Bioinformatics Approach to Assess the Biogeographical Patterns of Soil Communities: The Utility for Soil Provenance, |
| |
Authors: | Natalie Damaso PhD Julian Mendel PhD Maria Mendoza PhD Eric J von Wettberg PhD Giri Narasimhan PhD DeEtta Mills PhD |
| |
Institution: | 1. Department of Biological Sciences, Florida International University, Miami, FL 33199;2. International Forensic Research Institute, Florida International University, Miami, FL 33199;3. International Center for Tropical Botany, Florida International University, Miami, FL 33133;4. Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Biomolecular Sciences Institute, Florida International University, Miami, FL 33199 |
| |
Abstract: | Soil DNA profiling has potential as a forensic tool to establish a link between soil collected at a crime scene and soil recovered from a suspect. However, a quantitative measure is needed to investigate the spatial/temporal variability across multiple scales prior to their application in forensic science. In this study, soil DNA profiles across Miami‐Dade, FL, were generated using length heterogeneity PCR to target four taxa. The objectives of this study were to (i) assess the biogeographical patterns of soils to determine whether soil biota is spatially correlated with geographic location and (ii) evaluate five machine learning algorithms for their predictive ability to recognize biotic patterns which could accurately classify soils at different spatial scales regardless of seasonal collection. Results demonstrate that soil communities have unique patterns and are spatially autocorrelated. Bioinformatic algorithms could accurately classify soils across all scales with Random Forest significantly outperforming all other algorithms regardless of spatial level. |
| |
Keywords: | forensic science soil DNA profiling spatial scale machine learning algorithms Random Forest soil provenance |
|
|