A new method to detect methylation profiles for forensic body fluid identification combining ARMS-PCR technique and random forest model
A new method to detect methylation profiles for forensic body fluid identification combining ARMS-PCR technique and random forest model
复制标题
结合ARMS-PCR技术和随机森林模型的法医体液鉴定甲基化谱检测新方法
DOI：
10.1016/j.fsigen.2020.102371
复制
发表时间：
2020
影响因子：
3.1
通讯作者：
Lin Zhang
中科院分区：
文献类型：
--
作者：
Huan Tian;Peng Bai;Yu Tan;Zhilong Li;Duo Peng;Xiao;Huan Zhao;Yan Zhou;Weibo Liang;Lin Zhang
A set of DNA methylation markers was detected and evaluated to identify body fluids using the amplification refractory mutation system-PCR (ARMS-PCR) and random forest algorithm. In this study, four multiplex DNA methylation reactions composed of 22 promising methylation markers were used to identify regular forensic body fluids, including venous blood, saliva, semen, menstrual blood, and vaginal fluid. The ARMS-specific primers were used to amplify the candidate markers, and then the methylation values of each CpG site were detected through capillary electrophoresis (CE). The DNA methylation patterns of 22 highly informative methylation markers were consistent with previously reported results to a certain extent. To our knowledge, our study is a new method to apply the ARMS-PCR technique and random forest model to detect DNA methylation patterns and identify the type of body fluids in forensic science, thus providing a new method for forensic body fluid identification. Moreover, we proved that this method is robust, applicable and effective for identifying body fluids using the random forest model. The accuracy to predict all body fluids reached up to 0.9966. We firmly believe that this method will have a great potential in the detection of methylation profiles at the molecular level.
