The human microbiome contributes significantly to the genetic content of the human body. Genetic and environmental factors help shape the microbiome; and as such, the microbiome can be unique to an individual. Previous studies have demonstrated the potential to use microbiome profiling for forensic applications; however, a method has yet to identify stable features of skin microbiomes that produce high classification accuracies for samples collected over lengthy time intervals. The current project used supervised learning to attribute skin microbiomes from 14 skin body sites of 12 healthy individuals sampled at three time points over a >2.5-year period, with accuracies of up to 100 percent for three body sites. Feature selection identified a reduced subset of markers from each body site that are highly individualizing, identifying 187 markers from 12 clades. Classification accuracies were compared in a formal model testing framework. The results of this analysis indicate that learners trained on nucleotide diversity performed significantly better than those trained on presence/absence encodings. This study used supervised learning to identify individuals with high accuracy and associated stable features from skin microbiomes over a period of up to almost 3 years. These selected features provide a preliminary marker panel for future development of a robust and reproducible method for skin microbiome profiling for forensic human identification. (publisher abstract modified)
Forensic Human Identification Using Skin Microbiomes
NCJ Number
253390
Journal
Applied and Environmental Microbiology Volume: 83 Issue: 22 Dated: 2017
Date Published
2017
Length
6 pages
Annotation
This article describes a novel approach for classifying skin microbiomes to their donors by comparing two feature types: Propionibacterium acnes pangenome presence/absence features and nucleotide diversities of stable clade-specific markers.
Abstract