GeneMANIA Network Weighting Methods

By default, the GeneMANIA prediction server uses one of two different adaptive network weighting methods. For longer gene lists, GeneMANIA uses the basic weighting method [called GeneMANIA^Entry-1 in (10 (link)) and called ‘assigned based on query genes’ on the web site] and weights each network so that after the networks are combined, the query genes interact as much as possible with each other while interacting as little as possible with genes not in the list. GeneMANIA learns from longer gene lists, allowing a gene list-specific network weighting to be calculated. Shorter gene lists do not contain enough information for GeneMANIA to learn which networks mediate the underlying functional relationship among the genes. For short gene lists, GeneMANIA uses a similar principle to weight networks, but tries to reproduce Gene Ontology (GO) biological process co-annotation patterns rather than the gene list. This method is described in detail in (11 ). The user may choose other adaptive and non-adaptive weighting methods in the advanced options panel, found directly under the gene query text box. The two non-adaptive methods are the most conservative options and work well on small gene lists (10 (link)). These methods allow users to choose either to weight every individual network equally, or weight each class (e.g. co-expression and protein interaction) of network equally. Network weights can also be assigned based on how well they reproduce GO co-annotation patterns for that organism in the molecular function, biological process or cellular component hierarchies. Note that the annotation-based weighting may slightly inflate weights for networks on which current annotations are based or for networks that were derived based on co-annotation patterns of genes. The networks most affected by this inflation are the older, smaller scale protein and genetic interaction studies and networks classified as ‘predicted’. However, this inflation does not seem to have a large impact on weights and may be largely avoided by only using networks derived from high-throughput assays with the annotation-based schemes.