Massive gene expression analyses are widely used to find differentially expressed genes under specific conditions. The results of these experiments are often available in public databases that are undergoing a growth similar to that of molecular sequence databases in the past. This now allows novel secondary computational tools to emerge that use such information to gain new knowledge. If several genes have a similar expression profile across heterogeneous transcriptomics experiments, they could be functionally related. These associations are usually useful for the annotation of uncharacterized genes. In addition, the search for genes with opposite expression profiles is useful for finding negative regulators and proposing inhibitory compounds in drug repurposing projects. Here we present a new web application, Automatic and Serial Analysis of CO-expression (ASACO), which has the potential to discover positive and negative correlator genes to a given query gene, based on thousands of public transcriptomics experiments. In addition, examples of use are presented, comparing with previous contrasted knowledge. The results obtained propose ASACO as a useful tool to improve knowledge about genes associated with human diseases and noncoding genes. ASACO is available at http://www.bioinfocabd.upo.es/asaco/.
Keywords: differential gene expression; drug repurposing; expression profile; gene annotation; noncoding genes.
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