PyIOmica: longitudinal omics analysis and trend identification

Sergii Domanskyi; Carlo Piermarocchi; George I Mias

doi:10.1093/bioinformatics/btz896

PyIOmica: longitudinal omics analysis and trend identification

Bioinformatics. 2020 Apr 1;36(7):2306-2307. doi: 10.1093/bioinformatics/btz896.

Authors

Sergii Domanskyi¹, Carlo Piermarocchi¹, George I Mias^{1

2

3}

Affiliations

¹ Department of Physics and Astronomy, East Lansing, MI 48824, USA.
² Department of Biochemistry and Molecular Biology, East Lansing, MI 48824, USA.
³ Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.

Abstract

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.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Computational Biology*
Gene Ontology
Software*

Grants and funding

R01 GM122085/GM/NIGMS NIH HHS/United States