• Developing and reusing bioinfo...

Treffer: Developing and reusing bioinformatics data analysis pipelines using scientific workflow systems.

Title:
Developing and reusing bioinformatics data analysis pipelines using scientific workflow systems.
Authors:
Djaffardjy M; Universite Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, Orsay 91405, France., Marchment G; Universite Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, Orsay 91405, France., Sebe C; Universite Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, Orsay 91405, France., Blanchet R; Nantes Université, CNRS, INSERM, l'institut du thorax, 8 quai Moncousu, Nantes F-44000, France., Bellajhame K; PSL, Universite Paris-Dauphine, LAMSADE, Place du Maréchal de Lattre de Tassigny, Paris 75775, France., Gaignard A; Nantes Université, CNRS, INSERM, l'institut du thorax, 8 quai Moncousu, Nantes F-44000, France., Lemoine F; Institut Pasteur, Université Paris Cité, G5 Evolutionary Genomics of RNA Viruses, 28, rue du Dr Roux, Paris 75015, France.; Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, France, 28, rue du Dr Roux, Paris 75015, France., Cohen-Boulakia S; Universite Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, Orsay 91405, France.
Source:
Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2023 Mar 07; Vol. 21, pp. 2075-2085. Date of Electronic Publication: 2023 Mar 07 (Print Publication: 2023).
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology Country of Publication: Netherlands NLM ID: 101585369 Publication Model: eCollection Cited Medium: Print ISSN: 2001-0370 (Print) Linking ISSN: 20010370 NLM ISO Abbreviation: Comput Struct Biotechnol J Subsets: PubMed not MEDLINE
Imprint Name(s):
Publication: Amsterdam : Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology
Original Publication: Gothenburg, Sweden : Research Network of Computational and Structural Biotechnology
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Contributed Indexing:
Keywords: Bioinformatics; Reproducibility; Reuse; Scientific workflows
Entry Date(s):
Date Created: 20230327 Latest Revision: 20230328
Update Code:
20250114
PubMed Central ID:
PMC10030817
DOI:
10.1016/j.csbj.2023.03.003
PMID:
36968012
Database:
MEDLINE

Weitere Informationen

Data analysis pipelines are now established as an effective means for specifying and executing bioinformatics data analysis and experiments. While scripting languages, particularly Python, R and notebooks, are popular and sufficient for developing small-scale pipelines that are often intended for a single user, it is now widely recognized that they are by no means enough to support the development of large-scale, shareable, maintainable and reusable pipelines capable of handling large volumes of data and running on high performance computing clusters. This review outlines the key requirements for building large-scale data pipelines and provides a mapping of existing solutions that fulfill them. We then highlight the benefits of using scientific workflow systems to get modular, reproducible and reusable bioinformatics data analysis pipelines. We finally discuss current workflow reuse practices based on an empirical study we performed on a large collection of workflows.
(© 2023 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.