Pritchard Justin R, Chao Michael C, Abel Sören, Davis Brigid M, Baranowski Catherine, Zhang Yanjia J, Rubin Eric J, Waldor Matthew K
Department of Microbiology, Harvard Medical School, Boston, Massachusetts, United States of America; Department of Immunology and Infectious Disease, Harvard School of Public Health, Boston, Massachusetts, United States of America.
Department of Microbiology, Harvard Medical School, Boston, Massachusetts, United States of America; Division of Infectious Disease, Brigham and Women's Hospital, Boston, Massachusetts, United States of America; Howard Hughes Medical Institute, Boston, Massachusetts, United States of America.
PLoS Genet. 2014 Nov 6;10(11):e1004782. doi: 10.1371/journal.pgen.1004782. eCollection 2014 Nov.
Transposon-insertion sequencing (TIS) is a powerful approach for deciphering genetic requirements for bacterial growth in different conditions, as it enables simultaneous genome-wide analysis of the fitness of thousands of mutants. However, current methods for comparative analysis of TIS data do not adjust for stochastic experimental variation between datasets and are limited to interrogation of annotated genomic elements. Here, we present ARTIST, an accessible TIS analysis pipeline for identifying essential regions that are required for growth under optimal conditions as well as conditionally essential loci that participate in survival only under specific conditions. ARTIST uses simulation-based normalization to model and compensate for experimental noise, and thereby enhances the statistical power in conditional TIS analyses. ARTIST also employs a novel adaptation of the hidden Markov model to generate statistically robust, high-resolution, annotation-independent maps of fitness-linked loci across the entire genome. Using ARTIST, we sensitively and comprehensively define Mycobacterium tuberculosis and Vibrio cholerae loci required for host infection while limiting inclusion of false positive loci. ARTIST is applicable to a broad range of organisms and will facilitate TIS-based dissection of pathways required for microbial growth and survival under a multitude of conditions.
转座子插入测序(TIS)是一种强大的方法,可用于解读细菌在不同条件下生长的遗传需求,因为它能够对数千个突变体的适应性进行全基因组范围的同步分析。然而,目前用于TIS数据比较分析的方法并未针对数据集之间的随机实验变异进行调整,并且仅限于对已注释的基因组元件进行研究。在此,我们介绍了ARTIST,这是一种易于使用的TIS分析流程,用于识别在最佳条件下生长所需的必需区域以及仅在特定条件下参与生存的条件必需位点。ARTIST使用基于模拟的归一化来建模和补偿实验噪声,从而增强条件TIS分析中的统计功效。ARTIST还采用了隐马尔可夫模型的一种新颖变体,以生成全基因组范围内与适应性相关位点的统计稳健、高分辨率、与注释无关的图谱。使用ARTIST,我们灵敏且全面地定义了宿主感染所需的结核分枝杆菌和霍乱弧菌位点,同时限制了假阳性位点的纳入。ARTIST适用于广泛的生物体,并将有助于基于TIS剖析微生物在多种条件下生长和生存所需的途径。