A Step Towards Validation of High-Throughput Sequencing for the Identification of Plant Pathogenic Oomycetes

Abstract

The advancement in high-throughput sequencing (HTS) technology allows the detection of pathogens without the need for isolation or template amplification. Plant regulatory agencies worldwide are adopting HTS as a prescreening tool for plant pathogens in imported plant germplasm. The technique is a multipronged process and, often, the bioinformatic analysis complicates detection. Previously, we developed E-probe diagnostic nucleic acid analysis (EDNA), a bioinformatic tool that detects pathogens in HTS data. EDNA uses custom databases of signature nucleic acid sequences (e-probes) to reduce computational effort and subjectivity when determining pathogen presence in a sample. E-probes of Pythium ultimum and Phytophthora ramorum were previously validated only using simulated HTS data. However, HTS samples generated from infected hosts or pure culture may vary in pathogen concentration, sequencing bias, and data quality, suggesting that each pathosystem requires further validation. Here, we used metagenomic and genomic HTS data generated from infected hosts and pure culture, respectively, to further validate and curate e-probes of Pythium ultimum and Phytophthora ramorum. E-probe length was found to be a determinant of diagnostic sensitivity and specificity; 80-nucleotide e-probes increased the diagnostic specificity to 100%. Curating e-probes to increase specificity affected diagnostic sensitivity only for 80-nucleotide Pythium ultimum e-probes. Comparing e-probes with alternative databases and bioinformatic tools in their speed and ability to find Pythium ultimum and Phytophthora ramorum demonstrated that, although pathogen sequence reads were detected by other methods, they were less specific and slower when compared with e-probes.