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An automated workflow for parallel processing of large multiview SPIM recordings
Summary: Selective Plane Illumination Microscopy (SPIM) allows to image developing organisms in 3D at unprecedented temporal resolution over long periods of time. The resulting massive amounts of raw image data requires extensive processing interactively via dedicated graphical user interface (GUI)...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4896369/ https://www.ncbi.nlm.nih.gov/pubmed/26628585 http://dx.doi.org/10.1093/bioinformatics/btv706 |
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author | Schmied, Christopher Steinbach, Peter Pietzsch, Tobias Preibisch, Stephan Tomancak, Pavel |
author_facet | Schmied, Christopher Steinbach, Peter Pietzsch, Tobias Preibisch, Stephan Tomancak, Pavel |
author_sort | Schmied, Christopher |
collection | PubMed |
description | Summary: Selective Plane Illumination Microscopy (SPIM) allows to image developing organisms in 3D at unprecedented temporal resolution over long periods of time. The resulting massive amounts of raw image data requires extensive processing interactively via dedicated graphical user interface (GUI) applications. The consecutive processing steps can be easily automated and the individual time points can be processed independently, which lends itself to trivial parallelization on a high performance computing (HPC) cluster. Here, we introduce an automated workflow for processing large multiview, multichannel, multiillumination time-lapse SPIM data on a single workstation or in parallel on a HPC cluster. The pipeline relies on snakemake to resolve dependencies among consecutive processing steps and can be easily adapted to any cluster environment for processing SPIM data in a fraction of the time required to collect it. Availability and implementation: The code is distributed free and open source under the MIT license http://opensource.org/licenses/MIT. The source code can be downloaded from github: https://github.com/mpicbg-scicomp/snakemake-workflows. Documentation can be found here: http://fiji.sc/Automated_workflow_for_parallel_Multiview_Reconstruction. Contact: schmied@mpi-cbg.de Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-4896369 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-48963692016-06-09 An automated workflow for parallel processing of large multiview SPIM recordings Schmied, Christopher Steinbach, Peter Pietzsch, Tobias Preibisch, Stephan Tomancak, Pavel Bioinformatics Applications Notes Summary: Selective Plane Illumination Microscopy (SPIM) allows to image developing organisms in 3D at unprecedented temporal resolution over long periods of time. The resulting massive amounts of raw image data requires extensive processing interactively via dedicated graphical user interface (GUI) applications. The consecutive processing steps can be easily automated and the individual time points can be processed independently, which lends itself to trivial parallelization on a high performance computing (HPC) cluster. Here, we introduce an automated workflow for processing large multiview, multichannel, multiillumination time-lapse SPIM data on a single workstation or in parallel on a HPC cluster. The pipeline relies on snakemake to resolve dependencies among consecutive processing steps and can be easily adapted to any cluster environment for processing SPIM data in a fraction of the time required to collect it. Availability and implementation: The code is distributed free and open source under the MIT license http://opensource.org/licenses/MIT. The source code can be downloaded from github: https://github.com/mpicbg-scicomp/snakemake-workflows. Documentation can be found here: http://fiji.sc/Automated_workflow_for_parallel_Multiview_Reconstruction. Contact: schmied@mpi-cbg.de Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2016-04-01 2015-12-01 /pmc/articles/PMC4896369/ /pubmed/26628585 http://dx.doi.org/10.1093/bioinformatics/btv706 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Applications Notes Schmied, Christopher Steinbach, Peter Pietzsch, Tobias Preibisch, Stephan Tomancak, Pavel An automated workflow for parallel processing of large multiview SPIM recordings |
title | An automated workflow for parallel processing of large multiview SPIM recordings |
title_full | An automated workflow for parallel processing of large multiview SPIM recordings |
title_fullStr | An automated workflow for parallel processing of large multiview SPIM recordings |
title_full_unstemmed | An automated workflow for parallel processing of large multiview SPIM recordings |
title_short | An automated workflow for parallel processing of large multiview SPIM recordings |
title_sort | automated workflow for parallel processing of large multiview spim recordings |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4896369/ https://www.ncbi.nlm.nih.gov/pubmed/26628585 http://dx.doi.org/10.1093/bioinformatics/btv706 |
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