SpecieScan: Semi-Automated taxonomic identification of bone collagen peptides from MALDI-ToF MS

Emese Vegh (Corresponding author), Katerina Douka (Corresponding author)

Publications: Contribution to journalArticlePeer Reviewed

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).

Original languageEnglish
Article numberbtae054
Pages (from-to)1-12
Number of pages12
JournalBioinformatics
Volume40
Issue number3
DOIs
Publication statusPublished - 1 Mar 2024

Austrian Fields of Science 2012

  • 106005 Bioinformatics

Keywords

  • species identification
  • bioinformatics
  • Phylogenetics
  • MALDI-TOF-MS
  • ZooMS

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