Enrichment analysis, crucial for interpreting genomic, transcriptomic, and proteomic data, is expanding into metabolomics. Furthermore, there is a rising demand for integrated enrichment analysis that combines data from different studies and omics platforms, as seen in meta-analysis and multi-omics research. To address these growing needs, we have updated WebGestalt to include enrichment analysis capabilities for both metabolites and multiple input lists of analytes. We have also significantly increased analysis speed, revamped the user interface, and introduced new pathway visualizations to accommodate these updates. Notably, the adoption of a Rust backend reduced gene set enrichment analysis time by 95% from 270.64 to 12.41 s and network topology-based analysis by 89% from 159.59 to 17.31 s in our evaluation. This performance improvement is also accessible in both the R package and a newly introduced Python package. Additionally, we have updated the data in the WebGestalt database to reflect the current status of each source and have expanded our collection of pathways, networks, and gene signatures. The 2024 WebGestalt update represents a significant leap forward, offering new support for metabolomics, streamlined multi-omics analysis capabilities, and remarkable performance enhancements. Discover these updates and more at https://www.webgestalt.org.

中文翻译：

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WebGestalt 2024：更快的基因集分析以及对代谢组学和多组学的新支持


富集分析对于解释基因组、转录组和蛋白质组数据至关重要，并且正在扩展到代谢组学。此外，对整合来自不同研究和组学平台的数据的综合富集分析的需求不断增长，如荟萃分析和多组学研究中所见。为了满足这些不断增长的需求，我们更新了 WebGestalt，以包含代谢物和分析物的多个输入列表的富集分析功能。我们还显着提高了分析速度，改进了用户界面，并引入了新的路径可视化来适应这些更新。值得注意的是，在我们的评估中，采用 Rust 后端将基因集富集分析时间从 270.64 秒减少到 12.41 秒，减少了 95%，将基于网络拓扑的分析时间从 159.59 秒减少到 17.31 秒，减少了 89%。 R 包和新引入的 Python 包也可以实现这种性能改进。此外，我们还更新了 WebGestalt 数据库中的数据，以反映每个来源的当前状态，并扩展了我们的路径、网络和基因特征的收集。 2024 年 WebGestalt 更新代表了一次重大飞跃，为代谢组学提供了新的支持、简化的多组学分析功能以及显着的性能增强。请访问 https://www.webgestalt.org 了解这些更新及更多信息。