Bioinformatic Profiling of Written Artefacts
2020–2025
RFF02
In bioinformatics, mathematical and statistical approaches are applied to biological data to classify and characterize the samples that are investigated. This application enables insights into the composition of the samples that would not be accessible otherwise and is particularly suitable when analytical methods are utilized that generate complex data. In the framework of this project, data from state-of-the-art sequencing techniques will be analyzed with bioinformatic approaches to contribute to the characterization of the biological identity and the historical background of artefacts. In this context, we will characterize and classify written artefacts based on different properties, e.g. production material, preservation states and environmental conditions. For the analysis of DNA fingerprints, data processing pipelines will be developed that adapt established processing steps to the unique characteristics of written artefact samples. In addition, peptide fingerprints will provide complementary information that will allow us to confirm the classification results based on genetic information. In general, we will analyze sequencing data by means of established methods and novel approaches based on machine learning. In order to compare, validate and optimize different data analysis tools, simulated data will be generated and utilized in addition to experimental data obtained from written artefacts.
The aim of the project is the comprehensive sequence-based analysis of written artefacts by establishing various bioinformatic approaches. To achieve this, it is crucial to bundle our expertise with knowledge about paleogenetics (RFA04) and to combine bioinformatic and historical findings, e.g. about South Indian palm-leaf manuscripts (RFA07) and Arabic documents of the early Islamic centuries (RFA01). Anchored in the research unit Data Linking and cooperating with researchers from the natural sciences and the humanities, the project contributes to the fruitful transfer of knowledge and methods between disciplines and projects.
People
Principal Investigator: Stephan Seifert
Research Associate: Lucas F. Voges