Trends in Microbiology ( IF 15.9 ) Pub Date : 2021-01-14 , DOI: 10.1016/j.tim.2020.12.002 Jonathan P Allen 1 , Evan Snitkin 2 , Nathan B Pincus 3 , Alan R Hauser 4
The advent of inexpensive and rapid sequencing technologies has allowed bacterial whole-genome sequences to be generated at an unprecedented pace. This wealth of information has revealed an unanticipated degree of strain-to-strain genetic diversity within many bacterial species. Awareness of this genetic heterogeneity has corresponded with a greater appreciation of intraspecies variation in virulence. A number of comparative genomic strategies have been developed to link these genotypic and pathogenic differences with the aim of discovering novel virulence factors. Here, we review recent advances in comparative genomic approaches to identify bacterial virulence determinants, with a focus on genome-wide association studies and machine learning.
中文翻译:
森林和树木:通过全基因组关联研究和机器学习探索细菌毒力
廉价且快速的测序技术的出现使得细菌全基因组序列能够以前所未有的速度生成。如此丰富的信息揭示了许多细菌物种中菌株间遗传多样性的意想不到的程度。对这种遗传异质性的认识与对毒力种内变异的更大认识相对应。已经开发了许多比较基因组策略来将这些基因型和致病性差异联系起来,目的是发现新的毒力因子。在这里,我们回顾了识别细菌毒力决定因素的比较基因组方法的最新进展,重点是全基因组关联研究和机器学习。