The occurrence of multiple strains of a bacterial pathogen such as   M. tuberculosis   or   C. difficile   within a single human host, referred to as a mixed infection, has important implications for both healthcare and public health. However, methods for detecting it, and especially determining the proportion and identities of the underlying strains, from WGS (whole-genome sequencing) data, have been limited. In this paper we introduce SplitStrains, a novel method for addressing these challenges. Grounded in a rigorous statistical model, SplitStrains not only demonstrates superior performance in proportion estimation to other existing methods on both simulated as well as real   M. tuberculosis   data, but also successfully determines the identity of the underlying strains. We conclude that SplitStrains is a powerful addition to the existing toolkit of analytical methods for data coming from bacterial pathogens and holds the promise of enabling previously inaccessible conclusions to be drawn in the realm of public health microbiology.

中文翻译：

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SplitStrains，一种从 WGS 数据中识别和分离混合结核分枝杆菌感染的工具

在单个人类宿主内出现多种细菌病原体菌株，例如 结核分枝杆菌 或 艰难梭菌 ，称为混合感染，对医疗保健和公共卫生具有重要意义。然而，从 WGS（全基因组测序）数据中检测它，特别是确定潜在菌株的比例和身份的方法受到限制。在本文中，我们介绍了SplitStrains，这是一种解决这些挑战的新方法。以严格的统计模型为基础，SplitStrains不仅在模拟和真实 结核分枝杆菌 的比例估计方面表现出优于其他现有方法的性能      数据，而且还成功地确定了潜在菌株的身份。我们得出结论，SplitStrains是对来自细菌病原体数据的现有分析方法工具包的强大补充，并有望在公共卫生微生物学领域得出以前无法得出的结论。