Chromosome-scale genome assembly of Korean goosegrass (Eleusine indica).

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-01-27 DOI:10.1038/s41597-025-04490-2
Solji Lee, Changsoo Kim
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Abstract

Goosegrass, belonging to the genus Eleusine within the Chloridoideae subfamily, is often one of the problematic weeds with strong invasiveness, competing with crops for essential survival resources. Although a chromosome-level genome assembly of E. indica from China was published last year, the present research focuses on a population of E. indica native to South Korea. Considering the high genetic variability among wild E. indica populations, constructing multi-reference genomes from geographically distinct populations is crucial for comprehensive weed management strategies. In this study, we sequenced and assembled the whole genome using PacBio long read and Illumina short read sequencing platforms. We then combined Pore-C sequencing technology to successfully anchor 255 contigs to nine pseudochromosomes. The chromosome-level genome assembly showed a high level of completeness with a 97% score according to BUSCO analysis results. Repetitive sequences accounted for 97% of the genome assembly, and 26,836 protein-coding genes were predicted. The high-quality genome assembly of E. indica will serve as a valuable genetic resource to enhance our understanding of weed control research.

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韩国鹅草染色体尺度的基因组组装。
鹅草属是鹅草亚科中的鹅草属,是入侵性强的问题杂草之一,与农作物争夺必需的生存资源。虽然去年发表了来自中国的籼稻染色体水平的基因组组装,但目前的研究主要集中在韩国的籼稻种群上。考虑到野生籼稻居群间的高度遗传变异,从地理上不同的居群中构建多参考基因组对于制定综合杂草管理策略至关重要。在本研究中,我们使用PacBio长读和Illumina短读测序平台对整个基因组进行测序和组装。然后,我们结合Pore-C测序技术成功地将255个contigs锚定在9条假染色体上。根据BUSCO分析结果,染色体水平的基因组组装显示出高水平的完整性,得分为97%。重复序列占基因组组装的97%,预测了26,836个蛋白质编码基因。高质量的籼稻基因组组合将为加强杂草防治研究提供宝贵的遗传资源。
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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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