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Co-expression. Two genes are linked if their expression levels are similar across conditions in a gene expression study.
Physical Interaction. Two gene products are linked if they were found to interact in a protein-protein interaction study. These data are collected from primary studies found in protein interaction databases, including BioGRID and PathwayCommons.
Genetic interaction. Two genes are functionally associated if the effects of perturbing one gene were found to be modified by perturbations to a second gene. These data are collected from primary studies and BioGRID.
Shared protein domains. Two gene products are linked if they have the same protein domain. These data are collected from domain databases, such as InterPro, SMART and Pfam.
Co-localization. Two genes are linked if they are both expressed in the same tissue or if their gene products are both identified in the same cellular location.
Pathway. Two gene products are linked if they participate in the same reaction within a pathway. These data are collected from various source databases, such as Reactome and BioCyc, via PathwayCommons.
Predicted: Predicted functional relationships between genes, often protein interactions. A major source of predicted data is mapping known functional relationships from another organism via orthology.
Other: Networks that do not fit into any of the above categories. Examples include phenotype correlations from Ensembl, disease information from OMIM and chemical genomics data.
GeneMANIA extends the user’s inputs with genes that are functionally similar, or have shared properties with the initial query genes.
Users interested in prioritizing genes for planning a functional screen can use GeneMANIA to return ranked lists of genes likely to share phenotypes with those in the query list based on GeneMANIA’s large and diverse data collection.
GeneMANIA assigns weights to datasets based on how useful they are for each query.
In the basic algorithm, each network is assigned a weight primarily based on how well connected genes in the query list are to each other compared with their connectivity to non-query genes. However, GeneMANIA’sadaptive weighting methods also detect and down-weight redundant networks.
This feature is particularly useful for determining how genes in a gene list are connected to one another, or for determining which types of functional genomic data are most useful to collect for finding more genes like those in the query list.
For longer gene lists, it uses the basic weighting method called GeneMANIAEntry-1 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.
For short gene lists, GeneMANIA uses a similar principle to weight networks, but tries to reproduce Gene Ontology biological process co-annotation patterns rather than the gene list.
The user may choose other weighting methods in the advanced options panel.