PaPrBaG: A random forest approach for the detection of novel pathogens from NGS data

Abstract

The reliable detection of novel bacterial pathogens from next generation sequencing data is a key challenge for microbial diagnostics. Current computational tools usually rely on sequence similarity and often fail to detect novel species when closely related genomes are unavailable or missing from reference database used. Here, we present the random forest based approach PaPrBaG (Pathogenicity Prediction for Bacterial Genomes). PaPrBaG overcomes genetic divergence by training on a wide set of species with known pathogenicity phenotype. To that end we generated a novel label source of pathogenic and non-pathogenic bacterial strains, using a rule-based protocol to annotate pathogenicity based on genome metadata. A detailed comparative study reveals that PaPrBaG has several advantages over sequence similarity approaches. Most importantly, it always provides a prediction whereas other approaches discard a large number of sequencing reads that are far away from currently known reference genomes. Furthermore, PaPrBaG remains reliable even at very low genomic coverages. Combining PaPrBaG with existing approaches further improves prediction results.