LAUSR

Viruses are the main agents causing emerging and re-emerging infectious diseases. It is therefore important to screen for and detect them and uncover the evolutionary processes that support their ability to jump species boundaries and establish themselves in new hosts. Metagenomic next-generation sequencing (mNGS) is a high-throughput, impartial technology that has enabled virologists to detect either known or novel, divergent viruses from clinical, animal, wildlife and environmental samples, with little a priori assumptions. mNGS is heavily dependent on bioinformatic analysis, with an emerging demand for integrated bioinformatic workflows. Here, we present Lazypipe2, an updated mNGS pipeline with, as compared to Lazypipe1, significant improvements in code stability, transparency, with added functionality and support for new software components. We also present extensive benchmarking results, including evaluation on a novel canine simulated metagenome, and precision and recall of virus detection at varying sequencing depth and low to extremely low proportion of viral genetic material. Additionally we report accuracy of virus detection with two strategies: homology searches using nucleotide or amino acid sequences. We show that Lazypipe2 with nucleotide-based annotation approaches near perfect detection of eukaryotic viruses and, in terms of accuracy, outperforms the compared pipelines. We also discuss the importance of homology searches with amino acid sequences for the detection of highly divergent novel viruses.