Bacterial typing is of great importance in clinical diagnosis, environmental monitoring, food safety analysis, and biological research. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is now widely used to analyze bacterial samples. Identification of bacteria at the species level can be realized by matching the mass spectra of samples against a library of mass spectra of known bacteria. Nevertheless, in order to reasonably type bacteria, identification accuracy should be further improved. Herein, we propose a new framework to the identification and assessment for MALDI-MS based bacterial analysis. Our approach combines new measures for spectra similarity and a novel bootstrapping assessment. We tested our approach on a general data set containing the mass spectra of 1741 strains of bacteria and another challenging data set containing 250 strains, including 40 strains in the Bacillus cereus group that were previously claimed to be impossible to resolve by MALDI-MS. With the bootstrapping assessment, we achieved much more reliable predictions at both the genus and species level, and enabled to resolve the Bacillus cereus group. To the best of the authors’ knowledge, our method is the first to provide a statistical assessment to MALDI-MS based bacterial typing that could lead to more reliable bacterial typing.

中文翻译：

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通过质谱图匹配和自举评估细菌全细胞分型

细菌分型在临床诊断，环境监测，食品安全性分析和生物学研究中非常重要。基质辅助激光解吸/电离质谱（MALDI-MS）现在被广泛用于分析细菌样品。通过将样品的质谱与已知细菌的质谱库进行匹配，可以实现对物种水平细菌的鉴定。然而，为了合理地分型细菌，应该进一步提高识别精度。在这里，我们提出了一个新的框架，用于基于MALDI-MS的细菌分析的鉴定和评估。我们的方法结合了光谱相似性的新度量和新颖的自举评估。先前声称无法通过MALDI-MS解析的蜡状芽孢杆菌群。通过自举评估，我们在属和物种水平上均获得了更为可靠的预测，并能够解决蜡状芽孢杆菌群。据作者所知，我们的方法是第一个对基于MALDI-MS的细菌分型提供统计评估的方法，该方法可以导致更可靠的细菌分型。