Overview
========

An open source Python package, for analyzing longitudinal omics  datasets, which includes multiple 
tools for processing of multi-modal mapped data, characterizing time series in terms of periodograms 
and autocorrelations, classifying temporal behavior, visualizing visibility graphs, and testing data 
for gene ontology and pathway enrichment. PyIOmica includes optimized new algorithms adapted 
from MathIOmica (which runs on the proprietary Mathematica platform), now made available as Python 
open source code for all users, and additionally expands extensively graphical utilities for 
visualization of classified temporal data, and network representation of time series. 


The publication describing this software:
 | Sergii Domanskyi, Carlo Piermarocchi, George I Mias, **PyIOmica: longitudinal omics analysis and trend identification**, *Bioinformatics*, Volume 36, Issue 7, 2020, Pages 2306–2307, https://doi.org/10.1093/bioinformatics/btz896


Versions
--------

.. include:: ../../ChangeLog.md



Documentation
-------------

Documentation for PyIOmica is built-in and is available through the help() functionality in Python or online at https://pyiomica.readthedocs.io. 



Additional information
----------------------

-  PyIOmica is a multi-omics analysis framework distributed as a Python
   package that aims to assist in bioinformatics.
-  The most current version of the package is maintained at
   https://github.com/gmiaslab/pyiomica
-  News are distributed via twitter (@mathiomica)



Licensing
---------

PyIOmica is released under an MIT License. Please also consult the
folder `LICENSES 
<https://github.com/gmiaslab/pyiomica/tree/master/pyiomica_LICENSES>`_
distributed with PyIOmica regarding Licensing
information for use of external associated content.



Contact information
-------------------------
- Contributors: Sergii Domanskyi, Minzhang Zheng, Carlo Piermarocchi, George I. Mias.
- G.MiasLab (https://georgemias.org)
- e-mail: gmiaslab@gmail.com
- twitter: @gmiaslab

   
Funding
-------

PyIOmica development and associated research were supported by the Translational Research Institute 
for Space Health through NASA Cooperative Agreement NNX16AO69A (Project Number T0412, PI: Mias). 
The content is solely the responsibility of the authors and does not necessarily 
represent the official views of the supporting funding agencies.