Abstract
BackgroundReverse engineering approaches to infer gene regulatory networks using computational methods are of great importance to annotate gene functionality and identify hub genes. Although various statistical algorithms have been proposed, development of computational tools to integrate results from different methods and user-friendly online tools is still lagging.ResultsWe developed a web server that efficiently constructs gene networks from expression data. It allows the user to use ten different network construction methods (such as partial correlation-, likelihood-, Bayesian- and mutual information-based methods) and integrates the resulting networks from multiple methods. Hub gene information, if available, can be incorporated to enhance performance.ConclusionsGeNeCK is an efficient and easy-to-use web application for gene regulatory network construction. It can be accessed at http://lce.biohpc.swmed.edu/geneck.
Highlights
Reverse engineering approaches to infer gene regulatory networks using computational methods are of great importance to annotate gene functionality and identify hub genes
To provide easy accessibility for the network construction tool, we introduce a web server called Gene network construction tool kit (GeNeCK) (Gene Network Construction Tool Kit, see Fig. 1) which allows users to upload their own gene expression data and choose their preferred method to infer and visualize the network, as well as integrate different methods to obtain a more confident result
We investigated the performance of each method for data with various noise levels and sample sizes
Summary
Reverse engineering approaches to infer gene regulatory networks using computational methods are of great importance to annotate gene functionality and identify hub genes. A gene regulatory network (GRN) describes biological interactions among genes and provides a systematic understanding of cellular signaling and regulatory processes. It depicts how a set of genes interact with each other to form a functional module and how different gene modules are related. A typical GRN approximates a scale-free network topology with a few highly connected genes (i.e. hub genes) and many poorly connected nodes [1]. Statistical methods proposed for Despite the development of various computational methods and corresponding R packages for inferring
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