Design considerations for workflow management systems use in production genomics research and the clinic

Abstract The changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large volumes of data across heterogeneous computational environments. Workflow Management Systems (Wf...

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Autores principales:	Azza E. Ahmed, Joshua M. Allen, Tajesvi Bhat, Prakruthi Burra, Christina E. Fliege, Steven N. Hart, Jacob R. Heldenbrand, Matthew E. Hudson, Dave Deandre Istanto, Michael T. Kalmbach, Gregory D. Kapraun, Katherine I. Kendig, Matthew Charles Kendzior, Eric W. Klee, Nate Mattson, Christian A. Ross, Sami M. Sharif, Ramshankar Venkatakrishnan, Faisal M. Fadlelmola, Liudmila S. Mainzer
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/71063f0cca8645c6a434d845100e2c90
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spelling	oai:doaj.org-article:71063f0cca8645c6a434d845100e2c902021-11-08T10:55:45ZDesign considerations for workflow management systems use in production genomics research and the clinic10.1038/s41598-021-99288-82045-2322https://doaj.org/article/71063f0cca8645c6a434d845100e2c902021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99288-8https://doaj.org/toc/2045-2322Abstract The changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large volumes of data across heterogeneous computational environments. Workflow Management Systems (WfMSs) are the software components employed to fill this gap. This work provides an approach and systematic evaluation of key features of popular bioinformatics WfMSs in use today: Nextflow, CWL, and WDL and some of their executors, along with Swift/T, a workflow manager commonly used in high-scale physics applications. We employed two use cases: a variant-calling genomic pipeline and a scalability-testing framework, where both were run locally, on an HPC cluster, and in the cloud. This allowed for evaluation of those four WfMSs in terms of language expressiveness, modularity, scalability, robustness, reproducibility, interoperability, ease of development, along with adoption and usage in research labs and healthcare settings. This article is trying to answer, which WfMS should be chosen for a given bioinformatics application regardless of analysis type?. The choice of a given WfMS is a function of both its intrinsic language and engine features. Within bioinformatics, where analysts are a mix of dry and wet lab scientists, the choice is also governed by collaborations and adoption within large consortia and technical support provided by the WfMS team/community. As the community and its needs continue to evolve along with computational infrastructure, WfMSs will also evolve, especially those with permissive licenses that allow commercial use. In much the same way as the dataflow paradigm and containerization are now well understood to be very useful in bioinformatics applications, we will continue to see innovations of tools and utilities for other purposes, like big data technologies, interoperability, and provenance.Azza E. AhmedJoshua M. AllenTajesvi BhatPrakruthi BurraChristina E. FliegeSteven N. HartJacob R. HeldenbrandMatthew E. HudsonDave Deandre IstantoMichael T. KalmbachGregory D. KapraunKatherine I. KendigMatthew Charles KendziorEric W. KleeNate MattsonChristian A. RossSami M. SharifRamshankar VenkatakrishnanFaisal M. FadlelmolaLiudmila S. MainzerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Azza E. Ahmed Joshua M. Allen Tajesvi Bhat Prakruthi Burra Christina E. Fliege Steven N. Hart Jacob R. Heldenbrand Matthew E. Hudson Dave Deandre Istanto Michael T. Kalmbach Gregory D. Kapraun Katherine I. Kendig Matthew Charles Kendzior Eric W. Klee Nate Mattson Christian A. Ross Sami M. Sharif Ramshankar Venkatakrishnan Faisal M. Fadlelmola Liudmila S. Mainzer Design considerations for workflow management systems use in production genomics research and the clinic
description	Abstract The changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large volumes of data across heterogeneous computational environments. Workflow Management Systems (WfMSs) are the software components employed to fill this gap. This work provides an approach and systematic evaluation of key features of popular bioinformatics WfMSs in use today: Nextflow, CWL, and WDL and some of their executors, along with Swift/T, a workflow manager commonly used in high-scale physics applications. We employed two use cases: a variant-calling genomic pipeline and a scalability-testing framework, where both were run locally, on an HPC cluster, and in the cloud. This allowed for evaluation of those four WfMSs in terms of language expressiveness, modularity, scalability, robustness, reproducibility, interoperability, ease of development, along with adoption and usage in research labs and healthcare settings. This article is trying to answer, which WfMS should be chosen for a given bioinformatics application regardless of analysis type?. The choice of a given WfMS is a function of both its intrinsic language and engine features. Within bioinformatics, where analysts are a mix of dry and wet lab scientists, the choice is also governed by collaborations and adoption within large consortia and technical support provided by the WfMS team/community. As the community and its needs continue to evolve along with computational infrastructure, WfMSs will also evolve, especially those with permissive licenses that allow commercial use. In much the same way as the dataflow paradigm and containerization are now well understood to be very useful in bioinformatics applications, we will continue to see innovations of tools and utilities for other purposes, like big data technologies, interoperability, and provenance.
format	article
author	Azza E. Ahmed Joshua M. Allen Tajesvi Bhat Prakruthi Burra Christina E. Fliege Steven N. Hart Jacob R. Heldenbrand Matthew E. Hudson Dave Deandre Istanto Michael T. Kalmbach Gregory D. Kapraun Katherine I. Kendig Matthew Charles Kendzior Eric W. Klee Nate Mattson Christian A. Ross Sami M. Sharif Ramshankar Venkatakrishnan Faisal M. Fadlelmola Liudmila S. Mainzer
author_facet	Azza E. Ahmed Joshua M. Allen Tajesvi Bhat Prakruthi Burra Christina E. Fliege Steven N. Hart Jacob R. Heldenbrand Matthew E. Hudson Dave Deandre Istanto Michael T. Kalmbach Gregory D. Kapraun Katherine I. Kendig Matthew Charles Kendzior Eric W. Klee Nate Mattson Christian A. Ross Sami M. Sharif Ramshankar Venkatakrishnan Faisal M. Fadlelmola Liudmila S. Mainzer
author_sort	Azza E. Ahmed
title	Design considerations for workflow management systems use in production genomics research and the clinic
title_short	Design considerations for workflow management systems use in production genomics research and the clinic
title_full	Design considerations for workflow management systems use in production genomics research and the clinic
title_fullStr	Design considerations for workflow management systems use in production genomics research and the clinic
title_full_unstemmed	Design considerations for workflow management systems use in production genomics research and the clinic
title_sort	design considerations for workflow management systems use in production genomics research and the clinic
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/71063f0cca8645c6a434d845100e2c90
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Design considerations for workflow management systems use in production genomics research and the clinic

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