Defining syntenic relationships among orthologous gene clusters is a frequent undertaking of biologists studying organismal evolution through comparative genomic approaches. With the increasing availability of genome data made possible through next-generation sequencing technology, there is a growing need for user-friendly tools capable of assessing synteny. Here we present SimpleSynteny, a new web-based platform capable of directly interrogating collinearity of local genomic neighbors across multiple species in a targeted manner. SimpleSynteny provides a pipeline for evaluating the synteny of a preselected set of gene targets across multiple organismal genomes. An emphasis has been placed on ease-of-use, and users are only required to submit FASTA files for their genomes and genes of interest. SimpleSynteny then guides the user through an iterative process of exploring and customizing genomes individually before combining them into a final high-resolution figure. Because the process is iterative, it allows the user to customize the organization of multiple contigs and incorporate knowledge from additional sources, rather than forcing complete dependence on the computational predictions. Additional tools are provided to help the user identify which contigs in a genome assembly contain gene targets and to optimize analyses of circular genomes. SimpleSynteny is freely available at: http://www.SimpleSynteny.com.

High-throughput sequencing (HTS) technologies have become indispensable tools assisting plant virus diagnostics and research thanks to their ability to detect any plant virus in a sample without prior knowledge. As HTS technologies are heavily relying on bioinformatics analysis of the huge amount of generated sequences, it is of utmost importance that researchers can rely on efficient and reliable bioinformatic tools and can understand the principles, advantages, and disadvantages of the tools used. Here, we present a critical overview of the steps involved in HTS as employed for plant virus detection and virome characterization. We start from sample preparation and nucleic acid extraction as appropriate to the chosen HTS strategy, which is followed by basic data analysis requirements, an extensive overview of the in-depth data processing options, and taxonomic classification of viral sequences detected. By presenting the bioinformatic tools and a detailed overview of the consecutive steps that can be used to implement a well-structured HTS data analysis in an easy and accessible way, this paper is targeted at both beginners and expert scientists engaging in HTS plant virome projects.

Rapid and accurate detection of plant pathogens in the field is crucial to prevent the proliferation of infected crops. Polymerase chain reaction (PCR) process is the most reliable and accepted method for plant pathogen diagnosis, however current conventional PCR machines are not portable and require additional post-processing steps to detect the amplified DNA (amplicon) of pathogens. Real-time PCR can directly quantify the amplicon during the DNA amplification without the need for post processing, thus more suitable for field operations, however still takes time and require large instruments that are costly and not portable. Microchip PCR systems have emerged in the past decade to miniaturize conventional PCR systems and to reduce operation time and cost. Real-time microchip PCR systems have also emerged, but unfortunately all reported portable real-time microchip PCR systems require various auxiliary instruments. Here we present a stand-alone real-time microchip PCR system composed of a PCR reaction chamber microchip with integrated thin-film heater, a compact fluorescence detector to detect amplified DNA, a microcontroller to control the entire thermocycling operation with data acquisition capability, and a battery. The entire system is 25×16×8 cm³ in size and 843 g in weight. The disposable microchip requires only 8-µl sample volume and a single PCR run consumes 110 mAh of power. A DNA extraction protocol, notably without the use of liquid nitrogen, chemicals, and other large lab equipment, was developed for field operations. The developed real-time microchip PCR system and the DNA extraction protocol were used to successfully detect six different fungal and bacterial plant pathogens with 100% success rate to a detection limit of 5 ng/8 µl sample.

Early and accurate diagnosis of new plant pathogens is vital for the rapid implementation of effective mitigation strategies and appropriate regulatory responses. Most commonly, pathogen identification relies on morphology and DNA marker analysis. However, for new diseases, these approaches may not be sufficient for precise diagnosis. In this study, we used whole-genome sequencing (WGS) to identify the causal agent of a new disease affecting Sarcococca hookeriana (sarcococca). Blight symptoms were observed on sarcococca and adjacent Buxus sempervirens (boxwood) plants in Maryland during 2014. Symptoms on sarcococca were novel, and included twig dieback and dark lesions on leaves and stems. A Calonectria sp. was isolated from both hosts and used to fulfill Koch’s postulates but morphology and marker sequence data precluded species-level identification. A 51.4-Mb WGS was generated for the two isolates and identified both as Calonectria pseudonaviculata. A single-nucleotide polymorphism at a noncoding site differentiated between the two host isolates. These results indicate that the same C. pseudonaviculata genotype has the ability to induce disease on both plant species. This study marks the first application of WGS for fungal plant pathogen diagnosis and demonstrates the power of this approach to rapidly identify causal agents of new diseases.

The genomes of Chrysoporthe austroafricana, Diplodia scrobiculata, Fusarium nygami, Leptographium lundbergii, Limonomyces culmigenus, Stagonosporopsis tanaceti, and Thielaviopsis punctulata are presented in this genome announcement. These seven genomes are from endophytes, plant pathogens and economically important fungal species. The genome sizes range from 26.6 Mb in the case of Leptographium lundbergii to 44 Mb for Chrysoporthe austroafricana. The availability of these genome data will provide opportunities to resolve longstanding questions regarding the taxonomy of species in these genera, and may contribute to our understanding of the lifestyles through comparative studies with closely related organisms.

Rapid global germplasm trade has increased concern about the spread of plant pathogens and pests across borders that could become established, affecting agriculture and environment systems. Viral pathogens are of particular concern due to their difficulty to control once established. A comprehensive diagnostic platform that accurately detects both known and unknown virus species, as well as unreported variants, is playing a pivotal role across plant germplasm quarantine programs. Here we propose the addition of high-throughput sequencing (HTS) from total RNA to the routine quarantine diagnostic workflow of sugarcane viruses. We evaluated the impact of sequencing depth needed for the HTS-based identification of seven regulated sugarcane RNA/DNA viruses across two different growing seasons (spring and fall). Our HTS analysis revealed that viral normalized read counts (RPKM) was up to 23-times higher in spring than in the fall season for six out of the seven viruses. Random read subsampling analyses suggested that the minimum number of reads required for reliable detection of RNA viruses was 0.5 million, with a viral genome coverage of at least 92%. Using an HTS-based total RNA metagenomics approach, we identified all targeted viruses independent of the time of the year, highlighting that higher sequencing depth is needed for the identification of DNA viruses.