How to get your goat: automated identification of species from MALDI-ToF spectra

Author:

Hickinbotham Simon1ORCID,Fiddyment Sarah12,Stinson Timothy L3,Collins Matthew J24

Affiliation:

1. BioArch, University of York, York YO10 5DD, UK

2. McDonald Institute for Archaeological Research, University of Cambridge, Cambridge, CB2 3ER, UK

3. North Carolina State University, Raleigh, NC 27695, USA

4. Evogenomics Section, University of Copenhagen, Copenhagen 1307 K, Denmark

Abstract

Abstract Motivation Classification of archaeological animal samples is commonly achieved via manual examination of matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) spectra. This is a time-consuming process which requires significant training and which does not produce a measure of confidence in the classification. We present a new, automated method for arriving at a classification of a MALDI-ToF sample, provided the collagen sequences for each candidate species are available. The approach derives a set of peptide masses from the sequence data for comparison with the sample data, which is carried out by cross-correlation. A novel way of combining evidence from multiple marker peptides is used to interpret the raw alignments and arrive at a classification with an associated confidence measure. Results To illustrate the efficacy of the approach, we tested the new method with a previously published classification of parchment folia from a copy of the Gospel of Luke, produced around 1120 C.E. by scribes at St Augustine’s Abbey in Canterbury, UK. In total, 80 of the 81 samples were given identical classifications by both methods. In addition, the new method gives a quantifiable level of confidence in each classification. Availability and implementation The software can be found at https://github.com/bioarch-sjh/bacollite, and can be installed in R using devtools. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Marie Curie International Fellowship

PALIMPSEST

British Academy Postdoctoral Fellowship

Danish National Research Foundation

ERC Investigator

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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