BioArch, University of York, York YO10 5DD, UK.
McDonald Institute for Archaeological Research, University of Cambridge, Cambridge, CB2 3ER, UK.
Bioinformatics. 2020 Jun 1;36(12):3719-3725. doi: 10.1093/bioinformatics/btaa181.
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.
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.
The software can be found at https://github.com/bioarch-sjh/bacollite, and can be installed in R using devtools.
Supplementary data are available at Bioinformatics online.
考古动物样本的分类通常通过手动检查基质辅助激光解吸/电离飞行时间（MALDI-ToF）光谱来实现。这是一个耗时的过程，需要大量的培训，并且不能对分类产生置信度的衡量。我们提出了一种新的、自动化的方法，用于对 MALDI-ToF 样本进行分类，前提是每个候选物种的胶原序列可用。该方法从序列数据中得出一组肽质量，以便与样本数据进行比较，这是通过互相关来完成的。一种新的方法用于结合来自多个标记肽的证据，以解释原始比对并得出具有关联置信度衡量的分类。
为了说明该方法的有效性，我们使用之前发表的一篇关于公元 1120 年左右在英国坎特伯雷圣奥古斯丁修道院抄写的《路加福音》副本羊皮纸叶子的分类，对新方法进行了测试。总共有 81 个样本中的 80 个被两种方法赋予了相同的分类。此外，新方法还为每个分类提供了可量化的置信度水平。
该软件可在 https://github.com/bioarch-sjh/bacollite 上找到，并可使用 devtools 在 R 中安装。
补充数据可在 Bioinformatics 在线获得。
