Microbiome Tools for Forensic Science

• NCJ Number: 304195
• Author(s): Jessica L. Metcalf; et al
• Date Published: 2017
• Length: 10 pages
• Grant Number(s): 2011-DN-BX-K533; 2012-DN-BX-K023; 2014-R2-CX-K411; 2014-R2-CX-K011; 2015-DN-BX-K016; 2015-DN-BX-K047; 2016-DN-BX-4194; 2016-DN-BX-0194
• Agencies: NIJ-Sponsored
• Award: Characterization of Bacterial and Fungal Communities Associated with Corpse Decomposition Using Next Generation Sequencing; Human decomposition: A mosaic model for community succession and implications for future forensic research; Improving Estimates of the Postmortem Interval with Metagenomics and Metabolomics; Evaluating the Skin Microbiome as Trace Evidence; Microbial Clocks for Estimating the Postmortem Interval of Human Remains at Three Anthropological Research Facilities; Forensic Identification Using Individual Chemical Signatures; Habitat Specialization and the Evolution of Dispersal; Evaluating the Skin Microbiome ad Trace Evidence

Annotation

This article reports on trends in the use of microbiome tools for forensic science.

 

Abstract

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 postmortem intervals (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. 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; particularly, integrating microbiome and metabolomic data has excellent potential to advance microbial forensics. (publisher abstract modified)