Population informative markers selected using Wright's fixation index and machine learning improves human identification using the skin microbiome

Research progress on the application of 16S rRNA gene sequencing and machine learning in forensic microbiome individual identification

Forensic microbiome research is a field with a wide range of applications and a number of protocols have been developed for its use in this area of research. As individuals host radically different microbiota, the human microbiome is expected to become a new biomarker for forensic identification. To achieve an effective use of this procedure an understanding of factors which can alter the human microbiome and determinations of stable and changing elements will be critical in selecting appropriate targets for investigation. The 16S rRNA gene, which is notable for its conservation and specificity, represents a potentially ideal marker for forensic microbiome identification. Gene sequencing involving 16S rRNA is currently the method of choice for use in investigating microbiomes. While the sequencing involved with microbiome determinations can generate large multi-dimensional datasets that can be difficult to analyze and interpret, machine learning methods can be useful in surmounting this analytical challenge. In this review, we describe the research methods and related sequencing technologies currently available for application of 16S rRNA gene sequencing and machine learning in the field of forensic identification. In addition, we assess the potential value of 16S rRNA and machine learning in forensic microbiome science.

Artificial intelligence-driven microbiome data analysis for estimation of postmortem interval and crime location

Microbial communities, demonstrating dynamic changes in cadavers and the surroundings, provide invaluable insights for forensic investigations. Conventional methodologies for microbiome sequencing data analysis face obstacles due to subjectivity and inefficiency. Artificial Intelligence (AI) presents an efficient and accurate tool, with the ability to autonomously process and analyze high-throughput data, and assimilate multi-omics data, encompassing metagenomics, transcriptomics, and proteomics. This facilitates accurate and efficient estimation of the postmortem interval (PMI), detection of crime location, and elucidation of microbial functionalities. This review presents an overview of microorganisms from cadavers and crime scenes, emphasizes the importance of microbiome, and summarizes the application of AI in high-throughput microbiome data processing in forensic microbiology.

Advances in machine learning-based bacteria analysis for forensic identification: identity, ethnicity, and site of occurrence

When faced with an unidentified body, identifying the victim can be challenging, particularly if physical characteristics are obscured or masked. In recent years, microbiological analysis in forensic science has emerged as a cutting-edge technology. It not only exhibits individual specificity, distinguishing different human biotraces from various sites of occurrence (e.g., gastrointestinal, oral, skin, respiratory, and genitourinary tracts), each hosting distinct bacterial species, but also offers insights into the accident's location and the surrounding environment. The integration of machine learning with microbiomics provides a substantial improvement in classifying bacterial species compares to traditional sequencing techniques. This review discusses the use of machine learning algorithms such as RF, SVM, ANN, DNN, regression, and BN for the detection and identification of various bacteria, including Bacillus anthracis, Acetobacter aceti, Staphylococcus aureus, and Streptococcus, among others. Deep leaning techniques, such as Convolutional Neural Networks (CNN) models and derivatives, are also employed to predict the victim's age, gender, lifestyle, and racial characteristics. It is anticipated that big data analytics and artificial intelligence will play a pivotal role in advancing forensic microbiology in the future.

Highly host-linked viromes in the built environment possess habitat-dependent diversity and functions for potential virus-host coevolution

Viruses in built environments (BEs) raise public health concerns, yet they are generally less studied than bacteria. To better understand viral dynamics in BEs, this study assesses viromes from 11 habitats across four types of BEs with low to high occupancy. The diversity, composition, metabolic functions, and lifestyles of the viromes are found to be habitat dependent. Caudoviricetes species are ubiquitous on surface habitats in the BEs, and some of them are distinct from those present in other environments. Antimicrobial resistance genes are identified in viruses inhabiting surfaces frequently touched by occupants and in viruses inhabiting occupants' skin. Diverse CRISPR/Cas immunity systems and anti-CRISPR proteins are found in bacterial hosts and viruses, respectively, consistent with the strongly coupled virus-host links. Evidence of viruses potentially aiding host adaptation in a specific-habitat manner is identified through a unique gene insertion. This work illustrates that virus-host interactions occur frequently in BEs and that viruses are integral members of BE microbiomes.

Advances in microbial metagenomics and artificial intelligence analysis in forensic identification

Microorganisms, which are widely distributed in nature and human body, show unique application value in forensic identification. Recent advances in high-throughput sequencing technology and significant reductions in analysis costs have markedly promoted the development of forensic microbiology and metagenomics. The rapid progression of artificial intelligence (AI) methods and computational approaches has shown their unique application value in forensics and their potential to address relevant forensic questions. Here, we summarize the current status of microbial metagenomics and AI analysis in forensic microbiology, including postmortem interval inference, individual identification, geolocation, and tissue/fluid identification.

Determining Informative Microbial Single Nucleotide Polymorphisms for Human Identification

The skin microbiome is a highly abundant and relatively stable source of DNA that may be utilized for human identification (HID). In this study, a set of single nucleotide polymorphisms (SNPs) with a high mean estimated Wright's fixation index (F_ST) (>0.1) and widespread abundance (found in ≥75% of samples compared) were selected from a diverse set of markers in the hidSkinPlex panel. The least absolute shrinkage and selection operator (LASSO) was used in a novel machine learning framework to generate a SNP panel and predict the human host from skin microbiome samples collected from the hand, manubrium, and foot. The framework was devised to emulate a new unknown person introduced to the algorithm and to match samples from that person against a population database. Unknown samples were classified with 96% accuracy (Matthews correlation coefficient [MCC], 0.954) in the test (n = 225 samples) data set. A final panel of informative SNPs was determined for HID (hidSkinPlex+) using all 51 individuals sampled at three body sites in triplicate. The hidSkinPlex+ panel comprises 365 SNPs and yielded prediction accuracy for the correct host of 95% (MCC = 0.949). The accuracy of the hidSkinPlex+ panel may be somewhat overestimated due to using 26 individuals from the training data set for the selection of the final panel. However, this accuracy still provides an indication of performance when tested on new samples. IMPORTANCE One of the fundamental goals in forensic genetics is to identify the source of biological evidence. Methods for detecting human DNA have advanced and can be quite sensitive, but not all DNA samples are amenable to current methods. However, the human skin microbiome is a source of DNA with high copy numbers, and it has the potential for high discriminatory power. The hidSkinPlex panel has been used for HID; however, some aspects of it could be improved. Missing information is ambiguous, as it is unclear if marker drop-out is a by-product of a low-template sample or if the reasons for not observing a marker are biological. Such ambiguity may confound methods for HID, and as such, an improved marker set (hidSkinPlex+) was designed that is considerably smaller and more robust to drop-out (365 SNPs contained in 135 markers) yet still can be used to accurately predict the human host.