Dichorhavirus is a recently accepted plant virus genus within the family Rhabdoviridae. Species assigned to the genus consist of bi-segmented, negative sense, single-stranded RNA viruses and are transmitted by Brevipalpus spp. Currently, there are five recognized species and two unclassified members in the genus Dichorhavirus. Four out of seven-orchid fleck virus (OFV), citrus leprosis virus N, citrus chlorotic spot virus, and citrus bright spot virus-can infect citrus and produce leprosis disease-like symptoms. The E-probe Diagnostic for Nucleic Acid Analysis (EDNA) was developed to reduce computational effort and then integrated within Microbe-Finder (MiFi^®) online platform to design and evaluate e-probes in raw High Throughput Sequencing (HTS) data. During this study, Dichorhavirus genomes were downloaded from public databases and e-probes were designed using the MiProbe incorporated into the MiFi^® platform. Three different sizes of e-probes, 40, 60, and 80 nucleotides, were developed and selected based on whole genome comparisons with near-neighbor genomes. For curation, each e-probe was searched in the NCBI nucleotide sequence database using BLASTn. All the e-probes that had hits with non-target species with ≥90% identities were removed. The sensitivity and specificity of Dichorhavirus genus, species, strain, and variant-specific e-probes were validated in vivo using HTS meta-transcriptomic libraries generated from Dichorhavirus-suspected citrus, orchid, and ornamentals. Through downstream analysis of HTS data, EDNA not only detected the known hosts of OFV but also discovered an unknown host leopard plant (Farfugium japonicum), and the possible existence of a new ornamental strain of OFV in nature.

Metagenomics analysis has enabled the measurement of the microbiome diversity in environmental samples without prior targeted enrichment. Functional and phylogenetic studies based on microbial diversity retrieved using HTS platforms have advanced from detecting known organisms and discovering unknown species to applications in disease diagnostics. Robust validation processes are essential for test reliability, requiring standard samples and databases deriving from real samples and in silico generated artificial controls. We propose a MeStanG as a resource for generating HTS Nanopore data sets to evaluate present and emerging bioinformatics pipelines. MeStanG allows samples to be designed with user-defined organism abundances expressed as number of reads, reference sequences, and predetermined or custom errors by sequencing profiles. The simulator pipeline was evaluated by analyzing its output mock metagenomic samples containing known read abundances using read mapping, genome assembly, and taxonomic classification on three scenarios: a bacterial community composed of nine different organisms, samples resembling pathogen-infected wheat plants, and a viral pathogen serial dilution sampling. The evaluation was able to report consistently the same organisms, and their read abundances as provided in the mock metagenomic sample design. Based on this performance and its novel capacity of generating exact number of reads, MeStanG can be used by scientists to develop mock metagenomic samples (artificial HTS data sets) to assess the diagnostic performance metrics of bioinformatic pipelines, allowing the user to choose predetermined or customized models for research and training.