Niklaus Grunwald, PhD
Research Plant Pathologist
USDA ARS
Corvallis, OR, USA
Jeff Chang
Oregon State University
Corvallis, Oregon, United States
Zach Foster
USDA ARS
Corvallis, Oregon, United States
Martha Sudermann
Oregon State University
Corvallis, Oregon, United States
Camilo Parada Rojas
Oregon State University
Corvallis, Oregon, United States
Logan Blair
USDA ARS
Corvallis, Oregon, United States
Fernanda Iruegas Bocardo
Oregon State University
Corvallis, Oregon, United States
Alexandra Weisberg
Oregon State University
Corvallis, Oregon, United States
Invasive pathogens continue to emerge at accelerated rates worldwide. Biosurveillance requires transformative methods that are affordable, scalable, robust, accurate, and rapid. Use of whole genome sequences (WGS) is a promising solution, but analysis requires many complex steps and bioinformatics expertise, both of which have a significant learning barrier. We describe the implementation and validation of an automated pipeline for pathogen surveillance of any bacterial or eukaryotic pathogen. One unique feature is that PathogensSurveillance automates all steps of the analysis, including reference selection, downloading, and comparisons to reveal pathogens at various taxonomic ranks, including species, subspecies, and variant levels. Secondly, PathogensSurveillance can analyze single or mixed datasets derived from batches of related or unrelated pathogen samples. Thirdly, the pipeline was developed using the Nextflow management system and can be executed via a single command line deployed on any Linux environment or cloud. Pathogensurveillance produces rich visuals including heatmaps, sunburst plots, phylogenetic trees, and minimum spanning networks. The pipeline drastically lowers barriers to adoption and provides unprecedented capabilities for using WGS in diagnostic laboratories and field settings.