Wheat Data Integration and FAIRification: IWGSC, GrainGenes, Ensembl and Other Data Repositories

Alaux, Michael; Dyer, Sarah; Sen, Taner Z.

doi:10.1007/978-3-031-38294-9_2

Cited by 3 publications

(3 citation statements)

References 42 publications

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“…Markers could not differentiate between Lr2a, Lr2b, and Lr2c, and Lr17 and Lr37 (**). Physical positions of the SNP markers were identified based on information provided by Sun et al [61,67]. Physical regions of cloned genes were identified based on a reference genome of Chinese spring wheat.…”

Section: Development and Validation Of Kasp Markers For Lr Resistance...mentioning

confidence: 99%

Phenotyping and Exploitation of Kompetitive Allele-Specific PCR Assays for Genes Underpinning Leaf Rust Resistance in New Spring Wheat Mutant Lines

Kenzhebayeva,

Mazkirat,

Shoinbekova

et al. 2024

CIMB

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Leaf rust (Puccinia triticina Eriks) is a wheat disease causing substantial yield losses in wheat production globally. The identification of genetic resources with permanently effective resistance genes and the generation of mutant lines showing increased levels of resistance allow the efficient incorporation of these target genes into germplasm pools by marker-assisted breeding. In this study, new mutant (M3 generation) lines generated from the rust-resistant variety Kazakhstanskaya-19 were developed using gamma-induced mutagenesis through 300-, 350-, and 400-Gy doses. In field trials after leaf rust inoculation, 75 mutant lines showed adult plant resistance. These lines were evaluated for resistance at the seedling stage via microscopy in greenhouse experiments. Most of these lines (89.33%) were characterized as resistant at both developmental stages. Hyperspectral imaging analysis indicated that infected leaves of wheat genotypes showed increased relative reflectance in visible and near-infrared light compared to the non-infected genotypes, with peak means at 462 and 644 nm, and 1936 and 2392 nm, respectively. Five spectral indexes, including red edge normalized difference vegetation index (RNDVI), structure-insensitive pigment index (SIPI), ratio vegetation index (RVSI), water index (WI), and normalized difference water index (NDWI), demonstrated significant potential for determining disease severity at the seedling stage. The most significant differences in reflectance between susceptible and resistant mutant lines appeared at 694.57 and 987.51 nm. The mutant lines developed were also used for the development and validation of KASP markers for leaf rust resistance genes Lr1, Lr2a, Lr3, Lr9, Lr10, and Lr17. The mutant lines had high frequencies of “a” resistance alleles (0.88) in all six Lr genes, which were significantly associated with seedling resistance and suggest the potential of favorable haplotype introgression through functional markers. Nine mutant lines characterized by the presence of “b” alleles in Lr9 and Lr10—except for one line with allele “a” in Lr9 and three mutant lines with allele “a” in Lr10—showed the progressive development of fungal haustorial mother cells 72 h after inoculation. One line from 300-Gy-dosed mutant germplasm with “b” alleles in Lr1, Lr2a, Lr10, and Lr17 and “a” alleles in Lr3 and Lr9 was characterized as resistant based on the low number of haustorial mother cells, suggesting the contribution of the “a” alleles of Lr3 and Lr9.

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Section: Development and Validation Of Kasp Markers For Lr Resistance...mentioning

confidence: 99%

Phenotyping and Exploitation of Kompetitive Allele-Specific PCR Assays for Genes Underpinning Leaf Rust Resistance in New Spring Wheat Mutant Lines

Kenzhebayeva,

Mazkirat,

Shoinbekova

et al. 2024

CIMB

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“…The resistance to diseases can be expressed as the resistance to the pathogen agent as in examples ( 5) to (7). Fungi are the major cause of disease in wheat.…”

Section: Plos Onementioning

confidence: 99%

“…They develop new semantic web-based tools for the semantic description of agricultural data, making them actionable and openly accessible, according to the FAIR (findable, accessible, interoperable, and reusable) principles [4]. The most prominent collaborative efforts, KnetMiner [5], WheatIS data discovery tool [6], FAIDARE FAIR Data-finder for Agricultural REsearch [7] or AgroLD-Southgreen [8,9] uses ontologies of traits and phenotypes, e.g. Plant Trait Ontology [10] and Crop Ontology [11], to describe phenotypic traits in plants by controlled vocabularies and make them interoperable.…”

Section: Introductionmentioning

confidence: 99%

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Mapping bread wheat trait ontologies for semantic interoperability

Nédellec,

Aubin,

Sauvion

et al. 2024

F1000Res

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Background The Wheat Crop ontology was created to annotate phenotypic experimental data (i.e. field and greenhouse measurements standardized and integrated in databases). The Wheat Trait and Phenotype ontology was created to annotate information on wheat traits from the literature (i.e. text found in the abstract, results and discussion of scholarly articles). To enable seamless data retrieval on wheat traits from these complementary sources, the classes in the two ontologies have been aligned. Methods All pairs of ontology classes were examined and categorized in nine groups based on the nature of their relationships (e.g. equivalence, subsumption). General principles emerged from this process which were formalized into rules. The Simple Standard for Sharing Ontological Mappings (SSSOM) representation was chosen to represent the mappings in RDF (Resource Description Framework), including their metadata such as creators, reviewers, and justification (including rules). Results The mapping dataset is publicly available. It covers 77% of the ontology classes. Most labels of the aligned classes differed significantly and required domain expertise for decisions, especially for traits related to biotic stress. Consequently, most mappings are close mappings rather than exact equivalents. Conclusions We present the end-to-end manual process used to select and represent mappings in SSSOM within the specific domain of wheat traits. We derive general lessons from the complex alignment process that extend beyond the specific case of these two ontologies and more generally apply to alignments of specialized ontologies for information retrieval purposes. This work demonstrates the relevance of SSSOM for representing these mappings.

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Wheat Data Integration and FAIRification: IWGSC, GrainGenes, Ensembl and Other Data Repositories

Cited by 3 publications

References 42 publications

Phenotyping and Exploitation of Kompetitive Allele-Specific PCR Assays for Genes Underpinning Leaf Rust Resistance in New Spring Wheat Mutant Lines

Phenotyping and Exploitation of Kompetitive Allele-Specific PCR Assays for Genes Underpinning Leaf Rust Resistance in New Spring Wheat Mutant Lines

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Mapping bread wheat trait ontologies for semantic interoperability

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