Biology subjects

Grunwald, N. J.

Publications and source records attributed to Grunwald, N. J..

7 recordsLinked to original sources

bioRxiv2026

Whole genome similarity provides a rapid, robust framework for classification of fungal taxa from the genus rank to intraspecies variants

Rapid and accurate microbial identification is critical for interpreting biological data in basic research and when making applied decisions on how to effectively treat patients and control human, animal, and plant diseases. Advancements in high-throughput sequencing have the potential to expedite fungal species identification and thus fungal biological research; however, analyses of ever-increasing numbers of genomes also present computational challenges. In this work, we evaluated how whole-genome similarity can serve as the basis for accurate classification and identification across the Kingdom Fungi and the Phylum Oomycota. Results from the computationally efficient k-mer-based tool sourmash are compared with those from more computationally demanding BLAST-based similarity method ANIb, as well as with conventional phylogenomic approaches, including maximum-likelihood concatenated ortholog trees and SNP-based methods. We observed that sourmash delivers orders-of-magnitude gains in speed and memory efficiency while maintaining strong concordance with phylogenomic methods. We found that a k-mer size of 21 is robust for genus and species determination, and that larger k-mers are well-suited for identification below the species rank. These results demonstrate that k-mer-based whole-genome similarity provides a scalable framework for fungal classification, enabling rapid analysis, lowering computational and bioinformatic barriers, and supporting the development of efficient identification pipelines.

bioinformatics↗

bioRxiv2025

PathogenSurveillance: an automated pipeline for population genomic analyses and pathogen identification

Whole genome sequencing (WGS) offers a comprehensive, organism-agnostic method that effectively meets the need for efficient, reliable, and standardized responses to emerging threats from pathogens and pests. Here, we present PathogenSurveillance, an open-source and automated Nextflow pipeline for population genomic analyses of WGS data. It is designed with features tailored for biosurveillance and is suitable for in-field or point-of-care diagnostics. PathogenSurveillance is flexible, accommodating short- and long-read datasets and mixed samples of prokaryotes and/or eukaryotes. It automates all steps, including reference identification and retrieval from the NCBI Assembly database, and produces customizable interactive reports with summaries, phylogenetic trees, and minimum spanning networks that enable species and subspecies level identification. It also outputs quality control metrics and carefully names and organizes output files to facilitate downstream analyses. The pipeline runs on any Linux-based system and minimizes the need for advanced computational expertise. Source code is available on GitHub under the open-source MIT license. The pipeline expands the toolkit for real-time biosurveillance, enabling rapid detection and monitoring of pathogens and pests for rapid response to novel variants.

bioinformatics↗

bioRxiv2023

Krisp: A python package for designing CRISPR and amplification-based diagnostic assays from whole genome data

Recent pandemics such as COVID-19 have highlighted the importance of rapidly developing diagnostics to detect and monitor evolving pathogens. CRISPR-Cas technology, combined with isothermal DNA amplification methods, 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 find primers and diagnostic sequences that differentiate groups of samples from each other at any taxonomic scale, 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 SHERLOCK assays 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. Author summaryPathogens continue to emerge at accelerated rates affecting animals, plants, and ecosystems. Rapid development of novel diagnostic tools is needed to monitor novel pathogen variants or groups. We developed the computational tool krisp to identify genetic regions suitable for development of CRISPR diagnostics and traditional amplification-based diagnostics such as PCR. Krisp scans whole genome sequence data for target and non-target groups to identify diagnostic regions based on DNA or RNA sequences. This computational tool has been validated using genome data for the sudden oak death pathogen Phytophthora ramorum. Krisp is released open source under a permissive license with all the documentation needed to quickly design CRISPR-Cas diagnostic assays and other amplification-based assays.

bioinformatics↗

bioRxiv2021

A new oomycete metabarcoding method using the rps10 gene

Oomycetes are a group of eukaryotes related to brown algae and diatoms, many of which cause diseases in plants and animals. Improved methods are needed for rapid and accurate characterization of oomycete communities using DNA metabarcoding. We have identified the mitochondrial 40S ribosomal protein S10 gene (rps10) as a locus useful for oomycete metabarcoding and provide primers predicted to amplify all oomycetes based on available reference sequences from a wide range of taxa. We evaluated its utility relative to a popular barcode, the internal transcribed spacer 1 (ITS1), by sequencing environmental samples and a mock community using Illumina MiSeq. Amplified sequence variants (ASVs) and operational taxonomic units (OTUs) were identified per community. Both the sequence and predicted taxonomy of ASVs and OTUs were compared to the known composition of the mock community. Both rps10 and ITS yielded ASVs with sequences matching 21 of the 24 species in the mock community and matching all 24 when allowing for a 1 bp difference. Taxonomic classifications of ASVs included 23 members of the mock community for rps10 and 17 for ITS1. Sequencing results for the environmental samples suggest the proposed rps10 locus results in substantially less amplification of non-target organisms than the ITS1 method. The amplified rps10 region also has higher taxonomic resolution than ITS1, allowing for greater discrimination of closely related species. We present a new website with a searchable rps10 reference database for species identification and all protocols needed for oomycete metabarcoding. The rps10 barcode and methods described herein provide an effective tool for metabarcoding oomycetes using short-read sequencing. Interpretive summaryOomycetes are a group of eukaryotes related to brown algae and diatoms, many of which cause diseases in plants and animals. Improved methods are needed to rapidly characterize the diversity of oomycete species found in environmental samples. We have identified the mitochondrial 40S ribosomal protein S10 gene (rps10) as being useful for oomycete community sequencing. We evaluated its utility relative to a popular barcode, the internal transcribed spacer 1 (ITS1), by sequencing environmental samples and a community we synthesized in the laboratory. The amplified rps10 region is predicted to have a higher taxonomic resolution than ITS1, allowing for greater discrimination of closely related species. We present a new website with a searchable rps10 reference database for species identification and all protocols needed for oomycete community sequencing. The rps10 barcode and methods described herein provide an effective tool for characterizing oomycetes using environmental DNA sequencing.

microbiology↗

bioRxiv2021

High quality, phased genomes of Phytophthora ramorum clonal lineages NA1 and EU1

Phytophthora ramorum is the causal agent of sudden oak death in West Coast forests and currently two clonal lineages, NA1 and EU1, cause epidemics in Oregon forests. Here, we report on two high-quality genomes of individuals belonging to the NA1 and EU1 clonal lineages respectively, using PacBio long-read sequencing. The NA1 strain Pr102, originally isolated from coast live oak in California, is the current reference genome and was previously sequenced independently using either Sanger (P. ramorum v1) or PacBio (P. ramorum v2) technology. The EU1 strain PR-15-019 was obtained from tanoak in Oregon. These new genomes have a total size of 57.5 Mb, with a contig N50 length of ~3.5-3.6 Mb and encode ~15,300 predicted protein-coding genes. Genomes were assembled into 27 and 28 scaffolds with 95% BUSCO scores and are considerably improved relative to the current JGI reference genome with 2,575 or the PacBio genomes with 1,512 scaffolds. These high-quality genomes provide a valuable resource for studying the genetics, evolution, and adaptation of these two clonal lineages.

plant biology↗

bioRxiv2019

Genomic investigation of the strawberry pathogen Phytophthora fragariae indicates pathogenicity is determined by transcriptional variation in three key races

The oomycete Phytophthora fragariae is a highly destructive pathogen of cultivated strawberry (Fragaria x ananassa), causing the root rotting disease, red core. The host-pathogen interaction has a well described gene-for-gene resistance relationship, but to date neither candidate avirulence nor resistance genes have been identified. We sequenced a set of American, Canadian and UK isolates of known race type, along with three representatives of the closely related pathogen of the raspberry (Rubus idaeus), Phytophthora rubi, and found a clear population structure, with a high degree of nucleotide divergence seen between some race types and abundant private variation associated with race types 4 and 5. In contrast, between isolates defined as UK races 1, 2 & 3 (UK1-2-3) there was no evidence of gene loss or gain; or the presence of insertions/deletions (INDELs) or Single Nucleotide Polymorphisms (SNPs) within or in proximity to putative pathogenicity genes could be found associated with race variation. Transcriptomic analysis of representative UK1-2-3 isolates revealed abundant expression variation in key effector family genes associated with pathogen race; however, further long read sequencing did not reveal any long range polymorphisms to be associated with avirulence to race UK2 or UK3 resistance, suggesting either control in trans or other stable forms of epigenetic modification modulating gene expression. This work reveals the combined power of population resequencing to uncover race structure in pathosystems and in planta transcriptomic analysis to identify candidate avirulence genes. This work has implications for the identification of putative avirulence genes in the absence of associated expression data and points towards the need for detailed molecular characterisation of mechanisms of effector regulation and silencing in oomycete plant pathogens.

genomics↗

bioRxiv2018

effectR: An expandable R package to predict candidate effectors

Effectors are by one definition small, secreted proteins that facilitate infection of host plants by all major groups of plant pathogens. Effector protein identification in oomycetes relies on identification of open reading frames with certain amino acid motifs among additional minor criteria. To date, identification of effectors relies on custom scripts to identify motifs in candidate open reading frames. Here, we developed the R package effectR that provides a convenient tool for rapid prediction of effectors in oomycete genomes, or with custom scripts for any genome, in a reproducible way. The effectR package relies on a combination of regular expressions statements and hidden Markov model approaches to predict candidate RxLR and CRN effectors. Other custom motifs for novel effectors can easily be implemented and added to package updates. The effectR package has been validated with published oomycete genomes. This package provides a convenient tool for reproducible identification of candidate effectors in oomycete genomes.

bioinformatics↗