Predicting the origin of stains from next generation sequencing mRNA data

Area of Science:

• Forensic Science
• Molecular Biology
• Bioinformatics

Background:

• Accurate identification of body fluid stains is crucial in forensic investigations.
• Existing methods often rely on qualitative marker presence/absence, limiting predictive power.

Purpose of the Study:

• To develop and validate a novel probabilistic model for body fluid identification using next-generation sequencing (NGS) mRNA data.
• To enhance the accuracy of stain origin prediction by incorporating quantitative sequencing information.

Main Methods:

• Analysis of 183 body fluid/tissue samples using a previously established NGS mRNA approach.
• Development of a probabilistic model employing partial least squares and linear discriminant analysis.
• Classification into six common forensic body fluids based on quantitative NGS read counts.

Main Results:

• The developed model successfully predicted the origin of body fluid stains.
• Incorporating quantitative NGS read counts improved prediction accuracy compared to traditional methods.
• The model demonstrated the ability to visualize important markers and their correlations with different body fluids.

Conclusions:

• The novel NGS mRNA-based probabilistic model offers improved accuracy for body fluid identification.
• Quantitative read count data enhances the predictive power of forensic stain origin analysis.
• The approach is effective for analyzing both single and mixed body fluid samples.