EnrichNet: network-based gene set enrichment analysis

Abstract

Motivation: Assessing functional associations between an experimentally derived gene or protein set of interest and a database of known gene/protein sets is a common task in the analysis of large-scale functional genomics data. For this purpose, a frequently used approach is to apply an over-representation-based enrichment analysis. However, this approach has four drawbacks: (i) it can only score functional associations of overlapping gene/proteins sets; (ii) it disregards genes with missing annotations; (iii) it does not take into account the network structure of physical interactions between the gene/protein sets of interest and (iv) tissue-specific gene/protein set associations cannot be recognized.

Results: To address these limitations, we introduce an integrative analysis approach and web-application called EnrichNet. It combines a novel graph-based statistic with an interactive sub-network visualization to accomplish two complementary goals: improving the prioritization of putative functional gene/protein set associations by exploiting information from molecular interaction networks and tissue-specific gene expression data and enabling a direct biological interpretation of the results. By using the approach to analyse sets of genes with known involvement in human diseases, new pathway associations are identified, reflecting a dense sub-network of interactions between their corresponding proteins.

Availability: EnrichNet is freely available at http://www.enrichnet.org.

Contact: Natalio.Krasnogor@nottingham.ac.uk, reinhard.schneider@uni.lu or avalencia@cnio.es

Supplementary information: Supplementary data are available at Bioinformatics Online.

Fig. 1.

Regression plot: Xd-scores versus significance-of-overlap scores (Fisher's test, q-values), computed for the comparison of gastric cancer mutated genes against gene sets from the BioCarta database (absolute Pearson correlation: 0.93). Non-overlapping dataset pairs, for which a meaningful scoring is only possible with the XD-distance, are highlighted on the right. See also Table 1 for a list of the 20 top-ranked pathways in this plot

Fig. 2.

Protein–protein interaction sub-networks (largest connected components) for target and reference set pairs with small overlap, predicted to be functionally associated by EnrichNet: (a) gastric cancer mutated genes (blue) and genes/proteins from the BioCarta pathway ‘Role of Erk5 in Neuronal Survival’ (magenta, the shared genes are shown in green); (b) bladder cancer mutated genes (blue) and genes/proteins from Gene Ontology term ‘Tyrosine phosphorylation of Stat3’ (GO:0042503, magenta; the only shared gene NF2 is shown in green). An over-representation analysis approach would have missed these associations, since only few of the cancer mutated genes are members of the corresponding processes

Fig. 3.

Protein–protein interaction sub-network (largest connected component) for the PD gene set (blue) and genes/proteins from GO term ‘Regulation of interleukin-6 biosynthetic process’ (magenta, GO:0045408; the only shared gene IL1B is shown in green)