Contracting authority: Fachhochschule Westschweiz (Hes-so)
Project period: 2017-2020
The impact of farming practices and pesticides on soil quality and health is a growing concern for consumers, farmers and land managers. To evaluate this impact, bioindicators such as protists have great potential but their use is limited because current methods do not allow for the analysis of soil samples in a detailed and rapid manner. To overcome these disadvantages, the identification of species based on DNA sequences coupled with the new next-generation sequencing techniques represents a promising approach, but the enormous amount of sequences and their large complexity makes it difficult to treat them by conventional means. It is therefore essential to develop methods that combine bioinformatics and machine learning to (i) quantify, analyze and treat sequences of protists; (ii) identifying and selecting bioindicators (a subset of protists) associated with different stressors; but also to (iii) model their relative abundance according to the different conditions, thus leading to the construction of diagnostic models. For this project, we aim to develop a biomonitoring approach in vine soils based on the quantification of metabarcoding of protists and on the predictive power of machine learning.