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Microbiome Data Accurately Predicts the Postmortem Interval Using Random Forest Regression Models.

Aeriel Belk1, Zhenjiang Zech Xu2, David O Carter3

  • 1Department of Animal Sciences, Colorado State University, Fort Collins, CO 80525, USA. aeriel.belk@colostate.edu.

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Summary
This summary is machine-generated.

Microbial communities in gravesoil and skin accurately predict the postmortem interval (PMI). Using 16S ribosomal RNA (rRNA) data at the phyla level offers the most robust models for forensic science.

Keywords:
Random Forest regressiondecompositionmicrobiomepostmortem interval

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Area of Science:

  • Forensic microbiology
  • Bioinformatics
  • Ecological modeling

Background:

  • Accurate postmortem interval (PMI) estimation is crucial in death investigations for identification and legal proceedings.
  • Microbial community shifts after death offer a potential biological clock for PMI determination.
  • Previous studies suggest microbial ecology is a promising avenue for forensic science.

Purpose of the Study:

  • To optimize Random Forest regression models for predicting PMI.
  • To evaluate the impact of different sample types, genetic markers, and taxonomic levels on model accuracy.
  • To identify microbial indicators consistently associated with PMI.

Main Methods:

  • Random Forest regression models were constructed using various datasets: gravesoil, torso skin, and head skin.
  • Models were trained using different genetic markers, including 16S ribosomal RNA (rRNA), 18S rRNA, and internal transcribed spacer regions (ITS).
  • Analysis included various taxonomic levels, from sequence variants to phyla, and assessed the informativeness of microbial suites.

Main Results:

  • The most accurate PMI prediction models utilized gravesoil and skin samples with the 16S rRNA genetic marker at the phyla taxonomic level.
  • Specific microbial phyla consistently demonstrated high contribution to model accuracy across different datasets.
  • The findings highlight the potential of certain microbial groups as reliable indicators for PMI estimation.

Conclusions:

  • Microbial community analysis, particularly using 16S rRNA data from gravesoil and skin at the phyla level, provides a robust method for postmortem interval estimation.
  • Certain microbial phyla can serve as valuable indicators for determining the time since death in forensic investigations.
  • This research advances the application of microbial ecology in forensic science for more accurate PMI determination.