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

Belk, Aeriel; Xu, Zhenjiang Zech; Carter, David O.; Lynne, Aaron; Bucheli, Sibyl; Knight, Rob; Metcalf, Jessica L.

doi:10.3390/genes9020104

Open AccessArticle

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

by

Aeriel Belk

^1,†,

Zhenjiang Zech Xu

^2,†,

David O. Carter

³,

Aaron Lynne

⁴,

Sibyl Bucheli

⁴

,

Rob Knight

^2,5,6,* and

Jessica L. Metcalf

^1,*

¹

Department of Animal Sciences, Colorado State University, Fort Collins, CO 80525, USA

²

Department of Pediatrics, University of California, La Jolla, San Diego, CA 92093, USA

³

Laboratory of Forensic Taphonomy, Forensic Sciences Unit, Division of Natural Sciences and Mathematics, Chaminade University of Honolulu, Honolulu, HI 96816, USA

⁴

Department of Biological Sciences, Sam Houston State University, Huntsville, TX 77340, USA

⁵

Department of Computer Science and Engineering, University of California, San Diego, CA 92037, USA

⁶

Microbiome Innovation Center, University of California, San Diego, CA 92037, USA

^*

Authors to whom correspondence should be addressed.

^†

Contributed equally.

Genes 2018, 9(2), 104; https://doi.org/10.3390/genes9020104

Submission received: 31 December 2017 / Revised: 12 February 2018 / Accepted: 12 February 2018 / Published: 16 February 2018

(This article belongs to the Special Issue Forensic Genomics)

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Abstract

Death investigations often include an effort to establish the postmortem interval (PMI) in cases in which the time of death is uncertain. The postmortem interval can lead to the identification of the deceased and the validation of witness statements and suspect alibis. Recent research has demonstrated that microbes provide an accurate clock that starts at death and relies on ecological change in the microbial communities that normally inhabit a body and its surrounding environment. Here, we explore how to build the most robust Random Forest regression models for prediction of PMI by testing models built on different sample types (gravesoil, skin of the torso, skin of the head), gene markers (16S ribosomal RNA (rRNA), 18S rRNA, internal transcribed spacer regions (ITS)), and taxonomic levels (sequence variants, species, genus, etc.). We also tested whether particular suites of indicator microbes were informative across different datasets. Generally, results indicate that the most accurate models for predicting PMI were built using gravesoil and skin data using the 16S rRNA genetic marker at the taxonomic level of phyla. Additionally, several phyla consistently contributed highly to model accuracy and may be candidate indicators of PMI.

Keywords: postmortem interval; microbiome; decomposition; Random Forest regression

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MDPI and ACS Style

Belk, A.; Xu, Z.Z.; Carter, D.O.; Lynne, A.; Bucheli, S.; Knight, R.; Metcalf, J.L. Microbiome Data Accurately Predicts the Postmortem Interval Using Random Forest Regression Models. Genes 2018, 9, 104. https://doi.org/10.3390/genes9020104

AMA Style

Belk A, Xu ZZ, Carter DO, Lynne A, Bucheli S, Knight R, Metcalf JL. Microbiome Data Accurately Predicts the Postmortem Interval Using Random Forest Regression Models. Genes. 2018; 9(2):104. https://doi.org/10.3390/genes9020104

Chicago/Turabian Style

Belk, Aeriel, Zhenjiang Zech Xu, David O. Carter, Aaron Lynne, Sibyl Bucheli, Rob Knight, and Jessica L. Metcalf. 2018. "Microbiome Data Accurately Predicts the Postmortem Interval Using Random Forest Regression Models" Genes 9, no. 2: 104. https://doi.org/10.3390/genes9020104

APA Style

Belk, A., Xu, Z. Z., Carter, D. O., Lynne, A., Bucheli, S., Knight, R., & Metcalf, J. L. (2018). Microbiome Data Accurately Predicts the Postmortem Interval Using Random Forest Regression Models. Genes, 9(2), 104. https://doi.org/10.3390/genes9020104

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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

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