Senckenberg am Meer, German Centre for Marine Biodiversity Research (DZMB), 26382, Wilhelmshaven, Germany.
German Centre for Marine Biodiversity Research (DZMB), Senckenberg am Meer, 20146, Hamburg, Germany.
Sci Rep. 2024 Jan 13;14(1):1280. doi: 10.1038/s41598-024-51235-z.
Proteomic fingerprinting using MALDI-TOF mass spectrometry is a well-established tool for identifying microorganisms and has shown promising results for identification of animal species, particularly disease vectors and marine organisms. And thus can be a vital tool for biodiversity assessments in ecological studies. However, few studies have tested species identification across different orders and classes. In this study, we collected data from 1246 specimens and 198 species to test species identification in a diverse dataset. We also evaluated different specimen preparation and data processing approaches for machine learning and developed a workflow to optimize classification using random forest. Our results showed high success rates of over 90%, but we also found that the size of the reference library affects classification error. Additionally, we demonstrated the ability of the method to differentiate marine cryptic-species complexes and to distinguish sexes within species.
利用 MALDI-TOF 质谱进行蛋白质组指纹分析是一种成熟的鉴定微生物的工具，已显示出在鉴定动物物种方面的良好效果，特别是在鉴定疾病媒介和海洋生物方面。因此，它可以成为生态研究中生物多样性评估的重要工具。然而，很少有研究测试过不同目和纲之间的物种鉴定。在这项研究中，我们收集了 1246 个样本和 198 个物种的数据，以测试多样化数据集的物种鉴定。我们还评估了不同的样本制备和数据处理方法，用于机器学习，并开发了一个使用随机森林优化分类的工作流程。我们的结果显示，成功率超过 90%，但我们也发现参考文库的大小会影响分类错误。此外，我们还证明了该方法能够区分海洋隐种复合体，并能够区分物种内的性别。
