Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA.
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
Nucleic Acids Res. 2024 Jul 5;52(W1):W415-W421. doi: 10.1093/nar/gkae456.
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.
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,目前正在扩展到代谢组学领域。此外,由于元分析和多组学研究的需求,需要对来自不同研究和组学平台的数据进行综合富集分析的需求也在不断增加。为了满足这些不断增长的需求,我们更新了 WebGestalt,以包括代谢物和多个分析物输入列表的富集分析功能。我们还显著提高了分析速度,改进了用户界面,并引入了新的途径可视化,以适应这些更新。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,网络拓扑分析时间从 159.59 秒减少到 17.31 秒,减少了 95%和 89%。这种性能提升在 R 包和新引入的 Python 包中都可以使用。此外,我们更新了 WebGestalt 数据库中的数据,以反映每个来源的当前状态,并扩展了途径、网络和基因特征的集合。2024 年的 WebGestalt 更新是一个重大的飞跃,为代谢组学提供了新的支持,简化了多组学分析功能,并显著提高了性能。在 https://www.webgestalt.org 上发现这些更新和更多内容。