The authors used their previously published next generation sequencing (NGS) mRNA approach for body fluid identification to analyze 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. This 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 enables visualization of important markers and their correlation with the different body fluids. This model was compared to previously published methods to show that the inclusion of read count information improves the prediction. Finally, the model was applied to mixed body fluid samples to test its ability to identify the individual components in a mixture. (publisher abstract modified)
Predicting the Origin of Stains From Next Generation Sequencing mRNA Data
NCJ Number
255689
Journal
Forensic Science International: Genetics Volume: 34 Dated: May 2018 Pages: 37-48
Date Published
May 2018
Length
12 pages
Annotation
This article presents a novel probabilistic method that predicts the originating body fluids from mRNA data with high accuracy.
Abstract