森林与树木：利用全基因组关联研究和机器学习探索细菌毒力。
Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning.
机构信息
Department of Microbiology and Immunology, Loyola University Chicago Stritch School of Medicine, Maywood, IL 60153, USA.
Department of Microbiology and Immunology, Department of Internal Medicine/Division of Infectious Diseases, University of Michigan, Ann Arbor, MI 48109, USA.
出版信息
Trends Microbiol. 2021 Jul;29(7):621-633. doi: 10.1016/j.tim.2020.12.002. Epub 2021 Jan 14.
Abstract
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.
摘要
廉价且快速的测序技术的出现使得细菌全基因组序列的生成速度达到了前所未有的水平。这些大量的信息揭示了许多细菌物种内部菌株之间遗传多样性的程度出人意料。对这种遗传异质性的认识与对种内毒力变异的认识相吻合。已经开发了许多比较基因组策略，将这些基因型和致病性差异与发现新的毒力因子联系起来。在这里，我们回顾了比较基因组方法在确定细菌毒力决定因素方面的最新进展，重点介绍了全基因组关联研究和机器学习。
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