Artículo

Updated guidelines for gene nomenclature in wheat

Boden, S. A. ; McIntosh, R. A. ; Uauy, C. ; Krattinger, S.G. ; Dubcovsky, Jorge ; Rogers, William John Icon; Xia, X. C. ; Badaeva, E.D. ; Bentley, A.R. ; Brown Guedira, G. ; Cáccamo, Mario José ; Cattivelli, L. ; Chhuneja, P. ; Cockram, J. ; Contreras Moreira, B. ; Dreisigacker, S. ; Edwards, D. ; González, Fernanda Gabriela Icon; Guzmán, C. ; Ikeda, T. M. ; Karsai, I. ; Nasuda, S. ; Pozniak, C. ; Prins, R. ; Sen, T .Z. ; Silva, P. ; Simkova, H. ; Zhang, Yunlin Icon

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Fecha

04/2023

Editorial

Springer

Revista

Theoretical And Applied Genetics

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ISSN

0040-5752

eISSN

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Inglés

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Clasificación temática

Resumen

Key message: Here, we provide an updated set of guidelines for naming genes in wheat that has been endorsed by the wheat research community. Abstract: The last decade has seen a proliferation in genomic resources for wheat, including reference- and pan-genome assemblies with gene annotations, which provide new opportunities to detect, characterise, and describe genes that influence traits of interest. The expansion of genetic information has supported growth of the wheat research community and catalysed strong interest in the genes that control agronomically important traits, such as yield, pathogen resistance, grain quality, and abiotic stress tolerance. To accommodate these developments, we present an updated set of guidelines for gene nomenclature in wheat. These guidelines can be used to describe loci identified based on morphological or phenotypic features or to name genes based on sequence information, such as similarity to genes characterised in other species or the biochemical properties of the encoded protein. The updated guidelines provide a flexible system that is not overly prescriptive but provides structure and a common framework for naming genes in wheat, which may be extended to related cereal species. We propose these guidelines be used henceforth by the wheat research community to facilitate integration of data from independent studies and allow broader and more efficient use of text and data mining approaches, which will ultimately help further accelerate wheat research and breeding.

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