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D-NetWeaver Overview

D-NetWeaver is an application to enable the manipulation and analysis of time course data matrices, such as gene expression data as generated by microarray or RNA-seq experiments. It is specifically geared toward reconstructing gene regulatory networks from time course gene expression data using differential equation network models.

It provides the ability to apply six primary steps to gene expression data:

  1. Significant gene detection
  2. Clustering
  3. Smoothing
  4. Functional enrichment analysis
  5. Regulation identification (otherwise known as variable selection of differential equation network models)
  6. Parameter estimation refinement

The software provides many data manipulation and data visualization features to assist users in exploring their data and interpreting the results of these six primary steps.

The end goal is the creation of a dynamic network model. Currently, the only supported model is the linear ordinary differential equation (ODE) model (Lu et al. (2011); see "Papers" in the help menus for the manuscript).

D-NetWeaver runs on any major OS, such as Windows, Mac OS, and Linux. If you already have Java installed on your machine, running D-NetWeaver usually requires just double-clicking on the jar file. Otherwise, you can run D-NetWeaver via a command such as:

java -Xmx1g -jar dnetweaver.jar

D-NetWeaver requires Java 1.6 or higher and R 2.15 or higher to run correctly. You can download Java from Java.com and you can download R from http://www.r-project.org/index.html

Screenshots

Cluster Results

Cluster Annotation

Network Model

Acknowledgements

Software was created by University of Rochester Center for Biodefense Immune Modeling with funding from NIAID HHSN272201000055C

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