Pathogen genomic data are increasingly used to characterize global and local transmission patterns of important human pathogens and to inform public health interventions. Yet, there is no current consensus on how to measure genomic variation. To test the effect of the variant-identification approach on transmission inferences for Mycobacterium tuberculosis, we conducted an experiment in which five genomic epidemiology groups applied variant-identification pipelines to the same outbreak sequence data. We compared the variants identified by each group in addition to transmission and phylogenetic inferences made with each variant set. To measure the performance of commonly used variant-identification tools, we simulated an outbreak. We compared the performance of three mapping algorithms, five variant callers and two variant filters in recovering true outbreak variants. Finally, we investigated the effect of applying increasingly stringent filters on transmission inferences and phylogenies. We found that variant-calling approaches used by different groups do not recover consistent sets of variants, which can lead to conflicting transmission inferences. Further, performance in recovering true variation varied widely across approaches. While no single variant-identification approach outperforms others in both recovering true genome-wide and outbreak-level variation, variant-identification algorithms calibrated upon real sequence data or that incorporate local reassembly outperform others in recovering true pairwise differences between isolates. The choice of variant filters contributed to extensive differences across pipelines, and applying increasingly stringent filters rapidly eroded the accuracy of transmission inferences and quality of phylogenies reconstructed from outbreak variation. Commonly used approaches to identify M. tuberculosis genomic variation have variable performance, particularly when predicting potential transmission links from pairwise genetic distances. Phylogenetic reconstruction may be improved by less stringent variant filtering. Approaches that improve variant identification in repetitive, hypervariable regions, such as long-read assemblies, may improve transmission inference.

中文翻译：

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基因组变异识别方法可能会改变结核分枝杆菌传播推断。

病原体基因组数据越来越多地用于表征重要人类病原体的全球和局部传播模式，并为公共卫生干预提供信息。然而，目前还没有就如何测量基因组变异达成共识。为了测试变异识别方法对结核分枝杆菌传播推断的影响，我们进行了一项实验，其中五个基因组流行病学小组将变异识别管道应用于相同的爆发序列数据。除了对每个变体集进行的传播和系统发育推断之外，我们还比较了每个组识别出的变体。为了衡量常用变异识别工具的性能，我们模拟了一次爆发。我们比较了三种映射算法、五个变体调用者和两个变体过滤器在恢复真实爆发变体方面的性能。最后，我们研究了应用越来越严格的过滤器对传输推断和系统发育的影响。我们发现不同组使用的变体调用方法无法恢复一致的变体集，这可能导致传输推断相互矛盾。更远，不同方法在恢复真实变异方面的表现差异很大。虽然没有单一的变异识别方法在恢复真正的全基因组和爆发水平的变异方面优于其他方法，但根据真实序列数据校准或结合局部重组的变异识别算法在恢复分离株之间的真实成对差异方面优于其他算法。变体过滤器的选择导致了管道之间的广泛差异，并且应用越来越严格的过滤器会迅速侵蚀传播推断的准确性和从爆发变异重建的系统发育质量。常用的鉴别方法 基于真实序列数据校准或结合局部重组的变异识别算法在恢复分离株之间真正的成对差异方面优于其他算法。变体过滤器的选择导致了管道之间的广泛差异，并且应用越来越严格的过滤器会迅速侵蚀传播推断的准确性和从爆发变异重建的系统发育质量。常用的鉴别方法 基于真实序列数据校准或结合局部重组的变异识别算法在恢复分离株之间真正的成对差异方面优于其他算法。变体过滤器的选择导致了管道之间的广泛差异，并且应用越来越严格的过滤器会迅速侵蚀传播推断的准确性和从爆发变异重建的系统发育质量。常用的鉴别方法 应用越来越严格的过滤器会迅速削弱传播推断的准确性和从爆发变异重建的系统发育质量。常用的鉴别方法 应用越来越严格的过滤器会迅速削弱传播推断的准确性和从爆发变异重建的系统发育质量。常用的鉴别方法 结核分枝杆菌 基因组变异的表现各不相同，特别是在从成对遗传距离预测潜在传播链接时。系统发育重建可以通过不太严格的变体过滤来改进。改进重复、高变区域中的变异识别的方法，例如长读组装，可能会改进传输推理。