The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function

2010 Nucleic Acids Research 4,856 citations

Abstract

GeneMANIA (http://www.genemania.org) is a flexible, user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. Six organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens and Saccharomyces cerevisiae) and hundreds of data sets have been collected from GEO, BioGRID, Pathway Commons and I2D, as well as organism-specific functional genomics data sets. Users can select arbitrary subsets of the data sets associated with an organism to perform their analyses and can upload their own data sets to analyze. The GeneMANIA algorithm performs as well or better than other gene function prediction methods on yeast and mouse benchmarks. The high accuracy of the GeneMANIA prediction algorithm, an intuitive user interface and large database make GeneMANIA a useful tool for any biologist.

Keywords

BiologyFunction (biology)Model organismOrganismComputer scienceSet (abstract data type)ExploitCaenorhabditis elegansComputational biologyGeneGenetics

MeSH Terms

AlgorithmsAnimalsGene Regulatory NetworksGenesGenomicsHumansInternetMiceSoftware

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Publication Info

Year
2010
Type
article
Volume
38
Issue
suppl_2
Pages
W214-W220
Citations
4856
Access
Closed

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267
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Cite This

David Warde-Farley, Sylva L. Donaldson, Ovi Comes et al. (2010). The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Research , 38 (suppl_2) , W214-W220. https://doi.org/10.1093/nar/gkq537

Identifiers

DOI
10.1093/nar/gkq537
PMID
20576703
PMCID
PMC2896186

Data Quality

Data completeness: 86%