Human diseases have led to rapidly expanding research to understand their cause and find solutions. This has led to the association of many genes to these diseases. However the underlying mechanisms often remain unknown or are unclear. There currently is a bias toward well studied genes and a tendency to ignore poorly annotated genes, which may be the missing pieces of the puzzle. By using the same pieces over and over again, we may be never able to see the complete picture of the disease under study. To identify new pieces and complete the puzzle we have created this online tool. This tool associates unstudied genes with well studied and disease related genes, by identifying which genes tend to work together. This can then be used to predict the function of unstudied genes adding new pieces to the puzzl

GeneFriends is a co-expression map that describes which genes tend to generally activate (increase in expression) and deactivate (decrease in expression) simultaneously in a large range of microarray datasets between the different conditions described within these micro-arrays (obtained from the GEO database).

This leads to a general impression of which genes tend to activate simultaneously irrespective of the condition (the microarray datasets describe a wide range of different conditions).

Since co-expressed genes tend to be involved in the same biological processes this map can be used to:

  1. Assign putative functions to poorly annotated genes.
  2. Identify new target genes related to a disease or biological process using a guilt by association approach.

GeneFriends Results


The GeneFriends tool employs a genome wide co-expression map which describes which genes are related based on how often they are co-expressed. To construct this map we used normalized microarray data from the GEO database. Entrez ID's present in at least 5% of the datasets were included in the co-expression map. These were paired to establish if genes were co-regulated; co-regulation being defined as both genes increasing or decreasing in expression at least two-fold simultaneously, a standard (even if arbitrary) measure of differential expression. Then based on how often gene pairs were co-regulated compared to how often the single genes showed a two-fold increase or decrease in expression we calculated a co-expression ratio, which quantifies how strongly two genes are co-expressed, for all gene pairs.

Number of datasets included in different co-expression maps: