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Predicting the origin of stains from next generation sequencing mRNA data


Dørum, Guro; Ingold, Sabrina; Hanson, Erin; Ballantyne, Jack; Snipen, Lars; Haas, Cordula (2018). Predicting the origin of stains from next generation sequencing mRNA data. Forensic Science International. Genetics, 34:37-48.

Abstract

We used our previously published NGS mRNA approach for body fluid identification to analyse 183 body fluids/tissues, including mock casework samples. The resulting data set was used to build a probabilistic model that predicts the origin of a stain. Our approach uses partial least squares followed by linear discriminant analysis to classify samples into six commonly occurring forensic body fluids. The model differs from the ones previously suggested in that it incorporates quantitative information (NGS read counts) rather than just presence/absence of markers. The suggested approach also allows for visualisation of important markers and their correlation with the different body fluids. We compared our model to previously published methods to show that the inclusion of read count information improves the prediction. Finally, we applied the model to mixed body fluid samples to test its ability to identify the individual components in a mixture.

Abstract

We used our previously published NGS mRNA approach for body fluid identification to analyse 183 body fluids/tissues, including mock casework samples. The resulting data set was used to build a probabilistic model that predicts the origin of a stain. Our approach uses partial least squares followed by linear discriminant analysis to classify samples into six commonly occurring forensic body fluids. The model differs from the ones previously suggested in that it incorporates quantitative information (NGS read counts) rather than just presence/absence of markers. The suggested approach also allows for visualisation of important markers and their correlation with the different body fluids. We compared our model to previously published methods to show that the inclusion of read count information improves the prediction. Finally, we applied the model to mixed body fluid samples to test its ability to identify the individual components in a mixture.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Legal Medicine
Dewey Decimal Classification:340 Law
610 Medicine & health
Uncontrolled Keywords:Pathology and Forensic Medicine, Genetics
Language:English
Date:1 May 2018
Deposited On:22 Nov 2018 09:43
Last Modified:24 Sep 2019 23:53
Publisher:Elsevier
ISSN:1872-4973
OA Status:Closed
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.fsigen.2018.01.001
PubMed ID:29413634
Project Information:
  • : FunderFP7
  • : Grant ID285487
  • : Project TitleEUROFORGEN-NOE - EUROPEAN FORENSIC GENETICS Network of Excellence

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