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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

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Machine learning clustering and classification of human microbiome source body sites.

Antonio L Tan-Torres¹, J Paul Brooks², Baneshwar Singh³

¹Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA, USA.

Forensic Science International

|October 17, 2021

Summary

Forensic science can now identify biological material origins using microbial signatures from the human body. Machine learning (ML) accurately predicts body site source from metagenomic data, enhancing crime scene investigations.

Keywords:

Human Microbiome Project Machine learning Shotgun metagenomic sequencing Source body site identification

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Area of Science:

Microbiology
Forensic Science
Bioinformatics

Background:

Human body sites harbor distinct microbial communities.
Current methods for identifying biological material origins from crime scenes are limited.
Metagenomics and machine learning offer potential advancements.

Purpose of the Study:

To investigate the utility of machine learning (ML) for forensic source body site identification.
To determine if microbiomic signatures can accurately discriminate between human body sites.
To assess the predictive power of ML algorithms on metagenomic data.

Main Methods:

Utilized shotgun metagenomic sequencing data.
Applied machine learning clustering algorithms for taxonomic classification.
Analyzed microbial signatures at genus, family, and order levels.

Main Results:

Achieved over 99% purity in predicting source body site at the genus taxonomy.
Consistent accuracy was observed across genus, family, and order taxonomies.
A core set of 51 genera also yielded accurate predictions.

Conclusions:

Machine learning effectively identifies human body site origins using microbial signatures.
The developed method offers a replicable and accurate approach for forensic investigations.
Integration of ML predictors is recommended for forensic source identification protocols.