Abstract
Plant-pathogenic fungi cause crop disease via a range of secreted effector proteins that interact with specific receptors of host plant cells. Effector identification can enable the diagnosis of disease outcomes and enable selection or breeding of disease-resistant crop cultivars. Bioinformatic methods have been developed to predict proteins with ‘effector-like’ properties, but the resulting number of candidates tends to be larger than can be feasibly validated and may contain numerous false positives. Challenges to effector discovery include the obfuscating effects of genome-wide mutations common to Fungi, such as Repeat-Induced Point (RIP) mutations. Refining effector predictions by incorporating disease phenotyping into genome-wide association studies (GWAS) have had mixed success for a handful of pathogen species. But the utility of GWAS approaches may be limited by low ‘signal-to-noise’ caused by widespread RIP-like SNP mutations across the genomes of most fungal pathogens. This study presents an alternative method for effector candidate refinement called ‘EffectorFisher’. EffectorFisher extends the output of Predector – a tool that automates and combines results of several bioinformatic tools commonly used in effector discovery – to apply pangenome-derived protein-isoform profiling to remove candidate effector protein isoforms with weak association with virulent phenotypes. This method was benchmarked using corresponding pangenome and phenotype data for two model wheat pathogens, each with multiple known effectors: the necrotroph Parastagonospora nodorum and the hemibiotroph Zymoseptoria tritici. Compared to prior methods based on effector-like protein properties, EffectorFisher improved predicted rankings of known effectors and reduced the total number of effector candidates. We present EffectorFisher (https://github.com/ccdmb/EffectorFisher-core) as a useful tool for refining effector predictions with phenotype data, which is broadly applicable to many fungal pathogen species, and is capable of predicting effectors involved in both gene-for-gene and inverse gene-for-gene effector-receptor interactions.
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Data availability
EffectorFisher code is available from [https://github.com/ccdmb/EffectorFisher-core](https:/github.com/ccdmb/EffectorFisher-core). Genome sequence data for Parastagonospora nodorum was obtained for: (a) reference isolates SN15 [NCBI BioProject: PRJNA686477], LDN03-Sn4 (Sn4), Sn2000, and Sn79-1087 39 [NCBI BioProject: PRJNA398070], (b) 14 isolates from multiple regions (Western Australia, Iran, Europe and the United States) [NCBI BioProject: PRJNA686477, PRJNA398070, PRJNA476481, PRJNA170816, PRJNA170815], and (c) 136 isolates from Western Australia [NCBI BioProject: PRJNA1130627]. Genome sequence data for Zymoseptoria tritici was obtained for (a) 132 isolates collected across multiple regions (Australia, Israel, Switzerland, United States) [NCBI BioProject: PRJNA890236, PRJNA327615], and (b) reference isolate IPO323 [NCBI BioProject: PRJNA19047]. Disease phenotype datasets used in this study were sourced from (a) a prior study of P. nodorum ([https://doi.org:10.3389/fpls.2019.01785] (https:/doi.org:10.3389/fpls.2019.01785)) (b) a prior study of Z. tritici by ([https://doi.org: https://doi.org/10.1111/eva.13117] (https:/doi.org: https:/doi.org/10.1111/eva.13117)) (available from: [https://datadryad.org/dataset/doi:10.5061/dryad.j3tx95 × 9 m] (https:/datadryad.org/dataset/doi:10.5061/dryad.j3tx95 × 9 m)).
References
Dean, R. et al. The Top 10 fungal pathogens in molecular plant pathology. Mol. Plant Pathol. 13, 414–430. https://doi.org/10.1111/j.1364-3703.2011.00783.x (2012).
Fisher, M. C. et al. Emerging fungal threats to animal, plant and ecosystem health. Nature 484, 186–194. https://doi.org/10.1038/nature10947 (2012).
Jones, D. A. B., Moolhuijzen, P. M. & Hane, J. K. Remote homology clustering identifies lowly conserved families of effector proteins in plant-pathogenic fungi. Microb. Genomics. 7 https://doi.org/10.1099/mgen.0.000637 (2021).
Jones, D. A. B. et al. An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens. Sci. Rep. 11, 19731. https://doi.org/10.1038/s41598-021-99363-0 (2021).
Kanja, C. & Hammond-Kosack, K. E. Proteinaceous effector discovery and characterization in filamentous plant pathogens. Mol. Plant Pathol. 21, 1353–1376. https://doi.org/10.1111/mpp.12980 (2020). https://doi.org:
Li, G. et al. Fungal effectors: past, present, and future. Curr. Opin. Microbiol. 81, 102526. https://doi.org/10.1016/j.mib.2024.102526 (2024). https://doi.org:.
Jones, J. D. G. & Dangl, J. L. The plant immune system. Nature 444, 323–329. https://doi.org/10.1038/nature05286 (2006).
Fenton, A., Antonovics, J. & Brockhurst, M. A. Inverse-Gene‐for‐Gene Infection Genetics and Coevolutionary Dynamics. Am. Nat. 174, E230–E242. https://doi.org/10.1086/645087 (2009).
Jones, D. A. B., Bertazzoni, S., Turo, C. J., Syme, R. A. & Hane, J. K. Bioinformatic prediction of plant–pathogenicity effector proteins of fungi. Curr. Opin. Microbiol. 46, 43–49. https://doi.org/10.1016/j.mib.2018.01.017 (2018).
McHale, L., Tan, X., Koehl, P. & Michelmore, R. W. Plant NBS-LRR proteins: Adaptable guards. Genome Biol. 7, 212. https://doi.org/10.1186/gb-2006-7-4-212 (2006).
Friesen, T. L., Meinhardt, S. W. & Faris, J. D. The Stagonospora nodorum-wheat pathosystem involves multiple proteinaceous host-selective toxins and corresponding host sensitivity genes that interact in an inverse gene-for-gene manner. Plant J. 51, 681–692. https://doi.org/10.1111/j.1365-313X.2007.03166.x (2007).
Sperschneider, J. & Dodds, P. N. EffectorP 3.0: Prediction of apoplastic and cytoplasmic effectors in fungi and oomycetes. Mol. Plant-Microbe Interact. 35, 146–156. https://doi.org/10.1094/mpmi-08-21-0201-r (2022).
Kale, S. D. et al. External lipid PI3P mediates entry of eukaryotic pathogen effectors into plant and animal host cells. Cell 142, 284–295. https://doi.org/10.1016/j.cell.2010.06.008 (2010).
Jiang, R. H. Y. & Tyler, B. M. Mechanisms and evolution of virulence in oomycetes. Annu. Rev. Phytopathol. 50, 295–318. https://doi.org/10.1146/annurev-phyto-081211-172912 (2012).
Leonelli, L. et al. Structural Elucidation and Functional Characterization of the Hyaloperonospora arabidopsidis Effector Protein ATR13. PLoS Pathog. 7, e1002428. https://doi.org/10.1371/journal.ppat.1002428 (2011).
Stassen, J. H. M. et al. Effector identification in the lettuce downy mildew Bremia lactucae by massively parallel transcriptome sequencing. Mol. Plant Pathol. 13, 719–731. https://doi.org/10.1111/j.1364-3703.2011.00780.x (2012).
Lévesque, C. A. et al. Genome sequence of the necrotrophic plant pathogen Pythium ultimum reveals original pathogenicity mechanisms and effector repertoire. Genome Biol. 11, 1–22 (2010).
Jiang, R. H., Tripathy, S., Govers, F. & Tyler, B. M. RXLR effector reservoir in two Phytophthora species is dominated by a single rapidly evolving superfamily with more than 700 members. Proc. Natl. Acad. Sci. U. S. A. 105, 4874–4879 (2008).
Urban, M. et al. PHI-base – The multi-species pathogen–host interaction database in 2025. Nucleic Acids Res. 53, D826–D838. https://doi.org/10.1093/nar/gkae1084 (2024).
Lu, T., Yao, B. & Zhang, C. DFVF: Database of fungal virulence factors. Database (Oxford) 2012, bas032. https://doi.org/10.1093/database/bas032 (2012).
Blum, M. et al. InterPro: The protein sequence classification resource in 2025. Nucleic Acids Res. 53, D444–D456. https://doi.org/10.1093/nar/gkae1082 (2024).
Testa, A. C., Oliver, R. P. & Hane, J. K. OcculterCut: A comprehensive survey of AT-rich regions in fungal genomes. Genome Biol. Evol. 8, 2044–2064. https://doi.org/10.1093/gbe/evw121 (2016).
Jones, D. A. B. et al. Repeat-induced point mutations driving Parastagonospora nodorum genomic diversity are balanced by selection against non-synonymous mutations. Commun. Biol. 7, 1614. https://doi.org/10.1038/s42003-024-07327-7 (2024).
Grandaubert, J. et al. Transposable element-assisted evolution and adaptation to host plant within the Leptosphaeria maculans-Leptosphaeria biglobosa species complex of fungal pathogens. BMC Genomics 15, 1–27 (2014).
Shiller, J. et al. A large family of AvrLm6-like genes in the apple and pear scab pathogens, Venturia inaequalis and Venturia pirina. Front. Plant Sci. 6, 980 (2015).
Rozano, L., Mukuka, Y. M., Hane, J. K. & Mancera, R. L. Ab initio modelling of the structure of ToxA-like and MAX fungal effector proteins. Int. J. Mol. Sci. 24, 6262 (2023).
Rozano, L., Jones, D. A. B., Hane, J. K. & Mancera, R. L. Template-based modelling of the structure of fungal effector proteins. Mol. Biotechnol. 66, 784–813. https://doi.org/10.1007/s12033-023-00703-4 (2024).
de Guillen, K. et al. Structure analysis uncovers a highly diverse but structurally conserved effector family in phytopathogenic fungi. PLoS. Pathog. 11, e1005228 (2015).
Lu, S., Turgeon, B. G. & Edwards, M. C. A ToxA-like protein from Cochliobolus heterostrophus induces light-dependent leaf necrosis and acts as a virulence factor with host selectivity on maize. Fungal Genet. Biol. 81, 12–24 (2015).
Teufel, F. et al. SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat. Biotechnol. 40, 1023–1025 (2022).
Sperschneider, J. & Dodds, P. N. EffectorP 3.0: Prediction of apoplastic and cytoplasmic effectors in fungi and oomycetes. Mol. Plant Microbe Interact. 35, 146–156 (2022).
Sperschneider, J., Dodds, P. N., Singh, K. B. & Taylor, J. M. ApoplastP: Prediction of effectors and plant proteins in the apoplast using machine learning. New Phytol. 217, 1764–1778 (2018).
Kristianingsih, R. & MacLean, D. Accurate plant pathogen effector protein classification ab initio with deepredeff: An ensemble of convolutional neural networks. BMC. Bioinformatics 22, 372 (2021).
Urban, M. et al. PHI-base–The multi-species pathogen–host interaction database in 2025. Nucleic Acids Res. 53, D826–D838 (2025).
Paysan-Lafosse, T. et al. InterPro in 2022. Nucleic Acids Res. 51, D418–D427 (2023).
Jones, D. A. et al. An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens. Sci. Rep. 11, 19731 (2021).
Jones, D. A. B. & Raffaele, S. Structural and Transcriptional Diversity in the Repertoire of Sclerotinia sclerotiorum Effector Candidates. Mol. Plant. Microbe Interact. Mpmi08250101r https://doi.org/10.1094/mpmi-08-25-0101-r (2026).
Uffelmann, E. et al. Genome-wide association studies. Nat. Rev. Methods Primers. 1, 59. https://doi.org/10.1038/s43586-021-00056-9 (2021).
Richards, J. K. et al. Local adaptation drives the diversification of effectors in the fungal wheat pathogen Parastagonospora nodorum in the United States. PLoS Genet. 15, e1008223. https://doi.org/10.1371/journal.pgen.1008223 (2019).
Kariyawasam, G. K. et al. The Parastagonospora nodorum necrotrophic effector SnTox5 targets the wheat gene Snn5 and facilitates entry into the leaf mesophyll. New Phytol. 233, 409–426. https://doi.org/10.1111/nph.17602 (2022).
Zhong, Z. et al. A small secreted protein in Zymoseptoria tritici is responsible for avirulence on wheat cultivars carrying the Stb6 resistance gene. New Phytol. 214, 619–631 (2017).
Amezrou, R. et al. Quantitative pathogenicity and host adaptation in a fungal plant pathogen revealed by whole-genome sequencing. Nat. Commun. 151933. https://doi.org/10.1038/s41467-024-46191-1 (2024).
Singh, N. K., Tralamazza, S. M., Abraham, L. N., Glauser, G. & Croll, D. Genome-wide association mapping reveals genes underlying population-level metabolome diversity in a fungal crop pathogen. BMC Biol. 20, 224. https://doi.org/10.1186/s12915-022-01422-z (2022).
Dutta, A., McDonald, B. A. & Croll, D. Combined reference-free and multi-reference based GWAS uncover cryptic variation underlying rapid adaptation in a fungal plant pathogen. PLoS Pathog. 19, e1011801. https://doi.org/10.1371/journal.ppat.1011801 (2023).
Chen, C. et al. GWAS reveals a rapidly evolving candidate avirulence effector in the Cercospora leaf spot pathogen. Mol. Plant Pathol. 25, e13407. https://doi.org/10.1111/mpp.13407 (2024). https://doi.org:
Hartmann, F. E. et al. Recombination suppression and evolutionary strata around mating-type loci in fungi: documenting patterns and understanding evolutionary and mechanistic causes. New. Phytol. 229, 2470–2491. https://doi.org/10.1111/nph.17039 (2021).
Menat, J., Armstrong-Cho, C. & Banniza, S. Lack of evidence for sexual reproduction in field populations of Colletotrichum lentis. Fungal Ecol. 20, 66–74. https://doi.org/10.1016/j.funeco.2015.11.001 (2016).
Hane, J. K., Williams, A. H., Taranto, A. P., Solomon, P. S. & Oliver, R. P. Repeat-induced point mutation: a fungal-specific, endogenous mutagenesis process, in Genetic Transformation Systems in Fungi 2:55–68 Springer, (2014).
Jones, D. A. et al. Repeat-induced point mutations driving Parastagonospora nodorum genomic diversity are balanced by selection against non-synonymous mutations. Commun. Biology. 7, 1614 (2024).
Tan, K. C. et al. Quantitative variation in effector activity of ToxA isoforms from Stagonospora nodorum and Pyrenophora tritici-repentis. Mol. Plant. Microbe Interact. 25, 515–522. https://doi.org/10.1094/mpmi-10-11-0273 (2012).
Brandes, N., Linial, N. & Linial, M. PWAS: proteome-wide association study—linking genes and phenotypes by functional variation in proteins. Genome Biol. 21, 173. https://doi.org/10.1186/s13059-020-02089-x (2020).
Zhao, T. et al. A proteome-wide association study identifies putative causal proteins for breast cancer risk. Br. J. Cancer. 131, 1796–1804. https://doi.org/10.1038/s41416-024-02879-1 (2024).
Liu, J., Li, X. & Luo, X. J. Proteome-wide Association Study Provides Insights Into the Genetic Component of Protein Abundance in Psychiatric Disorders. Biol. Psychiatry. 90, 781–789. https://doi.org/10.1016/j.biopsych.2021.06.022 (2021).
Cheng, B. et al. Integrated analysis of proteome-wide and transcriptome-wide association studies identified novel genes and chemicals for vertigo. Brain Commun. https://doi.org/10.1093/braincomms/fcac313 (2022).
Goodwin, S. B. et al. Finished genome of the fungal wheat pathogen Mycosphaerella graminicola reveals dispensome structure, chromosome plasticity, and stealth pathogenesis. PLoS Genet. 7, e1002070 (2011).
Van de Wouw, A. P. et al. Evolution of linked avirulence effectors in Leptosphaeria maculans is affected by genomic environment and exposure to resistance genes in host plants. PLoS Pathog. 6, e1001180 (2010).
Bertazzoni, S., Jones, D. A. B., Phan, H. T., Tan, K.-C. & Hane, J. K. Chromosome-level genome assembly and manually-curated proteome of model necrotroph Parastagonospora nodorum Sn15 reveals a genome-wide trove of candidate effector homologs, and redundancy of virulence-related functions within an accessory chromosome. BMC Genomics 22, 382. https://doi.org/10.1186/s12864-021-07699-8 (2021).
Dagvadorj, B., Outram, M. A., Williams, S. J. & Solomon, P. S. The necrotrophic effector ToxA from Parastagonospora nodorum interacts with wheat NHL proteins to facilitate Tsn1-mediated necrosis. Plant J. 110, 407–418. https://doi.org/10.1111/tpj.15677 (2022).
Liu, Z. et al. The Tsn1–ToxA interaction in the wheat–Stagonospora nodorum pathosystem parallels that of the wheat–tan spot system. Genome 49, 1265–1273 (2006).
Liu, Z. et al. The cysteine rich necrotrophic effector SnTox1 produced by Stagonospora nodorum triggers susceptibility of wheat lines harboring Snn1. PLoS Pathog. 8, e1002467 (2012).
Liu, Z. et al. SnTox3 acts in effector triggered susceptibility to induce disease on wheat carrying the Snn3 gene. PLoS Pathog. 5, e1000581 (2009).
Richards, J. K. et al. A triple threat: The Parastagonospora nodorum SnTox267 effector exploits three distinct host genetic factors to cause disease in wheat. New Phytol. 233, 427–442. https://doi.org/10.1111/nph.17601 (2022).
Jones, D. A. B. et al. A specific fungal transcription factor controls effector gene expression and orchestrates the establishment of the necrotrophic pathogen lifestyle on wheat. Sci. Rep. 9, 15884. https://doi.org/10.1038/s41598-019-52444-7 (2019).
Fradgley, N. et al. A large-scale pedigree resource of wheat reveals evidence for adaptation and selection by breeders. PLoS Biol. 17, e3000071. https://doi.org/10.1371/journal.pbio.3000071 (2019).
Aboukhaddour, R. et al. A revised nomenclature for ToxA haplotypes across multiple fungal species. Phytopathology 113, 1180–1184. https://doi.org/10.1094/phyto-01-23-0017-sc (2023).
Alassimone, J. et al. The Zymoseptoria tritici Avirulence factor AvrStb6 accumulates in Hyphae Close to Stomata and triggers a wheat defense response hindering fungal penetration. Mol. Plant Microbe Interact. 37, 432–444 (2024).
Stephens, C. et al. Remarkable recent changes in the genetic diversity of the avirulence gene AvrStb6 in global populations of the wheat pathogen Zymoseptoria tritici. Mol. Plant Pathol. 22, 1121–1133. https://doi.org/10.1111/mpp.13101 (2021).
Dutta, A., Croll, D., McDonald, B. A. & Barrett, L. G. Maintenance of variation in virulence and reproduction in populations of an agricultural plant pathogen. Evol. Appl. 14, 335–347. https://doi.org/10.1111/eva.13117 (2021).
Amezrou, R. et al. A secreted protease-like protein in Zymoseptoria tritici is responsible for avirulence on Stb9 resistance gene in wheat. PLoS Pathog. 19, e1011376. https://doi.org/10.1371/journal.ppat.1011376 (2023).
Meile, L. et al. A fungal avirulence factor encoded in a highly plastic genomic region triggers partial resistance to Septoria tritici blotch. New Phytol. 219, 1048–1061 (2018).
Karki, S. J. et al. A small secreted protein from Zymoseptoria tritici interacts with a wheat E3 ubiquitin ligase to promote disease. J. Exp. Bot. 72, 733–746. https://doi.org/10.1093/jxb/eraa489 (2020).
Ben M’Barek, S. et al. FPLC and liquid-chromatography mass spectrometry identify candidate necrosis-inducing proteins from culture filtrates of the fungal wheat pathogen Zymoseptoria tritici. Fungal Genet. Biol. 79, 54–62. https://doi.org/10.1016/j.fgb.2015.03.015 (2015).
Kettles, G. J., Bayon, C., Canning, G., Rudd, J. J. & Kanyuka, K. Apoplastic recognition of multiple candidate effectors from the wheat pathogen Zymoseptoria tritici in the nonhost plant Nicotiana benthamiana. New Phytol. 213, 338–350. https://doi.org/10.1111/nph.14215 (2017).
Thynne, E. et al. An array of Zymoseptoria tritici effectors suppress plant immune responses. Mol. Plant Pathol. 25, e13500. https://doi.org/10.1111/mpp.13500 (2024).
Kettles, G. J. et al. Characterization of an antimicrobial and phytotoxic ribonuclease secreted by the fungal wheat pathogen Zymoseptoria tritici. New Phytol. 217, 320–331. https://doi.org/10.1111/nph.14786 (2018).
Karki, S. J. et al. The Zymoseptoria tritici effector Zt-11 contributes to aggressiveness in wheat. PLoS One 19, e0313859. https://doi.org/10.1371/journal.pone.0313859 (2024).
Tian, H. et al. Three LysM effectors of Zymoseptoria tritici collectively disarm chitin-triggered plant immunity. Mol. Plant Pathol. 22, 683–693. https://doi.org/10.1111/mpp.13055 (2021).
Jones, D. A. et al. A specific fungal transcription factor controls effector gene expression and orchestrates the establishment of the necrotrophic pathogen lifestyle on wheat. Sci. Rep. 9, 15884 (2019).
Bringans, S. et al. Deep proteogenomics; high throughput gene validation by multidimensional liquid chromatography and mass spectrometry of proteins from the fungal wheat pathogen Stagonospora nodorum. BMC Bioinformatics 10, 301 (2009).
Jones, D. A., Moolhuijzen, P. M. & Hane, J. K. Remote homology clustering identifies lowly conserved families of effector proteins in plant-pathogenic fungi. Microb. Genomics 7, 000637 (2021).
Bertazzoni, S. et al. Accessories make the outfit: Accessory chromosomes and other dispensable DNA regions in plant-pathogenic fungi. Mol. Plant Microbe Interact. 31, 779–788 (2018).
Hane, J. K. et al. A novel mode of chromosomal evolution peculiar to filamentous Ascomycete fungi. Genome Biol. 12, 1–16 (2011).
Syme, R. A., Hane, J. K., Friesen, T. L. & Oliver, R. P. Resequencing and comparative genomics of Stagonospora nodorum: Sectional gene absence and effector discovery. G3 Genes|Genomes|Genetics 3, 959–969. https://doi.org/10.1534/g3.112.004994 (2013).
Syme, R. A. et al. Pan-Parastagonospora comparative genome analysis—effector prediction and genome evolution. Genome Biol. Evol. 10, 2443–2457 (2018).
Chen, H. et al. Combined pangenomics and transcriptomics reveals core and redundant virulence processes in a rapidly evolving fungal plant pathogen. BMC Biol. 21, 24. https://doi.org/10.1186/s12915-023-01520-6 (2023).
Prjibelski, A., Antipov, D., Meleshko, D., Lapidus, A. & Korobeynikov, A. Using SPAdes de novo assembler. Curr. Protoc. Bioinform. 70, e102 (2020).
Meng, G., Li, Y., Yang, C. & Liu, S. MitoZ: A toolkit for animal mitochondrial genome assembly, annotation and visualization. Nucleic Acids Res. 47, e63–e63 (2019).
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410. https://doi.org/10.1016/S0022-2836(05)80360-2 (1990).
Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: Quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075. https://doi.org/10.1093/bioinformatics/btt086 (2013).
Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: Assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).
Levy Karin, E., Mirdita, M. & Söding, J. MetaEuk—Sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics. Microbiome 8, 48 (2020).
Phan, H. T. T. et al. Low amplitude boom-and-bust cycles define the Septoria Nodorum Blotch interaction. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.01785 (2020).
Shaw, P. D., Graham, M., Kennedy, J., Milne, I. & Marshall, D. F. Helium: Visualization of large scale plant pedigrees. BMC Bioinformatics 15, 259 (2014).
Jones, P. et al. InterProScan 5: Genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).
Klopfenstein, D. V. et al. GOATOOLS: A Python library for Gene Ontology analyses. Sci. Rep. 8, 10872. https://doi.org/10.1038/s41598-018-28948-z (2018).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760. https://doi.org/10.1093/bioinformatics/btp324 (2009).
Van der Auwera, G. A. & O’Connor, B. D. Genomics in the cloud: using Docker, GATK, and WDL in Terra (O’Reilly Media, 2020).
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience https://doi.org/10.1093/gigascience/giab008 (2021).
Wang, J. & Zhang, Z. GAPIT Version 3: Boosting power and accuracy for genomic association and prediction. Genomics Proteomics Bioinformatics 19, 629–640. https://doi.org/10.1016/j.gpb.2021.08.005 (2021).
Acknowledgements
We thank Dr. Kefei Chen from AAGI for valuable feedback on the GWAS analysis. Data analysis was performed using the computational resources of the Pawsey Setonix Supercomputer at the Pawsey Supercomputing Research Centre (Setonix Supercomputer, 2023). This project was supported by the Grains Research and Development Corporation (GRDC) and Curtin University as part of the co-investment in the Centre for Crop and Disease Management (CCDM) phase III project: “Advanced Bioinformatics Approaches” [CUR00023]. M. Hossain and N. Gray were supported by Research Training Program (RTP) grants provided by the Australian Government Department of Education. Additional support for M. Hossain was provided by a GRDC Research Scholarship [CUR2301-006RSX], with support from the WA Agricultural Research Collaboration (WAARC).
Funding
This project was supported by the Grains Research and Development Corporation (GRDC) and Curtin University as part of the co-investment in the Centre for Crop and Disease Management (CCDM) phase III project: “Advanced Bioinformatics Approaches” [CUR00023]. M. Hossain and N. Gray were supported by Research Training Program (RTP) grants provided by the Australian Government Department of Education. Additional support for M. Hossain was provided by a GRDC Research Scholarship [CUR2301-006RSX], with support from the WA Agricultural Research Collaboration (WAARC).
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JH and MH conceived the study, wrote the manuscript, and developed the code. HP, EF, KC, LL, MS and HG provided isolates and materials used in this study. PM, KG and NG assisted with code development and testing. All authors revised and approved the manuscript.
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Hossain, M., Gray, N., Misiun, P. et al. EffectorFisher: association of disease phenotype with pangenomic protein-isoform profiles for improved prediction of fungal pathogenicity effectors. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43646-x
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DOI: https://doi.org/10.1038/s41598-026-43646-x
