Numerous methods have been developed for reverse engineering gene regulatory networks from expression data. Unraveling and modeling these networks is of primary importance for improving our understanding of biological organisms. However, both their absolute and comparative performance remain poorly understood. The aim of this project is to provide benchmarks and tools for rigorous testing of methods for gene network inference.
Our framework is available as an open-source and user-friendly software called GeneNetWeaver (GNW). GNW is the first tool that provides methods for both in silico benchmark generation and performance profiling of network inference algorithms. GNW has been developed to easily generate detailed models of gene regulatory networks. One of the main advantages of using in silico is that perturbation experiments can be quickly and easily simulated to produce expression data unlike in vivo experiments, which are usually expensive and time consuming. Moreover, both quantity and quality of the expression data generated can be controled (e.g. by varying the amount of molecular and/or measurement noise). Finally expression data are used by inference methods to reconstruct (or reverse engineer) the underlying in silico networks, before quantitatively evaluating the performance of the methods by comparing target (unknown in in vivo experiments) and predicted networks.
Moreover, we have used GNW to organize three editions of the DREAM challenge, an annual community-wide network inference challenge. In this context, GNW was used to identify systematic errors of network inference algorithms, thus providing useful insights into how to improve their performance.
GNW has been developed during my PhD thesis. T Schaffter, From genes to organisms: Bioinformatics System Models and Software, 2014.