Microbiome Tools for Forensic Science. Issue 9 (September 2017)

• Record Type: Journal Article
• Authors: Metcalf, Jessica L.; Xu, Zhenjiang Z.; Bouslimani, Amina; Dorrestein, Pieter; Carter, David O.; Knight, Rob
• Is Part Of: Trends in biotechnology. Volume 35:Issue 9(2017)
• Page Start: 814
• Page End: 823
• Publication Date: 2017-09
• Subjects: microbiome -- forensics -- postmortem interval -- trace evidence -- machine learning -- metabolomic; Biotechnology -- Periodicals; Biochemical engineering -- Periodicals; Genetic engineering -- Periodicals; Industrial microbiology -- Periodicals; 660.605
• DOI: 10.1016/j.tibtech.2017.03.006
• Languages: English
• ISSNs: 0167-7799

Abstract

Abstract : Microbes are present at every crime scene and have been used as physical evidence for over a century. Advances in DNA sequencing and computational approaches have led to recent breakthroughs in the use of microbiome approaches for forensic science, particularly in the areas of estimating postmortem intervals (PMIs), locating clandestine graves, and obtaining soil and skin trace evidence. Low-cost, high-throughput technologies allow us to accumulate molecular data quickly and to apply sophisticated machine-learning algorithms, building generalizable predictive models that will be useful in the criminal justice system. In particular, integrating microbiome and metabolomic data has excellent potential to advance microbial forensics. Trends: Microbes have been used as physical evidence for over a century. With recent advances in microbiome science, new opportunities exist for microbiome technologies in forensic science, particularly in the areas of estimating PMIs, location of clandestine graves, and soil and skin trace evidence. Integrating microbiome and metabolomic data sets has the potential to improve our predictive ability, thereby lowering error rates, which is key to establishing new methods for the criminal justice system. Low-cost, high-throughput technologies allow us to accumulate data quickly and to apply sophisticated machine-learning algorithms, building generalizable predictive models.