NET-GE: a web-server for linking protein variations to biological processes and pathways

Introduction High-Throughput Sequencing technologies allow a fast discovery of genetic variations characterizing specific human phenotypes (such us diseases) and in the perspective of personalized medicine each individual phenotype needs annotations for reconciling such variations with common biological processes and pathways. To this purpose, we developed NET-GE [1] a NETwork-based Gene Enrichment tool for associating biological processes and pathways to sets of human proteins involved in the same phenotype. NET-GE is available at http://net-ge.biocomp.unibo.it/enrich Methods NET-GE implements a standard and a network based enrichment. The network based enrichment relies on the human protein interactome available in the STRING database (http://string-db.org/). For each set of annotations (Gene Ontology, KEGG and Reactome pathways), proteins sharing the same annotation term are collected in a seed set and than mapped in STRING. Each originated subgraph is followed by a procedure aimed to reduce the resulting network and the protein set to be analyzed is mapped on the sub-networks and tested for enrichment by applying a Fisher's exact test. NET-GE web server runs on a web2py engine and it is publicly accessible. Results Given a set of human proteins (Uniprot accession, http://www.uniprot.org/), NET-GE perform a standard and network-based enrichment. Options are possible for Gene Ontology terms (http://geneontology.org/), KEGG (http://www.genome.jp/kegg/) or Reactome (http://www.reactome.org/). When tested on an OMIM-derived (http://www.ncbi.nlm.nih.gov/omim) benchmark, our method is able to detect functional associations not detectable by standard enrichment. Conclusions NET-GE is useful for highlighting new hypotheses on the molecular mechanisms underlying a given human phenotype. Furthermore, with our procedure, it is possible to explore new genes/proteins in the subgraph-enriched-network for helping the prioritization of genetic variant discovery. References [1] Di Lena, P., Martelli, P.L., Fariselli, P., Casadio, R. (2015) NET-GE: a novel NETwork-based Gene Enrichment for detecting biological processes associated to Mendelian diseases. BMC Genomics 16 Suppl 8, S6.