Correction: Haegeman et al. Looking beyond Virus Detection in RNA Sequencing Data: Lessons Learned from a Community-Based Effort to Detect Cellular Plant Pathogens and Pests. Plants 2023, 12, 2139.

In the original publication [...].

PhytoPipe: a phytosanitary pipeline for plant pathogen detection and diagnosis using RNA-seq data.

BACKGROUND: Detection of exotic plant pathogens and preventing their entry and establishment are critical for the protection of agricultural systems while securing the global trading of agricultural commodities. High-throughput sequencing (HTS) has been applied successfully for plant pathogen discovery, leading to its current application in routine pathogen detection. However, the analysis of massive amounts of HTS data has become one of the major challenges for the use of HTS more broadly as a rapid diagnostics tool. Several bioinformatics pipelines have been developed to handle HTS data with a focus on plant virus and viroid detection. However, there is a need for an integrative tool that can simultaneously detect a wider range of other plant pathogens in HTS data, such as bacteria (including phytoplasmas), fungi, and oomycetes, and this tool should also be capable of generating a comprehensive report on the phytosanitary status of the diagnosed specimen. RESULTS: We have developed an open-source bioinformatics pipeline called PhytoPipe (Phytosanitary Pipeline) to provide the plant pathology diagnostician community with a user-friendly tool that integrates analysis and visualization of HTS RNA-seq data. PhytoPipe includes quality control of reads, read classification, assembly-based annotation, and reference-based mapping. The final product of the analysis is a comprehensive report for easy interpretation of not only viruses and viroids but also bacteria (including phytoplasma), fungi, and oomycetes. PhytoPipe is implemented in Snakemake workflow with Python 3 and bash scripts in a Linux environment. The source code for PhytoPipe is freely available and distributed under a BSD-3 license. CONCLUSIONS: PhytoPipe provides an integrative bioinformatics pipeline that can be used for the analysis of HTS RNA-seq data. PhytoPipe is easily installed on a Linux or Mac system and can be conveniently used with a Docker image, which includes all dependent packages and software related to analyses. It is publicly available on GitHub at https://github.com/healthyPlant/PhytoPipe and on Docker Hub at https://hub.docker.com/r/healthyplant/phytopipe .

Looking beyond Virus Detection in RNA Sequencing Data: Lessons Learned from a Community-Based Effort to Detect Cellular Plant Pathogens and Pests.

High-throughput sequencing (HTS), more specifically RNA sequencing of plant tissues, has become an indispensable tool for plant virologists to detect and identify plant viruses. During the data analysis step, plant virologists typically compare the obtained sequences to reference virus databases. In this way, they are neglecting sequences without homologies to viruses, which usually represent the majority of sequencing reads. We hypothesized that traces of other pathogens might be detected in this unused sequence data. In the present study, our goal was to investigate whether total RNA-seq data, as generated for plant virus detection, is also suitable for the detection of other plant pathogens and pests. As proof of concept, we first analyzed RNA-seq datasets of plant materials with confirmed infections by cellular pathogens in order to check whether these non-viral pathogens could be easily detected in the data. Next, we set up a community effort to re-analyze existing Illumina RNA-seq datasets used for virus detection to check for the potential presence of non-viral pathogens or pests. In total, 101 datasets from 15 participants derived from 51 different plant species were re-analyzed, of which 37 were selected for subsequent in-depth analyses. In 29 of the 37 selected samples (78%), we found convincing traces of non-viral plant pathogens or pests. The organisms most frequently detected in this way were fungi (15/37 datasets), followed by insects (13/37) and mites (9/37). The presence of some of the detected pathogens was confirmed by independent (q)PCRs analyses. After communicating the results, 6 out of the 15 participants indicated that they were unaware of the possible presence of these pathogens in their sample(s). All participants indicated that they would broaden the scope of their bioinformatic analyses in future studies and thus check for the presence of non-viral pathogens. In conclusion, we show that it is possible to detect non-viral pathogens or pests from total RNA-seq datasets, in this case primarily fungi, insects, and mites. With this study, we hope to raise awareness among plant virologists that their data might be useful for fellow plant pathologists in other disciplines (mycology, entomology, bacteriology) as well.

Genetic Diversity, Evolution, and Diagnosis of Sugarcane Yellow Leaf Virus from 19 Sugarcane-Producing Locations Worldwide.

Sugarcane yellow leaf virus (SCYLV), the causal agent of yellow leaf, has been reported in an increasing number of sugarcane-growing locations since its first report in the 1990s in Brazil, Florida, and Hawaii. In this study, the genetic diversity of SCYLV was investigated using the genome coding sequence (5,561 to 5,612 nt) of 109 virus isolates from 19 geographical locations, including 65 new isolates from 16 geographical regions worldwide. These isolates were distributed in three major phylogenetic lineages (BRA, CUB, and REU), except for one isolate from Guatemala. Twenty-two recombination events were identified among the 109 isolates of SCYLV, thus confirming that recombination was a significant driving force in the genetic diversity and evolution of this virus. No temporal signal was found in the genomic sequence dataset, most likely because of the short temporal window of the 109 SCYLV isolates (1998 to 2020). Among 27 primers reported in the literature for the detection of the virus by RT-PCR, none matched 100% with all 109 SCYLV sequences, suggesting that the use of some primer pairs may not result in the detection of all virus isolates. Primers YLS111/YLS462, which were the first primer pair used by numerous research organizations to detect the virus by RT-PCR, failed to detect isolates belonging to the CUB lineage. In contrast, primer pair ScYLVf1/ScYLVr1 efficiently detected isolates of all three lineages. Continuous pursuit of knowledge of SCYLV genetic variability is therefore critical for effective diagnosis of yellow leaf, especially in virus-infected and mainly asymptomatic sugarcane plants.