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.

中文翻译：

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WormCat：用于注释和可视化秀丽隐杆线虫基因组规模数据的在线工具。

大型基因表达数据集的出现表明需要改进的工具来识别富集基因类别和可视化富集模式。虽然基因个体发育（GO）为基因集富集分析提供了有价值的工具，但它有一些局限性。首先，很难将多个 GO 分析绘制成图表以进行比较。其次，一些模型系统的基因没有得到很好的体现。例如，常用数据库的分析中缺失了约 30% 的秀丽隐杆线虫基因。为了对不同类型的基因组规模数据中丰富的线虫基因集进行分类和可视化，我们开发了 WormCat，这是一种基于网络的工具，它使用线虫基因组的近乎完整的注释来识别共表达的基因集并进行缩放用于富集可视化的热图。我们使用各种已发布的转录组数据集测试了 WormCat 的性能，并表明它再现了 GO 识别的主要类别。重要的是，我们还发现了以前未识别的类别，这些类别可为解释表型或预测生物功能提供信息。例如，我们分析了已发表的使用延长寿命药物组合治疗的线虫的 RNA-seq 数据，其中一种组合反而缩短了寿命。使用 WormCat，我们将甾醇代谢确定为一个类别，该类别在单一或双重组合中并未丰富，但随着寿命的缩短而在三重组合中出现。因此，WormCat 确定了一个具有潜力的基因组。之前的 GO 分析未发现表型相关性。总之，WormCat 提供了一个强大的工具，用于分析和可视化不同类型线虫数据集中的基因集富集。