Model-free combinatorial optimization algorithm to infer time-delayed gene regulatory networks from genome-wide time series datasets.
# GRNCOP2: Gene Regulatory Network inference by Combinatorial OPtimization 2 GRNCOP2 is a Cytoscape app that uses a model-free combinatorial optimization algorithm to infer time-delayed gene regulatory networks from genome-wide time series datasets. The discovered relationships, that represent potential interactions between genes, may be used to predict the gene expression states of a gene in terms of the gene expression values of other genes and, in this way, a putative GRN may then be reconstructed by applying and combining these rules. The approach offers several relevant and distinguishing features in relation to most of the existing methods. * The gene expression value discretization criterion performed is neither arbitrary nor uniform. * It can infer rules with multiple time-delays. * The results can be easily interpreted since the rules are derived from schemes that classify the different regulation states. * The algorithm can infer the relationships between genes automatically from multiple microarray time series data. * GRNCOP2 is capable of processing large scale datasets in order to perform genome-wide studies. ## Using GRNCOP2 First you need to install the app throug the app manager. If everything goes well, you will get a new item in the `Apps` menu called `GRNCOP2`. Open GRNCOP2 from the `Apps` menu and you'll be presented the configuration dialog. Select the maximum possible time window. This define the maximum time-lag that the generated rules might have. Select your genes files which should contain one gene per line. ``` Gene1 Gene2 ... GeneN ``` Once the file is parsed, you can see how many genes where identified in the file at the right of the upload button. Then, you can select your time series datasets. These files are expected to be in CSV format and you can selec the separator at the top of the coniguration dialog. Each time series dataset file should contain as many rows as genes are in the genes file and each row should contain the same number of elements. You can add as many time series files as you want. After running GRNCOP2 you'll get a network that contains all the information generated by the algorithm. The results panel (right-hand side) show some controls that you can use to explore you results. You can visualize the rules with a specific time-lag or all the rules at once. You can also modify different parameters to increase the flexibility of GRNCOP2. These parameters are described in detail at <a href="">Gallo et al.</a>. You can also download the rules yielded by GRNCOP2 with the current configuration. The network has a basic style applied to facilitate visual exploration. However, all the data is in the Cytoscape tables so you can create your own styles for visualization. The default style adapts itself to GRNCOP2 configuration and filters and includes: * The edges are directed so it's easy to know who regulates who * The edges are darker for higher accuracy values * The edges are thicker for higher coverage (i.e. more datasets predicting the same regulation) ## Reference [Gallo, Cristian A., Jessica A. Carballido, and Ignacio Ponzoni. "Discovering time-lagged rules from microarray data using gene profile classifiers." BMC bioinformatics 12.1 (2011): 123.]( ## COPYRIGHT * <a href="">Juan José Díaz Montaña</a> (<a href=""></a>) Copyright © 2017 Universidad Pablo de Olavide, Spain.


Works with Cytoscape 3.3

Release Notes

Initial release


Version 1.0.0

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Released 14 Sep 2018

Works with Cytoscape 3.3

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