Highlights
- •We have optimized the recognition of body fluids from 16S sequence data.
- •The new data handling workflow is based on PLS in combination with LDA.
- •Large datasets were used to evaluate method performance.
- •In a cross-validation, sensitivities were =0.99 for fecal and oral samples and 0.98 for vaginal samples.
- •High method robustness was demonstrated by testing and training on different datasets.
Abstract
In forensics the DNA-profile is used to identify the person who left a biological
trace, but information on body fluid can also be essential in the evidence evaluation
process. Microbial composition data could potentially be used for body fluid recognition
as an improved alternative to the currently used presumptive tests. We have developed
a customized workflow for interpretation of bacterial 16S sequence data based on a
model composed of Partial Least Squares (PLS) in combination with Linear Discriminant
Analysis (LDA). Large data sets from the Human Microbiome Project (HMP) and the American
Gut Project (AGP) were used to test different settings in order to optimize performance.
From the initial cross-validation of body fluid recognition within the HMP data, the
optimal overall accuracy was close to 98%. Sensitivity values for the fecal and oral
samples were ≥0.99, followed by the vaginal samples with 0.98 and the skin and nasal
samples with 0.96 and 0.81 respectively. Specificity values were high for all 5 categories,
mostly >0.99. This optimal performance was achieved by using the following settings:
Taxonomic profiles based on operational taxonomic units (OTUs) with 0.98 identity
(OTU98), Aitchisons simplex transform with C = 1 pseudo-count and no regularization (r = 1) in the PLS step. Variable selection did not improve the performance further.
To test for robustness across sequencing platforms, we also trained the classifier
on HMP data and tested on the AGP data set. In this case, the standard OTU based approach
showed moderately decline in accuracy. However, by using taxonomic profiles made by
direct assignment of reads to a genus, we were able to nearly maintain the high accuracy
levels. The optimal combination of settings was still used, except the taxonomic level
being genus instead of OTU98. The performance may be improved even further by using
higher resolution taxonomic bins.
Keywords
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Article Info
Publication History
Published online: July 15, 2018
Accepted:
July 13,
2018
Received in revised form:
July 12,
2018
Received:
March 26,
2018
Identification
Copyright
© 2018 Elsevier B.V. All rights reserved.
