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PyIOmica: longitudinal omics analysis and trend identification

SUMMARY: PyIOmica is an open-source Python package focusing on integrating longitudinal multiple omics datasets, characterizing and categorizing temporal trends. The package includes multiple bioinformatics tools including data normalization, annotation, categorization, visualization and enrichment...

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Detalles Bibliográficos
Autores principales: Domanskyi, Sergii, Piermarocchi, Carlo, Mias, George I
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141865/
https://www.ncbi.nlm.nih.gov/pubmed/31778155
http://dx.doi.org/10.1093/bioinformatics/btz896
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author Domanskyi, Sergii
Piermarocchi, Carlo
Mias, George I
author_facet Domanskyi, Sergii
Piermarocchi, Carlo
Mias, George I
author_sort Domanskyi, Sergii
collection PubMed
description SUMMARY: PyIOmica is an open-source Python package focusing on integrating longitudinal multiple omics datasets, characterizing and categorizing temporal trends. The package includes multiple bioinformatics tools including data normalization, annotation, categorization, visualization and enrichment analysis for gene ontology terms and pathways. Additionally, the package includes an implementation of visibility graphs to visualize time series as networks. AVAILABILITY AND IMPLEMENTATION: PyIOmica is implemented as a Python package (pyiomica), available for download and installation through the Python Package Index (https://pypi.python.org/pypi/pyiomica), and can be deployed using the Python import function following installation. PyIOmica has been tested on Mac OS X, Unix/Linux and Microsoft Windows. The application is distributed under an MIT license. Source code for each release is also available for download on Zenodo (https://doi.org/10.5281/zenodo.3548040). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics
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spelling pubmed-71418652020-04-13 PyIOmica: longitudinal omics analysis and trend identification Domanskyi, Sergii Piermarocchi, Carlo Mias, George I Bioinformatics Applications Notes SUMMARY: PyIOmica is an open-source Python package focusing on integrating longitudinal multiple omics datasets, characterizing and categorizing temporal trends. The package includes multiple bioinformatics tools including data normalization, annotation, categorization, visualization and enrichment analysis for gene ontology terms and pathways. Additionally, the package includes an implementation of visibility graphs to visualize time series as networks. AVAILABILITY AND IMPLEMENTATION: PyIOmica is implemented as a Python package (pyiomica), available for download and installation through the Python Package Index (https://pypi.python.org/pypi/pyiomica), and can be deployed using the Python import function following installation. PyIOmica has been tested on Mac OS X, Unix/Linux and Microsoft Windows. The application is distributed under an MIT license. Source code for each release is also available for download on Zenodo (https://doi.org/10.5281/zenodo.3548040). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Oxford University Press 2020-04-01 2019-11-28 /pmc/articles/PMC7141865/ /pubmed/31778155 http://dx.doi.org/10.1093/bioinformatics/btz896 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://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 Notes
Domanskyi, Sergii
Piermarocchi, Carlo
Mias, George I
PyIOmica: longitudinal omics analysis and trend identification
title PyIOmica: longitudinal omics analysis and trend identification
title_full PyIOmica: longitudinal omics analysis and trend identification
title_fullStr PyIOmica: longitudinal omics analysis and trend identification
title_full_unstemmed PyIOmica: longitudinal omics analysis and trend identification
title_short PyIOmica: longitudinal omics analysis and trend identification
title_sort pyiomica: longitudinal omics analysis and trend identification
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141865/
https://www.ncbi.nlm.nih.gov/pubmed/31778155
http://dx.doi.org/10.1093/bioinformatics/btz896
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