Enrichment analysis
Enrichment analysis is a widely applied procedure for:
1) shedding light on the molecular mechanisms and functions at the basis of phenotypes;
2) enlarging the dataset of possibly related genes/proteins;
3) helping interpretation and prioritization of newly determined variations.
Enrichment analysis approaches can be classified mainly in two broad classes:
1) standard enrichment;
2) network-based enrichment.
Both approaches rely on the annotations that characterize the genes/proteins included in the input set.
Network based ones also include in different ways physical and functional relationships among different genes or proteins that can be extracted from the available biological networks of interactions.
NET-GE
NET-GE implements both a standard and a network-based gene enrichment analysis. The protein set to be analyzed is mapped on each annotation term, determining, through a Fisher's exact test, whether there are significant overlaps between the input proteins and the annotation terms.
In the standard enrichment procedure, each annotation database contains only proteins directly associated to the term (seed set). The network-based enrichment relies on a processing phase aimed at extracting modules starting from seed sets of proteins sharing the same annotation term.
Module generation By construction, a module is a compact and connected subgraph of the protein-ptotein interaction network. Given an annotation term the corresponding module contains all the proteins directly annotated with the same term in UniProtKB (seed nodes) and some of their interacting partners (connecting nodes).
The module is determined by computing all the shortest paths among the seeds and by reducing the resulting network into the minimal connecting network preserving the distances among seeds (Di Lena et al.,2015). The module extraction is schematized in Figure 1.
The minimal connecting network adds to the seeds a set of connecting nodes that are more reliably related to the reference annotation term.
References
Di Lena P, Martelli PL, Fariselli P, Casadio R. (2015) NET-GE: a novel NETwork-based Gene Enrichment for detecting biological processes associated to Mendelian diseases. BMC Genomics. 2015;16 Suppl 8:S6. PUBMED