微生物是识别体液(静脉和月经血,精液,唾液,和阴道分泌物)和法医遗传学中的皮肤组织。现有已发表的研究主要集中在通过16SrRNA基因测序或宏基因组鸟枪测序来研究微生物DNA。我们很少在普通法医体液/组织上发现微生物RNA水平的调查。因此,尚未详细探讨使用metaracscriptomics来表征常见的法医体液/组织,并且在法医学中的潜在应用仍然未知。这里,我们通过大规模平行测序,对来自健康志愿者的6种常见法医学样本进行了30次转移组分析.经过质量控制和宿主RNA过滤,从干净的阅读中组装了总共345,300个unigenes。四个王国,137门,267类,488个订单,985个家庭,2052属,在所有样品中注释了4690种。还进行了α-和β-多样性和差异分析。因此,唾液和皮肤组显示出高α多样性(辛普森指数),尽管Chao1指数较高,但静脉血液组的多样性最低。具体来说,我们讨论了潜在的微生物污染和核心微生物组,“这可能是法医研究人员特别感兴趣的。此外,我们实现并评估了人工神经网络(ANN),随机森林(RF),和支持向量机(SVM)模型,用于使用属和物种水平的转移基因组特征进行法医体液/组织鉴定(BFID)。人工神经网络和射频预测模型区分了六种法医体液/组织,证明基于微生物RNA的方法可以应用于BFID。与宏基因组学研究不同,元转录组分析可以提供有关活跃微生物群落的信息;因此,它可能有更大的潜力成为法医科学中基于微生物的个体鉴定的强大工具。这项研究是首次尝试探索超转录组配置文件在法医学中的应用潜力。我们的发现有助于加深我们在RNA水平上对微生物群落结构的理解,并且有利于其他法医学应用的metatrscriptomics。
Microorganisms are potential markers for identifying body fluids (venous and menstrual blood, semen, saliva, and vaginal secretion) and skin tissue in forensic genetics. Existing published studies have mainly focused on investigating microbial DNA by 16 S rRNA gene sequencing or metagenome shotgun sequencing. We rarely find microbial RNA level investigations on common forensic body fluid/tissue. Therefore, the use of metatranscriptomics to characterize common forensic body fluids/tissue has not been explored in detail, and the potential application of metatranscriptomics in forensic science remains unknown. Here, we performed 30 metatranscriptome analyses on six types of common forensic sample from healthy volunteers by massively parallel sequencing. After quality control and host RNA filtering, a total of 345,300 unigenes were assembled from clean reads. Four kingdoms, 137 phyla, 267 classes, 488 orders, 985 families, 2052 genera, and 4690 species were annotated across all samples. Alpha- and beta-diversity and differential analysis were also performed. As a result, the saliva and skin groups demonstrated high alpha diversity (Simpson index), while the venous blood group exhibited the lowest diversity despite a high Chao1 index. Specifically, we discussed potential
microorganism contamination and the \"core microbiome,\" which may be of special interest to forensic researchers. In addition, we implemented and evaluated artificial neural network (ANN), random forest (RF), and support vector machine (SVM) models for forensic body fluid/tissue identification (BFID) using genus- and species-level metatranscriptome profiles. The ANN and RF prediction models discriminated six forensic body fluids/tissue, demonstrating that the microbial RNA-based method could be applied to BFID. Unlike metagenomic research, metatranscriptomic analysis can provide information about active microbial communities; thus, it may have greater potential to become a powerful tool in forensic science for microbial-based individual identification. This
study represents the first attempt to explore the application potential of metatranscriptome profiles in forensic science. Our findings help deepen our understanding of the
microorganism community structure at the RNA level and are beneficial for other forensic applications of metatranscriptomics.