aurora: a machine learning gwas tool for analyzing microbial habitat adaptation

被引：0
作者：
Bujdos, Dalimil [1 ,2 ]
Walter, Jens [1 ,2 ,3 ]
O'Toole, Paul W. [1 ,2 ]
机构：
[1] Natl Univ Ireland, Univ Coll Cork, APC Microbiome Ireland, Cork, Ireland
[2] Natl Univ Ireland, Univ Coll Cork, Sch Microbiol, Cork, Ireland
[3] Univ Coll Cork, Natl Univ Ireland, Dept Med, Cork, Ireland
来源：
GENOME BIOLOGY | 2025年 / 26卷 / 01期
关键词：
GWAS; Microbial GWAS; Habitat adaptation; Machine learning; Limosilactobacillus reuteri; Lactiplantibacillus plantarum; Salmonella Typhimurium; Mycobacterium paratuberculosis; Autochthonous; Allochthonous; GENOME-WIDE ASSOCIATION; LACTOBACILLUS-PLANTARUM; SALMONELLA-ENTERICA; PROTECTIVE EFFICACY; ECOLOGICAL ROLE; TYPHIMURIUM; REUTERI; DIVERSITY; VIRULENCE; PARATUBERCULOSIS;
D O I：
10.1186/s13059-025-03524-7
中图分类号：
Q81 [生物工程学（生物技术）]; Q93 [微生物学];
学科分类号：
071005 ; 0836 ; 090102 ; 100705 ;
摘要：
A primary goal of microbial genome-wide association studies is identifying genomic variants associated with a particular habitat. Existing tools fail to identify known causal variants if the analyzed trait shaped the phylogeny. Furthermore, due to inclusion of allochthonous strains or metadata errors, the stated sources of strains in public databases are often incorrect, and strains may not be adapted to the habitat from which they were isolated. We describe a new tool, aurora, that identifies autochthonous strains and the genes associated with habitats while acknowledging the potential role of the habitat adaptation trait in shaping phylogeny.
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页数：40
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