Literature DB >> 28366290

Microbiome Tools for Forensic Science.

Jessica L Metcalf1, Zhenjiang Z Xu2, Amina Bouslimani3, Pieter Dorrestein4, David O Carter5, Rob Knight6.   

Abstract

Microbes are present at every crime scene and have been used as physical evidence for over a century. Advances in DNA sequencing and computational approaches have led to recent breakthroughs in the use of microbiome approaches for forensic science, particularly in the areas of estimating postmortem intervals (PMIs), locating clandestine graves, and obtaining soil and skin trace evidence. Low-cost, high-throughput technologies allow us to accumulate molecular data quickly and to apply sophisticated machine-learning algorithms, building generalizable predictive models that will be useful in the criminal justice system. In particular, integrating microbiome and metabolomic data has excellent potential to advance microbial forensics.
Copyright © 2017. Published by Elsevier Ltd.

Keywords:  forensics; machine learning; metabolomic; microbiome; postmortem interval; trace evidence

Mesh:

Year:  2017        PMID: 28366290     DOI: 10.1016/j.tibtech.2017.03.006

Source DB:  PubMed          Journal:  Trends Biotechnol        ISSN: 0167-7799            Impact factor:   19.536


  19 in total

1.  A 1H NMR metabolomic approach for the estimation of the time since death using aqueous humour: an animal model.

Authors:  Emanuela Locci; Matteo Stocchero; Antonio Noto; Alberto Chighine; Luca Natali; Pietro Emanuele Napoli; Roberto Caria; Fabio De-Giorgio; Matteo Nioi; Ernesto d'Aloja
Journal:  Metabolomics       Date:  2019-05-08       Impact factor: 4.290

Review 2.  Microbiomes in forensic botany: a review.

Authors:  Sarah Ishak; Eleanor Dormontt; Jennifer M Young
Journal:  Forensic Sci Med Pathol       Date:  2021-04-08       Impact factor: 2.007

Review 3.  Microbiota succession throughout life from the cradle to the grave.

Authors:  Cameron Martino; Amanda Hazel Dilmore; Zachary M Burcham; Jessica L Metcalf; Dilip Jeste; Rob Knight
Journal:  Nat Rev Microbiol       Date:  2022-07-29       Impact factor: 78.297

4.  Consistent and correctable bias in metagenomic sequencing experiments.

Authors:  Michael R McLaren; Amy D Willis; Benjamin J Callahan
Journal:  Elife       Date:  2019-09-10       Impact factor: 8.140

Review 5.  Transmission of Airborne Bacteria across Built Environments and Its Measurement Standards: A Review.

Authors:  So Fujiyoshi; Daisuke Tanaka; Fumito Maruyama
Journal:  Front Microbiol       Date:  2017-11-29       Impact factor: 5.640

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

Authors:  Aeriel Belk; Zhenjiang Zech Xu; David O Carter; Aaron Lynne; Sibyl Bucheli; Rob Knight; Jessica L Metcalf
Journal:  Genes (Basel)       Date:  2018-02-16       Impact factor: 4.096

Review 7.  Elucidation of complexity and prediction of interactions in microbial communities.

Authors:  Cristal Zuñiga; Livia Zaramela; Karsten Zengler
Journal:  Microb Biotechnol       Date:  2017-09-19       Impact factor: 5.813

8.  Diurnal variation in the human skin microbiome affects accuracy of forensic microbiome matching.

Authors:  David Wilkins; Xinzhao Tong; Marcus H Y Leung; Christopher E Mason; Patrick K H Lee
Journal:  Microbiome       Date:  2021-06-05       Impact factor: 14.650

9.  Creating a 3D microbial and chemical snapshot of a human habitat.

Authors:  Clifford A Kapono; James T Morton; Amina Bouslimani; Alexey V Melnik; Kayla Orlinsky; Tal Luzzatto Knaan; Neha Garg; Yoshiki Vázquez-Baeza; Ivan Protsyuk; Stefan Janssen; Qiyun Zhu; Theodore Alexandrov; Larry Smarr; Rob Knight; Pieter C Dorrestein
Journal:  Sci Rep       Date:  2018-02-27       Impact factor: 4.379

10.  Temporal dynamics of microbiota before and after host death.

Authors:  David Preiswerk; Jean-Claude Walser; Dieter Ebert
Journal:  ISME J       Date:  2018-06-04       Impact factor: 10.302

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