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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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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 |
format | Online Article Text |
id | pubmed-7141865 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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