Bioinformatics Approach to Assess the Biogeographical Patterns of Soil Communities: The Utility for Soil Provenance,

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.

Single Fragment or Bulk Soil DNA Metabarcoding: Which is Better for Characterizing Biological Taxa Found in Surface Soils for Sample Separation?

“…Finally, the sample sites in this study were not only limited in number ( n = 10) and geographic location (i.e., only Virginia, USA), but also spatial and temporal variation. Given that previously published studies have identified that biological taxa can vary within a sample site (e.g., 10, 50, and 100 m apart), between seasons and across soil depths (e.g., [16,23,27,63,64]), additional studies designed to comprehensively assess the impact of these variables on sample separation is warranted.…”

Massively parallel sequencing is unlocking the potential of environmental trace evidence

“…plants, invertebrates) which can provide additional distinguishing features. As a result, the potential of eDNA for intelligence purposes to predict the origin of an unknown soil or dust sample [23][24][25]50], or even track the movements of an individual, has also been explored [36].…”

“…MiSAFE Project, a European initiative to standardise soil forensic DNA analysis involving 12 laboratories, also demonstrated that RISA and 16S MPS could both correctly affiliate a sample from a spade recovered from a suspect's home to the crime scene in the scenario. Furthermore, a number of studies have also incorporated temporal variation to demonstrate that profiles generated from soils collected weeks or months after a crime will likely be representative of the location where the soil transfer took place [19,21,50,54].…”

“…The cost of MPS has reduced over the past 10 years which has vastly increased sampling capacity. This has enabled large scale exploration of soil biota to assess and understand soil biogeographical variation at a continental scale [23][24][25][26]50] and the ability to target multiple taxonomic groups at a lower cost allowing a more comprehensive picture of diversity and thus increased discriminatory power of soil comparisons [55,56]. Such capability provides a framework for applying more robust statistics to soil comparisons of evidential value but also unlocks the potential of soil biota as a means to identify the provenance of an unknown sample for intelligence purposes, in the absence of case-specific reference samples.…”

Education and training in microbial forensics

“…Bioinformatic algorithms have simplified comparative genomics. Bioinformatics for medicine, microbial genome, and agriculture (Dahiya, 2017) are able to determine biologically relevant patterns in complex datasets (Damaso et al, 2018) as well as allow analyses of wholegenome shotgun sequencing of complex mixtures such as microbiomes (Citation: Chen and Pachter, 2005). The information derived from complex data will have to be extracted using algorithms such as support vector machines (Xu et al, 2015;Schlecht et al, 2008), neural networks Vidaki and Kayser, 2017), and other more complex algorithms yet to be developed.…”