Motivation

Classification of archaeological animal samples is commonly achieved via manual examination of 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, U.K. 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

The software can be found at https://github.com/bioarch-sjh/bacollite, and can be installed in R using devtools.

Supplementary information

Supplementary dataSupplementary data are available at Bioinformatics online.

中文翻译：

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如何饲养山羊：根据MALDI-ToF光谱自动识别物种。

动机

考古动物样品的分类通常通过手动检查MALDI-ToF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信心。我们提供了一种新的自动化方法，可以对MALDI-ToF样品进行分类，前提是每个候选物种的胶原序列均可用。该方法从序列数据中导出一组肽质量，以与样品数据进行比较，这是通过互相关进行的。一种结合来自多个标记肽的证据的新颖方法可用于解释原始比对并通过相关的置信度进行分类。

结果

为了说明这种方法的有效性，我们使用了先前出版的卢克福音书副本中的羊皮纸叶分类法对新方法进行了测试，该书是英国坎特伯雷的圣奥古斯丁修道院的抄写员于公元1120年制作的，共有81个样本中的80个两种方法都给了相同的分类。另外，新方法对每种分类都给出了可量化的置信度。

可用性

该软件可以在https://github.com/bioarch-sjh/bacollite找到，并且可以使用devtools安装在R中。

补充资料

补充数据补充数据可从Bioinformatics在线获得。