Abstract
Motivation: Zooarchaeology by Mass Spectrometry (ZooMS) is a palaeoproteomics method for the taxonomic determination of collagen, which traditionally involves challenging manual spectra analysis with limitations in quantitative results. As the ZooMS reference database expands, a faster and reproducible identification tool is necessary. Here we present SpecieScan, an open-access algorithm for automating taxa identification from raw MALDI-ToF mass spectrometry (MS) data. Results: SpecieScan was developed using R (pre-processing) and Python (automation). The algorithm’s output includes identified peptide markers, closest matching taxonomic group (taxon, family, order), correlation scores with the reference databases, and contaminant peaks present in the spectra. Testing on original MS data from bones discovered at Palaeothic archaeological sites, including Denisova Cave in Russia, as well as using publicly-available, externally produced data, we achieved >90% accuracy at the genus-level and ~92% accuracy at the family-level for mammalian bone collagen previously analysed manually. Availability and implementation: The SpecieScan algorithm, along with the raw data used in testing, results, reference database, and common contaminants lists are freely available on Github (https://github.com/mesve/SpecieScan).
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | btae054 |
| Seiten (von - bis) | 1-12 |
| Seitenumfang | 12 |
| Fachzeitschrift | Bioinformatics |
| Jahrgang | 40 |
| Ausgabenummer | 3 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 1 März 2024 |
Fördermittel
We would like to thank our close collaborator Prof. M. Shunkov and his team (Institute for Archeology and Ethnography, Siberian Branch of the Russian Academy of Sciences, Russia) for providing access to samples from Denisova Cave for this work. Prof. Nicolas Conard was the excavator and responsible person for Vogelherd Cave (Germany); we would like to thank him and his team from the University of Tubingen € for access to material from the site. The material from Vogelherd Cave was analysed by Theresa Bloß as part of her unpublished Master’s dissertation at the University of Vienna, and we kindly thank her for her contribution. We would also like to thank Dr Kristine Korzow Richter and Dr Hossein Rafipoor for their useful comments and advice on the ideas presented in this article. We also thank Dr Laura van der Sluis for assistance in the preparation of samples. This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant number FINDER-StG-715069 to K.D.). E.V. would like to thank the FWO Research Foundation Flanders (Brussels, BE) for the research grant, which allowed for the completion of this article (postdoctoral fellowship grant number 1240123N to E.V.). This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant number FINDER-StG-715069 to K.D.). E.V. would like to thank the FWO Research Foundation Flanders (Brussels, BE) for the research grant, which allowed for the completion of this article (postdoctoral fellowship grant number 1240123N to E.V.).
ÖFOS 2012
- 106005 Bioinformatik
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