Krisp: A Python package to aid in the design of CRISPR and amplification-based diagnostic assays from whole genome sequencing data.

Recent pandemics like COVID-19 highlighted the importance of rapidly developing diagnostics to detect evolving pathogens. CRISPR-Cas technology has recently been used to develop diagnostic assays for sequence-specific recognition of DNA or RNA. These assays have similar sensitivity to the gold standard qPCR but can be deployed as easy to use and inexpensive test strips. However, the discovery of diagnostic regions of a genome flanked by conserved regions where primers can be designed requires extensive bioinformatic analyses of genome sequences. We developed the Python package krisp to aid in the discovery of primers and diagnostic sequences that differentiate groups of samples from each other, using either unaligned genome sequences or a variant call format (VCF) file as input. Krisp has been optimized to handle large datasets by using efficient algorithms that run in near linear time, use minimal RAM, and leverage parallel processing when available. The validity of krisp results has been demonstrated in the laboratory with the successful design of a CRISPR diagnostic assay to distinguish the sudden oak death pathogen Phytophthora ramorum from closely related Phytophthora species. Krisp is released open source under a permissive license with all the documentation needed to quickly design CRISPR-Cas diagnostic assays.

Openness and Computational Reproducibility in Plant Pathology: Where We Stand and a Way Forward.

Open research practices have been highlighted extensively during the last 10 years in many fields of scientific study as essential standards needed to promote transparency and reproducibility of scientific results. Scientific claims can only be evaluated based on how protocols, materials, equipment, and methods were described; data were collected and prepared; and analyses were conducted. Openly sharing protocols, data, and computational code is central to current scholarly dissemination and communication, but in many fields, including plant pathology, adoption of these practices has been slow. We randomly selected 450 articles published from 2012 to 2021 across 21 journals representative of the plant pathology discipline and assigned them scores reflecting their openness and computational reproducibility. We found that most of the articles did not follow protocols for open science and failed to share data or code in a reproducible way. We propose that use of open-source tools facilitates computationally reproducible work and analyses, benefitting not just readers but the authors as well. Finally, we provide ideas and suggest tools to promote open, reproducible computational research practices among plant pathologists. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

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.

Genetic Differentiation and Clonal Expansion of Chinese Botrytis cinerea Populations from Tomato and Other Crops in China.

Botrytis cinerea is an important pathogen of vegetable and fruit crops but little is known about its population structure and genetics in China. We hypothesized that the geographic populations of B. cinerea in China would be genetically differentiated by host, geographic location, and/or year. In this study, we collected 393 B. cinerea isolates representing 28 populations from tomato, cherry, and nectarine from 2006 to 2014 in China. The isolates were analyzed using 14 microsatellite markers, including six new markers that provided more genotyping power than the eight previously published loci. We also investigated the B. cinerea population structure and inferred its mode of reproduction and dispersal based on genotype data. High genotypic diversity was detected in all populations, and clonal reproduction was dominant. Southern China populations harbored more genotypes than northern populations. Differentiation by host plant was evident. Between 2011 and 2012, genotypes changed only slightly among years for Liaoning populations, but they changed substantially among years for the Shanghai and Fujian populations. Clonal dispersal was detected and the farthest dispersal distance was estimated to be about 1,717 km. Two high-frequency genotypes were widely distributed in more than 10 populations and across several years. Our results provide useful, novel information for plant breeding programs and control of B. cinerea in China.

Soilborne Phytophthora and Pythium Diversity From Rhododendron in Propagation, Container, and Field Production Systems of the Pacific Northwest.

Rhododendron root rot is a severe disease that causes significant mortality in rhododendrons. Information is needed about the incidence and identity of soilborne Phytophthora and Pythium species causing root rot in Pacific Northwest nurseries in order to better understand the disease etiology and to optimize disease control strategies. The last survey focusing solely on soilborne oomycete pathogens in rhododendron production was conducted in 1974. Since then, advances in pathogen identification have occurred, new species may have been introduced, pathogen communities may have shifted, and little is known about Pythium species affecting this crop. Therefore, a survey of root-infecting Phytophthora and Pythium species was conducted at seven nurseries from 2013 to 2017 to (i) document the incidence of root rot damage at each nursery and stage of production, (ii) identify soilborne oomycetes infecting rhododendron, and (iii) determine whether there are differences in pathogen diversity among nurseries and production systems. Rhododendrons from propagation, container, and field systems were sampled and Phytophthora and Pythium species were isolated from the roots and collar region. Root rot was rarely evident in propagation systems, which were dominated by Pythium species. However, severe root rot was much more common in container and field systems where the genus Phytophthora was also more prevalent, suggesting that Phytophthora species are the primary cause of severe root rot and that most contamination by these pathogens comes in after the propagation stage. In total, 20 Pythium species and 11 Phytophthora species were identified. Pythium cryptoirregulare, Pythium aff. macrosporum, Phytophthora plurivora, and Phytophthora cinnamomi were the most frequently isolated species and the results showed that Phytophthora plurivora has become much more common than in the past. Phytophthora diversity was also greater in field systems than in propagation or container systems. Risks for Phytophthora contamination were commonly observed during the survey and included placement of potting media in direct contact with field soil, the presence of dead plants that could serve as continuous sources of inoculum, and the presence of excess water as a result of poor drainage, overirrigation, or malfunctioning irrigation equipment. In the past, research on disease development and root rot disease control in rhododendron focused almost exclusively on Phytophthora cinnamomi. More research is needed on both of these topics for the other root-infecting species identified in this survey.

Metacoder: An R package for visualization and manipulation of community taxonomic diversity data.

Community-level data, the type generated by an increasing number of metabarcoding studies, is often graphed as stacked bar charts or pie graphs that use color to represent taxa. These graph types do not convey the hierarchical structure of taxonomic classifications and are limited by the use of color for categories. As an alternative, we developed metacoder, an R package for easily parsing, manipulating, and graphing publication-ready plots of hierarchical data. Metacoder includes a dynamic and flexible function that can parse most text-based formats that contain taxonomic classifications, taxon names, taxon identifiers, or sequence identifiers. Metacoder can then subset, sample, and order this parsed data using a set of intuitive functions that take into account the hierarchical nature of the data. Finally, an extremely flexible plotting function enables quantitative representation of up to 4 arbitrary statistics simultaneously in a tree format by mapping statistics to the color and size of tree nodes and edges. Metacoder also allows exploration of barcode primer bias by integrating functions to run digital PCR. Although it has been designed for data from metabarcoding research, metacoder can easily be applied to any data that has a hierarchical component such as gene ontology or geographic location data. Our package complements currently available tools for community analysis and is provided open source with an extensive online user manual.

Taxa: An R package implementing data standards and methods for taxonomic data.

The taxa R package provides a set of tools for defining and manipulating taxonomic data. The recent and widespread application of DNA sequencing to community composition studies is making large data sets with taxonomic information commonplace. However, compared to typical tabular data, this information is encoded in many different ways and the hierarchical nature of taxonomic classifications makes it difficult to work with. There are many R packages that use taxonomic data to varying degrees but there is currently no cross-package standard for how this information is encoded and manipulated. We developed the R package taxa to provide a robust and flexible solution to storing and manipulating taxonomic data in R and any application-specific information associated with it. Taxa provides parsers that can read common sources of taxonomic information (taxon IDs, sequence IDs, taxon names, and classifications) from nearly any format while preserving associated data. Once parsed, the taxonomic data and any associated data can be manipulated using a cohesive set of functions modeled after the popular R package dplyr. These functions take into account the hierarchical nature of taxa and can modify the taxonomy or associated data in such a way that both are kept in sync. Taxa is currently being used by the metacoder and taxize packages, which provide broadly useful functionality that we hope will speed adoption by users and developers.