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Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows

Reproducing computational workflows in image analysis and microscopy can be a daunting task due to different software versions and dependencies. This is especially true for users with little specific knowledge of scientific computation. To overcome these challenges, we introduce Singularity containe...

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Detalles Bibliográficos
Autores principales: Mitra-Behura, Shilpita, Fiolka, Reto Paul, Daetwyler, Stephan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581025/
https://www.ncbi.nlm.nih.gov/pubmed/36303730
http://dx.doi.org/10.3389/fbinf.2021.757291
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author Mitra-Behura, Shilpita
Fiolka, Reto Paul
Daetwyler, Stephan
author_facet Mitra-Behura, Shilpita
Fiolka, Reto Paul
Daetwyler, Stephan
author_sort Mitra-Behura, Shilpita
collection PubMed
description Reproducing computational workflows in image analysis and microscopy can be a daunting task due to different software versions and dependencies. This is especially true for users with little specific knowledge of scientific computation. To overcome these challenges, we introduce Singularity containers as a useful tool to run and share image analysis workflows among many users, even years later after establishing them. Unfortunately, containers are rarely used so far in the image analysis field. To address this lack of use, we provide a detailed step-by-step protocol to package a state-of-the-art segmentation algorithm into a container on a local Windows machine to run the container on a high-performance cluster computer.
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spelling pubmed-95810252022-10-26 Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows Mitra-Behura, Shilpita Fiolka, Reto Paul Daetwyler, Stephan Front Bioinform Bioinformatics Reproducing computational workflows in image analysis and microscopy can be a daunting task due to different software versions and dependencies. This is especially true for users with little specific knowledge of scientific computation. To overcome these challenges, we introduce Singularity containers as a useful tool to run and share image analysis workflows among many users, even years later after establishing them. Unfortunately, containers are rarely used so far in the image analysis field. To address this lack of use, we provide a detailed step-by-step protocol to package a state-of-the-art segmentation algorithm into a container on a local Windows machine to run the container on a high-performance cluster computer. Frontiers Media S.A. 2022-01-27 /pmc/articles/PMC9581025/ /pubmed/36303730 http://dx.doi.org/10.3389/fbinf.2021.757291 Text en Copyright © 2022 Mitra-Behura, Fiolka and Daetwyler. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioinformatics
Mitra-Behura, Shilpita
Fiolka, Reto Paul
Daetwyler, Stephan
Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows
title Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows
title_full Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows
title_fullStr Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows
title_full_unstemmed Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows
title_short Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows
title_sort singularity containers improve reproducibility and ease of use in computational image analysis workflows
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581025/
https://www.ncbi.nlm.nih.gov/pubmed/36303730
http://dx.doi.org/10.3389/fbinf.2021.757291
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