Bioinformatics Approach to Assess the Biogeographical Patterns of Soil Communities: The Utility for Soil Provenance1, 2. Issue 4 (22nd January 2018)

• Record Type: Journal Article
• Authors: Damaso, Natalie; Mendel, Julian; Mendoza, Maria; von Wettberg, Eric J.; Narasimhan, Giri; Mills, DeEtta
• Is Part Of: Journal of forensic sciences. Volume 63:Issue 4(2018)
• Page Start: 1033
• Page End: 1042
• Publication Date: 2018-01-22
• Subjects: forensic science -- soil DNA profiling -- spatial scale -- machine learning algorithms -- Random Forest -- soil provenance; Medical jurisprudence -- Periodicals; Forensic sciences -- Periodicals; Forensic Medicine -- Periodicals; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; 363.2505
• DOI: 10.1111/1556-4029.13741
• Languages: English
• ISSNs: 0022-1198

Abstract

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.