TY - JOUR

T1 - How to get your goat

T2 - automated identification of species from MALDI-ToF spectra

AU - Hickinbotham, Simon

AU - Fiddyment, Sarah

AU - Stinson, Timothy L.

AU - Collins, Matthew J.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

U2 - 10.1093/bioinformatics/btaa181

DO - 10.1093/bioinformatics/btaa181

M3 - Journal article

C2 - 32176274

AN - SCOPUS:85087320225

VL - 36

SP - 3719

EP - 3725

JO - Computer Applications in the Biosciences

JF - Computer Applications in the Biosciences

SN - 1471-2105

IS - 12

ER -