Department of Biostatistics, University of Oslo, 0372 Blindern, Norway.
MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London W12 0BZ, United Kingdom.
Genome Res. 2024 Aug 20;34(7):1081-1088. doi: 10.1101/gr.278485.123.
Studies of bacterial adaptation and evolution are hampered by the difficulty of measuring traits such as virulence, drug resistance, and transmissibility in large populations. In contrast, it is now feasible to obtain high-quality complete assemblies of many bacterial genomes thanks to scalable high-accuracy long-read sequencing technologies. To exploit this opportunity, we introduce a phenotype- and alignment-free method for discovering coselected and epistatically interacting genomic variation from genome assemblies covering both core and accessory parts of genomes. Our approach uses a compact colored de Bruijn graph to approximate the intragenome distances between pairs of loci for a collection of bacterial genomes to account for the impacts of linkage disequilibrium (LD). We demonstrate the versatility of our approach to efficiently identify associations between loci linked with drug resistance and adaptation to the hospital niche in the major human bacterial pathogens and .
难以在大群体中测量毒力、耐药性和传染性等特征。相比之下,由于可扩展的高精度长读测序技术,现在已经可以获得许多细菌基因组的高质量完整组装。为了利用这一机会,我们引入了一种表型和无比对的方法,用于从覆盖基因组核心和辅助部分的基因组组装中发现共选择和上位性相互作用的基因组变异。我们的方法使用紧凑的彩色 de Bruijn 图来近似一组细菌基因组中对位点之间的基因组内距离,以考虑连锁不平衡 (LD) 的影响。我们展示了我们的方法的多功能性,可有效地识别与耐药性和适应医院小生境相关的基因座之间的关联,这些基因座存在于主要的人类细菌病原体 和 中。