Phenonaut: multiomics data integration for phenotypic space exploration

SUMMARY: Data integration workflows for multiomics data take many forms across academia and industry. Efforts with limited resources often encountered in academia can easily fall short of data integration best practices for processing and combining high-content imaging, proteomics, metabolomics, and...

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Autores principales: Shave, Steven, Dawson, John C, Athar, Abdullah M, Nguyen, Cuong Q, Kasprowicz, Richard, Carragher, Neil O
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Applications Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10068743/
https://www.ncbi.nlm.nih.gov/pubmed/36944259
http://dx.doi.org/10.1093/bioinformatics/btad143
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author Shave, Steven
Dawson, John C
Athar, Abdullah M
Nguyen, Cuong Q
Kasprowicz, Richard
Carragher, Neil O
author_facet Shave, Steven
Dawson, John C
Athar, Abdullah M
Nguyen, Cuong Q
Kasprowicz, Richard
Carragher, Neil O
author_sort Shave, Steven
collection PubMed
description SUMMARY: Data integration workflows for multiomics data take many forms across academia and industry. Efforts with limited resources often encountered in academia can easily fall short of data integration best practices for processing and combining high-content imaging, proteomics, metabolomics, and other omics data. We present Phenonaut, a Python software package designed to address the data workflow needs of migration, control, integration, and auditability in the application of literature and proprietary techniques for data source and structure agnostic workflow creation. AVAILABILITY AND IMPLEMENTATION: Source code: https://github.com/CarragherLab/phenonaut, Documentation: https://carragherlab.github.io/phenonaut, PyPI package: https://pypi.org/project/phenonaut/.
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spelling pubmed-100687432023-04-04 Phenonaut: multiomics data integration for phenotypic space exploration Shave, Steven Dawson, John C Athar, Abdullah M Nguyen, Cuong Q Kasprowicz, Richard Carragher, Neil O Bioinformatics Applications Note SUMMARY: Data integration workflows for multiomics data take many forms across academia and industry. Efforts with limited resources often encountered in academia can easily fall short of data integration best practices for processing and combining high-content imaging, proteomics, metabolomics, and other omics data. We present Phenonaut, a Python software package designed to address the data workflow needs of migration, control, integration, and auditability in the application of literature and proprietary techniques for data source and structure agnostic workflow creation. AVAILABILITY AND IMPLEMENTATION: Source code: https://github.com/CarragherLab/phenonaut, Documentation: https://carragherlab.github.io/phenonaut, PyPI package: https://pypi.org/project/phenonaut/. Oxford University Press 2023-03-21 /pmc/articles/PMC10068743/ /pubmed/36944259 http://dx.doi.org/10.1093/bioinformatics/btad143 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Shave, Steven
Dawson, John C
Athar, Abdullah M
Nguyen, Cuong Q
Kasprowicz, Richard
Carragher, Neil O
Phenonaut: multiomics data integration for phenotypic space exploration
title Phenonaut: multiomics data integration for phenotypic space exploration
title_full Phenonaut: multiomics data integration for phenotypic space exploration
title_fullStr Phenonaut: multiomics data integration for phenotypic space exploration
title_full_unstemmed Phenonaut: multiomics data integration for phenotypic space exploration
title_short Phenonaut: multiomics data integration for phenotypic space exploration
title_sort phenonaut: multiomics data integration for phenotypic space exploration
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10068743/
https://www.ncbi.nlm.nih.gov/pubmed/36944259
http://dx.doi.org/10.1093/bioinformatics/btad143
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