Tim Hughes and colleagues from the University of Toronto, Canada, looked at the mouse genome using a technique previously only applied to simple organisms such as yeast and the nematode worm C. elegans. In yeast and other simple organisms, the expression of genes with similar functions tends to be coordinately regulated. In these organisms, identifying correlated expression of known and unknown genes can help predicting the function of a novel gene. It has been assumed that this strategy couldn't be applied to mammals, but instead that genes expressed in the same tissue are most likely to have a functional relationship, making tissue-specificity the best indicator of function.
In an experiment that challenges this view, Hughes and colleagues created and analysed a microarray panel of over 40,000 known mouse mRNAs, expressed in 55 tissues. Their results showed that genes from the same Gene Ontology 'Biological Process' (GO-BP) category which indicates the physiological function of their encoded protein, such as 'response to temperature' or 'amino acid metabolism' - are transcriptionally co-regulated, independent of the tissue in which they are expressed.
To show that this approach could be used to predict novel gene function, the team then carried out a co-expression analysis on genes of unknown function. They analysed the microarray results using a machine learning computational algorithm called a support vector machine (SVM). SVMs had never been used on this scal
Contact: Juliette Savin