@article{hickinbotham_fiddyment_stinson_collins_2020, title={How to get your goat: automated identification of species from MALDI-ToF spectra}, volume={36}, ISSN={["1460-2059"]}, DOI={10.1093/bioinformatics/btaa181}, abstractNote={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.
               }, number={12}, journal={BIOINFORMATICS}, author={Hickinbotham, Simon and Fiddyment, Sarah and Stinson, Timothy L. and Collins, Matthew J.}, year={2020}, month={Jun}, pages={3719–3725} }