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POSTERS: Pathogen detection, quantification and diagnosis

In silico detection of multiple oomycetes in metagenomic data by using E-probe Detection of Nucleic acid Analysis (EDNA)
Carla Garzon - Oklahoma State University. Z. Gloria Abad- USDA-APHIS-PPQ S&T CPHST, Andres Espindola- Oklahoma State University, Maria Proano- Oklahoma State University

Plant diseases can be better managed if pathogens are detected early and identified accurately. Metagenomic diagnostic methods allow identification of multiple pathogens from a single sample without pure culture isolation or specific gene-targeted sequencing. This projects utilized E-probe Diagnostic Nucleic acid Analysis (EDNA), a computational pipeline that couples metagenomic sequencing and bioinformatics, to detect multiple oomycete plant pathogens in metagenomic data. EDNA-MiFi, a new bioinformatic tool based on the EDNA concept, consist of of two parts: MiProbe, for selection of pathogen-specific sequences (E-probes); and MiDetect, for the rapid identification of target organisms from metasamples. E-probes 20-120 nucleotides length were obtained for nine pathogenic oomycete species. To avoid false positives, e-probes with similarity to non-target sequences in NCBI’s Nucleotide database were removed. Curated e-probe databases were validated in silico on mock-sequencing databases (MSDs) created using MetaSim, simulating Illumina average read length and average error rates. MSDs contained the genomes of a known host and the target oomycete at different percentages (10 to 10-4). A metasample was positive for the presence of a pathogen when a significant number of e-probes were found in a MSD. Accurate pathogen detection in silico was achieved even at very low pathogen concentrations. EDNA-MiFi is a highly sensitive diagnostic tool for oomycetes from metasamples.