The emergence of large gene expression datasets has revealed the need for improved tools to identify enriched gene categories and visualize enrichment patterns. While gene ontogeny (GO) provides a valuable tool for gene set enrichment analysis, it has several limitations. First, it is difficult to graph multiple GO analyses for comparison. Second, genes from some model systems are not well represented. For example, ∼30% of Caenorhabditis elegans genes are missing from the analysis in commonly used databases. To allow categorization and visualization of enriched C. elegans gene sets in different types of genome-scale data, we developed WormCat, a web-based tool that uses a near-complete annotation of the C. elegans genome to identify coexpressed gene sets and scaled heat map for enrichment visualization. We tested the performance of WormCat using a variety of published transcriptomic datasets, and show that it reproduces major categories identified by GO. Importantly, we also found previously unidentified categories that are informative for interpreting phenotypes or predicting biological function. For example, we analyzed published RNA-seq data from C. elegans treated with combinations of lifespan-extending drugs, where one combination paradoxically shortened lifespan. Using WormCat, we identified sterol metabolism as a category that was not enriched in the single or double combinations, but emerged in a triple combination along with the lifespan shortening. Thus, WormCat identified a gene set with potential. phenotypic relevance not found with previous GO analysis. In conclusion, WormCat provides a powerful tool for the analysis and visualization of gene set enrichment in different types of C. elegans datasets.
GOing Bayesian: model-based gene set analysis of genome-scale data