to an... In recent years, nucleotide sequencing has become increasingly instrumental in both research and clinical settings. This has led to an explosive growth in... Background In recent years, nucleotide sequencing has become increasingly instrumental in both research and clinical settings. This has led to an explosive... Background: In recent years, nucleotide sequencing has become increasingly instrumental in both research and clinical settings. This has led to an explosive...
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SubjectTerms	Automation Bioinformatics Cancer research Data analysis Data processing Electronic Data Processing - methods High-Throughput Nucleotide Sequencing - methods Human error Humans Infrastructure Laboratories Medical research Modular design Nucleotides Open source software Public domain Python Science Sequences Software Technical Note Workflow
Title	Arteria: An automation system for a sequencing core facility
URI	https://www.ncbi.nlm.nih.gov/pubmed/31825479 https://www.proquest.com/docview/2715816047 https://www.proquest.com/docview/3169745008 https://www.proquest.com/docview/2324917659 https://pubmed.ncbi.nlm.nih.gov/PMC6905352 https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-357972
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