Statistical analysis of genetic interactions in Tn-Seq data.
Statistical analysis of genetic interactions in Tn-Seq data.
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DOI：
10.1093/nar/gkx128
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发表时间：
2017-06-20
影响因子：
14.9
通讯作者：
Ioerger TR
中科院分区：
文献类型：
--
作者：
DeJesus MA;Nambi S;Smith CM;Baker RE;Sassetti CM;Ioerger TR
Tn-Seq是一种通过构建复杂的随机转座子插入文库并使用下一代测序定量每个突变体丰度来探测基因功能的实验方法。Tn-Seq的一个重要的新兴应用是用于鉴定遗传相互作用，其涉及比较在不同遗传背景（例如野生型菌株与敲除菌株）中产生的Tn突变体文库。已经提出了几种分析方法来分析Tn-Seq数据以识别遗传相互作用，包括估计相对适应度比和拟合广义线性模型。然而，这些都有局限性，需要改进的方法。我们提出了一种分层贝叶斯方法，通过量化富集变化的统计学意义来识别遗传相互作用。该分析涉及跨数据集的插入计数的四向比较，以识别根据遗传背景差异影响细菌适应性的转座子突变体。我们的方法被应用到Tn-Seq库中的结核分枝杆菌的等基因菌株缺乏三个不同的基因的未知功能，以前被证明是必要的感染过程中的最佳健身。通过分析在小鼠中进行选择的文库，我们能够区分每个靶基因的几种不同类型的遗传相互作用，从而阐明它们在感染过程中的功能和作用。
Tn-Seq is an experimental method for probing the functions of genes through construction of complex random transposon insertion libraries and quantification of each mutant's abundance using next-generation sequencing. An important emerging application of Tn-Seq is for identifying genetic interactions, which involves comparing Tn mutant libraries generated in different genetic backgrounds (e.g. wild-type strain versus knockout strain). Several analytical methods have been proposed for analyzing Tn-Seq data to identify genetic interactions, including estimating relative fitness ratios and fitting a generalized linear model. However, these have limitations which necessitate an improved approach. We present a hierarchical Bayesian method for identifying genetic interactions through quantifying the statistical significance of changes in enrichment. The analysis involves a four-way comparison of insertion counts across datasets to identify transposon mutants that differentially affect bacterial fitness depending on genetic background. Our approach was applied to Tn-Seq libraries made in isogenic strains of Mycobacterium tuberculosis lacking three different genes of unknown function previously shown to be necessary for optimal fitness during infection. By analyzing the libraries subjected to selection in mice, we were able to distinguish several distinct classes of genetic interactions for each target gene that shed light on their functions and roles during infection.