The amount of data produced by molecular biologists is growing at an exponential rate. Some of the fastest growing sets of data are measurements of gene expression, comparable in quantity only to gene sequences and the vast biological literature. Both gene expression data and sequence data offer hints as to the functions of thousands of newly discovered genes, but neither give complete answers. Therefore, much effort is being focused on integrating these large data sets and combining them with all available functional data to draw inferences about the functions of uncharacterised genes. This review discusses the most pertinent functional data for genome-wide functional inference and describes several methods by which these disparate data types are being integrated.