Shedding light on yet uncharacterised components of photorespiration, such as transport processes required for the function of this pathway, is a prerequisite for manipulating photorespiratory fluxes and hence for decreasing photorespiratory energy loss. The ability of forward genetic screens to identify missing links is apparently limited, as indicated by the fact that little progress has been made with this approach during the past decade. The availability of large amounts of gene expression data and the growing power of bioinformatics, paired with availability of computational resources, opens new avenues to discover proteins involved in transport of photorespiratory intermediates. Co-expression analysis is a tool that compares gene expression data under hundreds of different conditions, trying to find groups of genes that show similar expression patterns across many different conditions. Genes encoding proteins that are involved in the same process are expected to be simultaneously expressed in time and space. Thus, co-expression data can aid in the discovery of novel players in a pathway, such as the transport proteins required for facilitating the transfer of intermediates between compartments during photorespiration. We here review the principles of co-expression analysis and show how this tool can be used for identification of candidate genes encoding photorespiratory transporters.