Predicting The Origin Of Stains From Next Generation Sequencing Mrna Data

Keywords

Body fluid identification; Forensic science; Linear discriminant analysis (LDA); Massive parallel sequencing (MPS); mRNA; Partial least squares (PLS); Prediction model

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.

Publication Date

5-1-2018

Publication Title

Forensic Science International: Genetics

Volume

34

Number of Pages

37-48

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.fsigen.2018.01.001

Socpus ID

85041419466 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85041419466

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