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Background  Handling the vast amount of gene expression data generated by genome-wide transcriptional profiling techniques is a challenging task, demanding an informed combination of pre-processing, filtering and analysis methods if meaningful biological conclusions are to be drawn. For example, a range of traditional statistical and computational pathway analysis approaches have been used to identify over-represented processes in microarray data derived from various disease states. However, most of these approaches tend not to exploit the full spectrum of gene expression data, or the various relationships and dependencies. Previously, we described a pathway enrichment analysis tool created in MATLAB that yields a Pathway Regulation Score (PRS) by considering signalling pathway topology, and the overrepresentation and magnitude of differentially-expressed genes ( J Comput Biol  19:563–573, 2012). Herein, we extended this approach to include metabolic pathways, and described the use of a graphical user interface (GUI).    Results  Using input from a variety of microarray platforms and species, users are able to calculate PRS scores, along with a corresponding z-score for comparison. Further pathway significance assessment may be performed to increase confidence in the pathways obtained, and users can view Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway diagrams marked-up to highlight impacted genes.    Conclusions  The PRS tool provides a filter in the isolation of biologically-relevant insights from complex transcriptomic data.    Electronic supplementary material  The online version of this article (doi:10.1186/s12859-014-0358-2) contains supplementary material, which is available to authorized users.

A MATLAB tool for pathway enrichment using a topology-based pathway regulation score