An artificial neural network identifies glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

Abstract

Artificial neural networks can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network to support environmental monitoring efforts in case of a contamination event by detecting induced changes towards the microbial communities. The neural net was trained on taxonomic cluster count tables obtained via next-generation amplicon sequencing of water column samples originating from a lab microcosm incubation experiment conducted over 140 days to determine the effects of the herbicide glyphosate on succession within brackish-water microbial communities. Glyphosate-treated assemblages were classified correctly; a subsetting approach identified the clusters primarily responsible for this, permitting the reduction of input features. This study demonstrates the potential of artificial neural networks to predict indicator species in cases of glyphosate contamination. The results could empower the development of environmental monitoring strategies with applications limited to neither glyphosate nor amplicon sequence data.\n\nHighlight bullet pointsO_LIAn artificial neural net was able to identify glyphosate-affected microbial community assemblages based on next generation sequencing data\nC_LIO_LIDecision-relevant taxonomic clusters can be identified by a stochastically subsetting approach\nC_LIO_LIJust a fraction of present clusters is needed for classification\nC_LIO_LIFiltering of input data improves classification\nC_LI