Adaption of the Aristotle Classifier for Accurately Identifying Highly Similar Bacteria Analyzed by MALDI-TOF MS.
Adaption of the Aristotle Classifier for Accurately Identifying Highly Similar Bacteria Analyzed by MALDI-TOF MS.
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DOI：
10.1021/acs.analchem.9b04049
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发表时间：
2020-01-07
影响因子：
7.4
通讯作者：
Hua D
中科院分区：
文献类型：
--
作者：
Desaire H;Hua D
MALDI-TOF MS 在快速鉴定微生物物种方面显示出巨大的实用性。与传统方法相比，它可以更快、更经济地成功对各种来源的细菌和真菌进行分型。需要改进的一个领域是高度相似的样本的分型，例如来自同一属但不同物种的样本或来自单个物种内但来自不同品系的样本。解决当前这一限制的一种有希望的方法是使用先进的机器学习技术。在这项工作中，我们采用了新开发的机器学习工具亚里士多德分类器来对 MALDI-TOF MS 数据进行细菌分类。该工具最初是为了对糖组学和糖蛋白质组学数据进行分类而开发的，因此我们对其进行了修改，使其非常适合分配细菌蛋白质的质谱数据。该分类器超越了现有的细菌分类基准，当待识别的样本高度相似时，它表现出特别强大的性能。质谱数据和亚里士多德分类器等工具的结合可以改善与具有挑战性的细菌分类问题相关的模糊性。
MALDI-TOF MS has shown great utility for rapidly identifying microbial species. It can be used to successfully type bacteria and fungi from a variety of sources more rapidly and cost-effectively than traditional methods. One area where improvements are necessary is in the typing of highly similar samples, such as those samples from the same genus but different species or samples from within a single species but from different strains. One promising way to address this current limitation is by using advanced machine learning techniques. In this work, we adapt a newly developed machine learning tool, the Aristotle Classifier, to bacterial classification of MALDI-TOF MS data. This tool was originally developed for classifying glycomics and glycoproteomics data, so we modified it to be well-suited for assigning mass spectral data from bacterial proteins. The classifier exceeds existing benchmarks in classifying bacteria, and it shows particularly strong performance when the samples to be identified are highly similar. The combination of mass spectrometry data and tools like the Aristotle Classifier could ameliorate the ambiguities associated with challenging bacterial classification problems.
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