Pham Cong M, Rankin Timothy J, Stinear Timothy P, Walsh Calum J, Ryan Feargal J
Flinders Health and Medical Research Institute, University Drive, Flinders University, Bedford Park, SA 5042, Australia.
Department of Microbiology and Immunology, Doherty Institute, University of Melbourne, 792 Elizabeth St, Melbourne, VIC 3000, Australia.
Brief Bioinform. 2025 Mar 4;26(2). doi: 10.1093/bib/bbaf173.
Microbial communities are essential regulators of ecosystem function, with their composition commonly assessed through DNA sequencing. Most current tools focus on detecting changes among individual taxa (e.g. species or genera), however in other omics fields, such as transcriptomics, enrichment analyses like gene set enrichment analysis are commonly used to uncover patterns not seen with individual features. Here, we introduce TaxSEA, a taxon set enrichment analysis tool available as an R package, a web portal (https://shiny.taxsea.app), and a Python package. TaxSEA integrates taxon sets from five public microbiota databases (BugSigDB, MiMeDB, GutMGene, mBodyMap, and GMRepoV2) while also allowing users to incorporate custom sets such as taxonomic groupings. In silico assessments show TaxSEA is accurate across a range of set sizes. When applied to differential abundance analysis output from inflammatory bowel disease and type 2 diabetes metagenomic data, TaxSEA can rapidly identify changes in functional groups corresponding to known associations. We also show that TaxSEA is robust to the choice of differential abundance analysis package. In summary, TaxSEA enables researchers to efficiently contextualize their findings within the broader microbiome literature, facilitating rapid interpretation, and advancing understanding of microbiome-host and environmental interactions.
微生物群落是生态系统功能的重要调节者,其组成通常通过DNA测序进行评估。当前大多数工具专注于检测单个分类单元(如物种或属)之间的变化,然而在其他组学领域,如转录组学,基因集富集分析等富集分析通常用于揭示单个特征无法看到的模式。在这里,我们介绍TaxSEA,这是一种分类单元集富集分析工具,可作为R包、网络门户(https://shiny.taxsea.app)和Python包使用。TaxSEA整合了来自五个公共微生物群数据库(BugSigDB、MiMeDB、GutMGene、mBodyMap和GMRepoV2)的分类单元集,同时还允许用户纳入自定义集,如分类分组。计算机模拟评估表明,TaxSEA在一系列集大小范围内都是准确的。当应用于炎症性肠病和2型糖尿病宏基因组数据的差异丰度分析输出时,TaxSEA可以快速识别与已知关联相对应的功能组变化。我们还表明,TaxSEA对差异丰度分析包的选择具有鲁棒性。总之,TaxSEA使研究人员能够在更广泛的微生物组文献中有效地将他们的发现置于背景中,促进快速解释,并推进对微生物组-宿主和环境相互作用的理解。