EnrichNet: network-based gene set enrichment analysis
By E. Glaab, A. Baudot, Natalio Krasnogor, Reinhard Schneider, Alfonso Valencia. 2012
297 citations recorded by the source corpus.
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.
Subfields: Genetics, Molecular Biology
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In Molecular Biology
Citations in the corpus
- Pathway and Network Analysis of Cancer Genomes (2015, unknown): “Examples of recommended network construction algorithms include GeneMANIA 39 , ReactomeFIViz 40 , STRING 38 , ResponseNet 41 , NetBox 42 , MEMo 43 , and EnrichNet 44 ( Supplementary Table 2 ).”
- pathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks (2019, background): “In EnrichNet (Glaab et al., 2012), input genes and pathway genes are mapped on a PIN.”
- Proteomics: Technologies and Their Applications. (2017, methodology): “Furthermore, protein networks can be drawn based on the list of genes provided and the available interactions using STRING database (138, 151, 152) (Table 1).”