Abstract

Optimal determination of the postmortem interval (PMI) is one of the cornerstones of forensic analysis but oftentimes the dependence and variability of the conventional methods like entomology, histology and biochemical techniques most commonly experience sudden limitations since they are sensitive to the environment. New also cites the human microbiome as an active and quantifiable state of an after death postmortem clock, and post mortal microbial succession is an excellent clock. The integration of Next-Generation Sequencing (NGS) technologies and Machine Learning (ML) algorithms, though, is revolutionizing the field and is now able to profile microorganisms at high resolution and model and predict PMI with high accuracy. NGS technologies such as 16S rRNA sequencing, shotgun metagenomics and whole-genome sequencing can potentially lead organizations to find various types of microbial communities which were not used before with higher accuracy. None of them are capable, however, of bulking up these enormous data sets with superior computing methods. Machine learning algorithms like rule-based systems Rain Forest, the Support Vector Machines and deep learning systems have show the potential to recognize patterns of microbial succession regimes and translate them into useful prediction of PMI. The answer to this challenge lies in switched NGS and ML: with sequencing, the biological signal is provided, and predictive models are utilized in order to refine the temporal resolution and minimize the margin of error. Even with such progress, there are more challenging problems. In this instance variability between settings, the absence of standard procedures, small databases of forensic microbiomes, and its potential legal inadmissibility all pose hurdles to its application on a large scale. The development of portable sequencing machines, global forensic databases on microbes, and the explicable AI to suit the needs of the judiciary are the way forward. The article is a review of the current conditions of microbiome-based PMI estimation with a focus on highlighting how both NGS and ML can be valuable in developing an effective and standardized postmortem clock in forensic scientists.

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