Leveraging mRNA cMH and machine learning for the deconvolution of mixed body fluid stains in East Asian populations.
- 1Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, 1023-1063 Shatai South Road, Baiyun District, Guangzhou 510515, Guangdong, China.
- 2Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou 510515, Guangdong, China.
- 3Key Laboratory of Forensic Medicine in Shanxi Province, School of Forensic Medicine, Shanxi Medical University, 98 Daxue Street, Yuci District, Jinzhong 030600, Shanxi, China.
Abstract
Body fluid identification methods have become increasingly well-established, but deconvoluting multi-component mixtures remains a significant challenge. When dealing with complex mixtures, methods based on mRNA coding-region SNPs (cSNPs) still face limitations such as limited polymorphisms of markers and insufficient population specificity. mRNA coding-region microhaplotypes (cMHs), combining tissue specificity with high discrimination power, offer a promising alternative for donor attribution in body fluid stains. In this study, we developed a targeted RNA sequencing panel for East Asian populations, comprising 36 body fluid-specific genes containing 48 cMHs (including 108 cSNPs), along with five genes lacking cMHs. This panel enables both tissue source identification and donor attribution for five types of body fluids and their mixtures. Through specificity evaluation of single-source body fluid stains, we identified six novel body fluid-specific genes. Furthermore, by integrating machine learning algorithms with an in silico mixing strategy, we developed a body fluid classifier that achieved accurate inference for both single-source fluids and complex mixtures. The panel's cumulative discrimination power across the five body fluid types ranged from 0.99568126 (peripheral blood) to 0.99999999 (vaginal secretion). In conclusion, the dual-purpose panel demonstrates robust performance in both tissue source identification and donor attribution for single-source body fluids and complex mixtures.
Related
- The Identification of Biological Stains at Crime Scenes: A Promising Role for Proteomics and Machine Learning
- ML4SD: Leveraging Machine Learning and High-Throughput Search Algorithms for an Iterative Growth-Coupled Design Innovation
- Leveraging meteorological legacy effects and machine learning to improve daily gap-free normalized difference vegetation index reconstruction: a critical basis for fine-scale carbon sink capacity monitoring
- Machine learning reveals biocontrol agents shaping disease outcome in natural Arabidopsis populations
- Machine‐Learning Models Trained on Genomic Summary Statistics Assess Extinction Risk and Recovery Potential of Bottlenecked Populations
BibTeX
@article{liu2026leveraging,
title = {{Leveraging mRNA cMH and machine learning for the deconvolution of mixed body fluid stains in East Asian populations.}},
author = {Qinglin Liu and Xiaolian Wu and Fanzhang Lei and Xi Yuan and Meiming Cai and Yiman Wu and Qiong Lan and Chunmei Shen and Bofeng Zhu},
journal = {Briefings in bioinformatics},
year = {2026},
month = sep,
doi = {10.1093/bib/bbag540},
url = {https://europepmc.org/article/MED/42896094},
note = {CC BY},
}
RIS
TY - JOUR
TI - Leveraging mRNA cMH and machine learning for the deconvolution of mixed body fluid stains in East Asian populations.
AU - Qinglin Liu
AU - Xiaolian Wu
AU - Fanzhang Lei
AU - Xi Yuan
AU - Meiming Cai
AU - Yiman Wu
AU - Qiong Lan
AU - Chunmei Shen
AU - Bofeng Zhu
PY - 2026
DA - 2026/09/01
JO - Briefings in bioinformatics
DO - 10.1093/bib/bbag540
UR - https://europepmc.org/article/MED/42896094
AB - Body fluid identification methods have become increasingly well-established, but deconvoluting multi-component mixtures remains a significant challenge. When dealing with complex mixtures, methods based on mRNA coding-region SNPs (cSNPs) still face limitations such as limited polymorphisms of markers and insufficient population specificity. mRNA coding-region microhaplotypes (cMHs), combining tissue specificity with high discrimination power, offer a promising alternative for donor attribution in body fluid stains. In this study, we developed a targeted RNA sequencing panel for East Asian populations, comprising 36 body fluid-specific genes containing 48 cMHs (including 108 cSNPs), along with five genes lacking cMHs. This panel enables both tissue source identification and donor attribution for five types of body fluids and their mixtures. Through specificity evaluation of single-source body fluid stains, we identified six novel body fluid-specific genes. Furthermore, by integrating machine learning algorithms with an in silico mixing strategy, we developed a body fluid classifier that achieved accurate inference for both single-source fluids and complex mixtures. The panel's cumulative discrimination power across the five body fluid types ranged from 0.99568126 (peripheral blood) to 0.99999999 (vaginal secretion). In conclusion, the dual-purpose panel demonstrates robust performance in both tissue source identification and donor attribution for single-source body fluids and complex mixtures.
SN - 1467-5463
SN - 1477-4054
ER -