Polymicrobial samples present unique challenges for mass spectrometric identification. A recently developed glycolipid technology has the potential to accurately identify individual bacterial species from polymicrobial samples. In order to develop and validate bacterial identification algorithms (e.g. machine learning) using this glycolipid technology, generating a large number of various polymicrobial samples can be beneficial, but it is costly and labor‐intensive. Here, we propose an alternative cost‐effective approach that generates realistic in silico polymicrobial glycolipid mass spectra.

中文翻译：

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MGMS2：多微生物样品的膜糖脂质谱模拟器。

多种微生物样品对质谱鉴定提出了独特的挑战。最近开发的糖脂技术有可能从多种微生物样品中准确识别单个细菌物种。为了使用这种糖脂技术开发和验证细菌识别算法（例如机器学习），生成大量各种多微生物样本可能是有益的，但成本高且劳动密集。在这里，我们提出了一种替代的具有成本效益的方法，该方法可以生成逼真的硅多微生物糖脂质谱图。