The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Field pathogenomics reveals the emergence of a diverse wheat yellow rust population
Open accessAbstract
Background: Emerging and re-emerging pathogens imperil public health and global food security. Responding to these threats requires improved surveillance and diagnostic systems. Despite their potential, genomic tools have not been readily applied to emerging or re-emerging plant pathogens such as the wheat yellow (stripe) rust pathogen Puccinia striiformis f. sp. tritici (PST). This is due largely to the obligate parasitic nature of PST, as culturing PST isolates for DNA extraction remains slow and tedious. Results: To counteract the limitations associated with culturing PST, we developed and applied a field pathogenomics approach by transcriptome sequencing infected wheat leaves collected from the field in 2013. This enabled us to rapidly gain insights into this emerging pathogen population. We found that the PST population across the United Kingdom (UK) underwent a major shift in recent years. Population genetic structure analyses revealed four distinct lineages that correlated to the phenotypic groups determined through traditional pathology-based virulence assays. Furthermore, the genetic diversity between members of a single population cluster for all 2013 PST field samples was much higher than that displayed by historical UK isolates, revealing a more diverse population of PST. Conclusions: Our field pathogenomics approach uncovered a dramatic shift in the PST population in the UK, likely due to a recent introduction of a diverse set of exotic PST lineages. The methodology described herein accelerates genetic analysis of pathogen populations and circumvents the difficulties associated with obligate plant pathogens. In principle, this strategy can be widely applied to a variety of plant pathogens.
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R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0
Tamura, K., Stecher, G., Peterson, D., Filipski, A., & Kumar, S. (2013) · Molecular Biology and Evolution · doi:10.1093/molbev/mst197 · Cited by 47,205
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
Inference of Population Structure Using Multilocus Genotype Data
Pritchard, J. K., Stephens, M., & Donnelly, P. (2000) · Genetics · doi:10.1093/genetics/155.2.945 · Cited by 33,908
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
DnaSP v5: a software for comprehensive analysis of DNA polymorphism data
Librado, P., & Rozas, J. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp187 · Cited by 16,017
RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models
Stamatakis, A. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl446 · Cited by 15,582
Metadata
- Authors
- Hubbard, A., Lewis, C. M., Yoshida, K., Ramirez-Gonzalez, R. H., de Vallavieille-Pope, C., Thomas, J., Kamoun, S., Bayles, R., Uauy, C., & Saunders, D. G.
- Venue
- Genome Biology
- Published
- 2015-02-25
- Volume
- 16
- Issue
- 1
- Pages
- 23-23
- ISSN
- 1465-6906 (ISSN-L)
- OpenAlex
- W2085123256
- Cited by
- 263
- References
- 47
- FWCI
- 20.37
- Type
- journal article
- Language
- en
- Open access
- gold
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- Hubbard, A., Lewis, C. M., Yoshida, K., Ramirez-Gonzalez, R. H., de Vallavieille-Pope, C., Thomas, J., Kamoun, S., Bayles, R., Uauy, C., & Saunders, D. G. (2015). Field pathogenomics reveals the emergence of a diverse wheat yellow rust population. Genome Biology. https://doi.org/10.1186/s13059-015-0590-8
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