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TaxSEA:利用分类群集富集分析和公共数据库快速解读微生物组改变

TaxSEA: rapid interpretation of microbiome alterations using taxon set enrichment analysis and public databases.

作者信息

Pham Cong M, Rankin Timothy J, Stinear Timothy P, Walsh Calum J, Ryan Feargal J

机构信息

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.

出版信息

Brief Bioinform. 2025 Mar 4;26(2). doi: 10.1093/bib/bbaf173.

Abstract

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.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82d3/12009713/d6bd6c33a94f/bbaf173ga1.jpg

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