A Computational Framework for High-throughput Virus Detection and Pathogen Discovery

High-throughput pathogen detection from environmental samples is a challenging problem given that a specimen may contain different pathogens from a vast diversity of microbes known to cause disease, some of which are yet to be discovered. Traditional diagnostic methods are time consuming and are focused on the detection of a single pathogen or panel of pathogens. Metagenomic sequencing coupled with robust computational tools offer significant advantages over traditional diagnostics in this regard, presenting a novel opportunity for understanding the genetic diversity present in ecosystems and have the potential to increase the efficacy with which we detect pathogens and control disease. We developed a bioinformatics pipeline that utilizes publicly available tools to automate the analyses of viral metagenomes. The biopipeline was benchmarked on Illumina MiSeq data generated from 24 plant specimens collected from three 'maize mixed' farming ecosystems in order to provide the first ever baseline data of the plant virome in Kenya. Subsequent analyses revealed a spectrum of viruses in these specimens including known and highly divergent viruses such as Maize Chlorotic mottle virus (MCMV) and Sugarcane mosaic virus (SMV). The application of these methodologies to metagenomic survey data provides a basis for future investigations about the possible role of these viruses in disease.