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Updated: Jun 1, 2026
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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
Published on: October 15, 2019
Investigative leads and individualization through forensically relevant human commensal microbiome
Hirak Ranjan Dash1, Surajit Das2
1Department of Forensic Science, National Forensic Sciences University, New Delhi, India; School of Forensic Sciences, Centurion University of Technology and Management, Bhubaneswar, Odisha, India.
Advances in Applied Microbiology
|May 30, 2026
Summary
Human microbiome analysis offers powerful forensic evidence for identifying contacts and estimating time of death. Despite challenges, its potential in the post-DNA era for individual identification is significant.
Area of Science:
- Forensic microbiology
- Human microbiome analysis
- DNA sequencing technologies
Background:
- High-throughput DNA sequencing enables rapid analysis of the human microbiome.
- The human microbiome varies based on geographic, lifestyle, and ethnic factors.
- Microbiome analysis is established for predicting post-mortem intervals and identifying body fluids.
Purpose of the Study:
- To describe the forensic applications of human microbiome analysis.
- To highlight the challenges and promise of microbiome analysis in forensic science.
- To discuss the role of microbiome in identifying individuals and body fluids.
Main Methods:
- Analysis of human microbiome data from crime scenes.
- Utilizing a Forensic Microbiome Database for microorganism cataloging.
- Review of technological and data-related challenges in forensic microbiome analysis.
Main Results:
- Microbiome analysis provides evidence of human contact and body fluid identification.
- Personalized microbiome data can be influenced by diverse environmental factors.
- Established role in estimating post-mortem intervals.
Conclusions:
- Forensic microbiome analysis holds significant promise for generating investigative leads.
- It offers potential for individual identification in the post-DNA profiling era.
- Addressing technological and data challenges is crucial for practical application.
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