藜麦藜麦多样性面板的基因组组装。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Elodie Rey, Michael Abrouk, Isabelle Dufau, Nathalie Rodde, Noha Saber, Jana Cizkova, Gabriele Fiene, Clara Stanschewski, David E Jarvis, Eric N Jellen, Peter J Maughan, Ingrid von Baer, Maxim Troukhan, Maksym Kravchuk, Eva Hribova, Stephane Cauet, Simon G Krattinger, Mark Tester
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引用次数: 0

摘要

藜麦(Chenopodium Quinoa)是应对未来粮食和营养安全挑战的重要作物。随着藜麦在世界范围内的种植扩大,需要深入研究藜麦的多样性,以支持藜麦的农艺改良和适应性。在这项研究中,我们报道了8个藜麦材料的染色体尺度基因组组装的构建,涵盖了低地和高地藜麦的表型和遗传多样性范围。该组装体由PacBio HiFi reads和Bionano Saphyr光学图谱组合而成,总组装大小平均为1.28 Gb,平均N50为71.1 Mb。对8个新的藜麦基因组预测了43733到48564个基因模型,平均每个藜麦基因组的66%被归类为重复序列。八个基因组组合之间的比对允许鉴定结构重排,包括倒位,易位和重复。这8个新的藜麦基因组组合为关联遗传学、比较基因组学和泛基因组分析提供了资源,用于发现农业重要性状的遗传成分和变异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Genome assembly of a diversity panel of Chenopodium quinoa.

Genome assembly of a diversity panel of Chenopodium quinoa.

Genome assembly of a diversity panel of Chenopodium quinoa.

Genome assembly of a diversity panel of Chenopodium quinoa.

Quinoa (Chenopodium quinoa) is an important crop for the future challenges of food and nutrient security. Deep characterization of quinoa diversity is needed to support the agronomic improvement and adaptation of quinoa as its worldwide cultivation expands. In this study, we report the construction of chromosome-scale genome assemblies of eight quinoa accessions covering the range of phenotypic and genetic diversity of both lowland and highland quinoas. The assemblies were produced from a combination of PacBio HiFi reads and Bionano Saphyr optical maps, with total assembly sizes averaging 1.28 Gb with a mean N50 of 71.1 Mb. Between 43,733 and 48,564 gene models were predicted for the eight new quinoa genomes, and on average, 66% of each quinoa genome was classified as repetitive sequences. Alignment between the eight genome assemblies allowed the identification of structural rearrangements including inversions, translocations, and duplications. These eight novel quinoa genome assemblies provide a resource for association genetics, comparative genomics, and pan-genome analyses for the discovery of genetic components and variations underlying agriculturally important traits.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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