利用 mRNA cMH 与机器学习对东亚人群混合体液斑迹进行去卷积
Leveraging mRNA cMH and machine learning for the deconvolution of mixed body fluid stains in East Asian populations
研究构建了一个面向东亚人群的靶向 RNA 测序 panel，包含 36 个体液特异性基因中的 48 个 cMH（覆盖 108 个 cSNP）及 5 个不含 cMH 的基因，可对五种体液及其混合样本同时完成组织来源鉴定与供者归属。通过对单一来源体液斑迹的特异性评估，研究鉴定出 6 个新的体液特异性基因；结合机器学习算法与 in silico 混合策略开发的体液分类器，对单一来源体液和复杂混合样本均能准确推断。该 panel 在五种体液中的累积辨别力从外周血的 0.99568126 到阴道分泌物的 0.99999999。
来源：Briefings in Bioinformatics · doi.org
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.