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Body fluid identification is an important area of forensic casework as it aids in crime scene reconstruction. Nonetheless, accurate body fluid identification remains a challenge in forensic cases given the limitations of current identification methods. Recent human microbiome studies have shown the tissue-specificity of microbial communities, highlighting their potential utilization to distinguish body sites. In this project we investigate the robustness and accuracy of microbial forensic methods for body fluid identification, utilizing next-generation sequencing methods as well as machine-learning algorithms. In addition, we also explore the potential of microbiome data for individual identification.
Bioinformatics Group, Institute of Molecular Life Sciences
Applied Computational Genomics group