Construction and evaluation of in-house methylation-sensitive SNaPshot system and three classification prediction models for identifying the tissue origin of body fluid

The identification of tissue origin of body fluid can provide clues and evidence for criminal case investigations. To establish an efficient method for identifying body fluid in forensic cases, eight novel body fluid-specific DNA methylation markers were selected in this study, and a multiplex singlebase extension reaction (SNaPshot) system for these markers was constructed for the identification of five common body fluids (venous blood, saliva, menstrual blood, vaginal fluid, and semen). The results indicated that the in-house system showed good species specificity, sensitivity, and ability to identify mixed biological samples. At the same time, an artificial body fluid prediction model and two machine learning prediction models based on the support vector machine (SVM) and random forest (RF) algorithms were constructed using previous research data, and these models were validated using the detection data obtained in this study (n=95). The accuracy of the prediction model based on experience was 95.79%; the prediction accuracy of the SVM prediction model was 100.00% for four kinds of body fluids except saliva (96.84%); and the prediction accuracy of the RF prediction model was 100.00% for all five kinds of body fluids. In conclusion, the in-house SNaPshot system and RF prediction model could achieve accurate tissue origin identification of body fluids.体液组织来源的鉴定可为刑事案件的侦查提供线索和证据。为了建立一种高效的法医学体液鉴定方法，本研究选取了8个新的体液特异性DNA甲基化标志物，并基于这些标志物构建了可用于5种常见体液（静脉血、唾液、经血、阴道液和精液）鉴定的多重单碱基延伸反应（SNaPshot）体系。结果表明，该系统具有良好的物种特异性和灵敏度，可用于混合生物样本的鉴定。同时，本研究利用前期研究数据构建了一个人工体液预测模型和两个分别基于支持向量机和随机森林算法的机器学习预测模型，并利用本研究获得的检测数据（n=95）对这些预测模型进行了测试。基于研究者经验建立的人工预测模型的准确率为95.79%，支持向量机预测模型对除唾液（96.84%）外的所有体液的预测准确率均为100.00%，随机森林预测模型对5种体液的预测准确率均为100.00%。综上所述，我们所构建的SNaPshot系统和随机森林预测模型能够实现体液组织来源的准确鉴定。.