A Regression-Based Approach for Accurate Source Tracking Using Microbial Communities

Authors: Luo Q, Lu M, Zhang M, Jiang H and An L*

DOI: 10.23880/ijfsc-16000338

Volume 8, Issue 4

Abstract

Microbiome data has emerged as a valuable resource in source tracking studies, owing to its applications in the examination of trace evidence and human identification. In response to the growing importance of microbial information in forensic and environmental investigations, we have developed a novel statistical method called REST (REgression-based Source Tracking). REST offers a robust framework to harness microbiome data for the purpose of linking sample or sink data to a diverse pool of potential sources, identifying missing contributors, and accurately estimating the proportions of each source. Our research findings clearly demonstrate the superiority of the REST method over existing techniques in source tracking. Notably, REST exhibits a remarkable ability to detect and estimate the proportions with a minimal error rate. This advancement holds the potential to significantly enhance the accuracy of identifying individuals in forensic cases and tracking the origins of microbial compositions in environmental samples. By minimizing errors and providing precise estimates, REST opens up new possibilities for improving criminal investigations, mitigating environmental contamination, and addressing public health concerns with greater confidence and precision. The versatility of the REST method extends its applications beyond the scenarios discussed here, making it a valuable tool in various source tracking endeavors.

Keywords: Microbiome; Trace Evidence; Forensic Study; Regression; Source Tracking

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