Abstract
Microbial source-tracking is a useful tool for trace evidence analysis in Forensics. Community-wide massively parallel sequencing profiles can bypass the need for satellite microbes or marker sets, which are unreliable when handling unstable samples. We propose a novel method utilizing Aitchison distance to select important suspects/sources, and then integrate it with existing algorithms in source tracking to estimate the proportions of microbial sample coming from important suspects/sources. A series of comprehensive simulation studies show that the proposed method is capable of accurate selection and therefore improves the performance of current methods such as Bayesian SourceTracker and FEAST in the presence of noise microbial sources.
Highlights
Trace evidence, as defined by the Federal Bureau of Investigation, refers to evidence transferred by a suspect to a crime scene [1]
More commonly this is used to refer to evidence that is minute or evidence where traditional fingerprinting methods are difficult or cannot be performed, but residual information may still be collected, such as hair, soil, fiber, glass, and other environmental objects commonly found at a crime scene
Modern approaches to DNA fingerprinting involve analysis of Short Tandem Repeat (STR) and SNP gene markers that utilize PCR amplification to reduce the effect of contamination and degradation [2]
Summary
As defined by the Federal Bureau of Investigation, refers to evidence transferred by a suspect to a crime scene [1]. More commonly this is used to refer to evidence that is minute or evidence where traditional fingerprinting methods are difficult or cannot be performed, but residual information may still be collected, such as hair, soil, fiber, glass, and other environmental objects commonly found at a crime scene. Microbial alternatives to DNA fingerprinting show promise in trace evidence analysis. Trace microbial profiles provide a rich avenue of exploration in tracking which suspects have had contact with trace evidence
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