Sehgeet Kaur
School of Plant and Environmental Sciences and Graduate Program in Genetics, Bioinformatics, and Computational Biology, Virginia Tech, Blacksburg, VA 24061, U.S.A
Blacksburg, Virginia, United States
Parul Sharma
School of Medicine, Emory University, Atlanta, GA, USA
Atlanta, Georgia, United States
Eric Newberry
Science and Technology, Animal and Plant Health Inspection Service, USDA
Laurel, Maryland, United States
Tiffany Lowe-Power (she/her/hers)
Asst Professor
Department of Plant Pathology, University of California
Davis, CA, USA
Tessa Pierce-Ward
Department of Computer Science, Virginia Tech, Blacksburg, VA, US
Davis, California, United States
Reza Mazloom
Graduate Researcher
Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, U.S.A.
Blacksburg, Virginia, United States
Lenwood S. Heath
Professor
Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, U.S.A.
Blacksburg, Virginia, United States
Boris A. Vinatzer
Professor
School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061
Blacksburg, Virginia, United States
Fast, accurate, and precise pathogen identification is critical for plant disease surveillance and biosecurity. Metagenomics, i.e., sequencing all DNA in a sample and identifying all organisms in it, is a promising approach to address this need. However, most metagenomic classification software tools rely on species-rank taxonomy, failing to distinguish between strains of the same species that have different host ranges, differ in other phenotypes, or have different geographic distributions. Therefore, we developed LINtax, a tool that enhances the resolution of Kraken 2, a popular metagenomic classifier, by replacing taxonomic ranks with within-species classes at multiple similarity thresholds. To evaluate LINtax, we used simulated metagenomes and metagenomes of plant samples that were infected with known or unknown Ralstonia solanacearum species complex (RSSC) strains, using both short and long-read datasets. Our results demonstrated that LINtax allows for high-resolution classification assigning strains to separate sequence variants (sequevars), which is critical for surveillance of sequevars regulated as select agents. Quick and precise identification of these agents will allow for efficient implementation of proper biosecurity measures. However, we detected some misclassification, thus we are comparing different approaches to reduce that. In conclusion, metagenomic sequencing in combination with LINtax has the potential to offer a powerful tool for agricultural biosecurity, enabling faster and more precise pathogen identification, but further improvements are needed.