Updated guidelines for gene nomenclature in wheat

生物 基因 基因组 遗传学 计算生物学 生物技术 小麦族 命名法 基因命名 生态学 分类学(生物学)
作者
Scott A. Boden,R. A. McIntosh,Cristóbal Uauy,Simon G. Krattinger,Jorge Dubcovsky,William J. Rogers,X. C. Xia,Е. Д. Бадаева,Alison R. Bentley,Gina Brown‐Guedira,Mario Cáccamo,Luigi Cattivelli,Parveen Chhuneja,James Cockram,Bruno Contreras‐Moreira,Susanne Dreisigacker,David Edwards,Fernanda G. González,Carlos Guzmán,Tatsuya M. Ikeda,I. Karsaï,Shuhei Nasuda,Curtis Pozniak,R. Prins,Taner Z. Sen,Paula Silva,Hana Šimková,Y Zhang
出处
期刊:Theoretical and Applied Genetics [Springer Nature]
卷期号:136 (4) 被引量:41
标识
DOI:10.1007/s00122-023-04253-w
摘要

Abstract 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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