The ability to experimentally determine molecular interactions on an almost proteome-wide scale under different conditions is enabling researchers to move from static to dynamic network analysis, uncovering new insights into how interaction networks are physically rewired in response to different stimuli and in disease. Dynamic interaction data presents a special challenge in network biology. Here, we present DyNet, a Cytoscape application that provides a range of functionalities for the visualization, real-time synchronization, and analysis of large multi-state dynamic molecular interaction networks enabling users to quickly identify and analyze the most ‘rewired’ nodes across many network states.
**Published in**
[http://bioinformatics.oxfordjournals.org/cgi/pmidlookup?view=short&pmid=27153624 *Bioinformatics*]
**Please Cite**: Goenawan, Ivan H., Kenneth Bryan, and David J. Lynn. "DyNet: visualization and analysis of dynamic molecular interaction networks." Bioinformatics (2016): btw187.
**BibTex**:<br>
@article{goenawan2016dynet,<br>
title={DyNet: visualization and analysis of dynamic molecular interaction networks},<br>
author={Goenawan, Ivan H and Bryan, Kenneth and Lynn, David J},<br>
journal={Bioinformatics},<br>
pages={btw187},<br>
year={2016},<br>
publisher={Oxford Univ Press}<br>
}
*See* [https://scholar.google.com/scholar?hl=en&q=DyNet%3A+visualization+and+analysis+of+dynamic+molecular+interaction+networks&btnG=&as_sdt=1%2C5&as_sdtp= *Google Scholar*] *for further citation formats.*
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List of features:
* Import multiple network files at once.
* Live synchronisation of network layouts.
* Highlight differences between two networks (based on node/edge presence or specific attribute).
* Highlight most varying nodes/edges across multiple networks (based on node/edge presence or specific attribute).
* Highlight most varying nodes in terms of their edge connections (most rewired nodes) across multiple networks.
* Complex filtering criteria (e.g. show edges present in network A and B, but not C or D)
* Create a networks vs edges heatmap, with hierarchical clustering on both axes (cluster edges that evolve similarly across multiple networks and cluster networks that contain similar connections).
