Author:
Rossel Sven,Peters Janna,Charzinski Nele,Eichsteller Angelina,Laakmann Silke,Neumann Hermann,Martínez Arbizu Pedro
Abstract
AbstractProteomic 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.
Funder
Deutsche Forschungsgemeinschaft
Deutsche Forschungsgemeinschaft,Germany
The Federal Ministry of Education and Research
Niedersächsisches Ministerium für Wissenschaft und Kultur
Volkswagen Foundation
Carl von Ossietzky Universität Oldenburg
Publisher
Springer Science and Business Media LLC
Cited by
2 articles.
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