Chromosomal-level genome assembly of the autotetraploid Anoectochilus roxburghii (Jinxianlian, Orchidaceae).

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Jing Fang, Mei Wu, Xinyi Zhang, Yang Chen, Lingjuan Tou, Jinjing Xu, Xiangjun Kong, Yingxiong Qiu
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引用次数: 0

Abstract

Anoectochilus roxburghii (Orchidaceae), commonly known as Jinxianlian, is a highly valued traditional Chinese herbal medicine. Here, we present a high-quality chromosome-level genome assembly of the cultivar 'Jinkang No.1'. Genome survey analyses suggest that the cultivar is an autotetraploid. The assembled genome spans 5.17 Gb, with a contig N50 of 24.90 Mb, and 93.4% (4.82 Gb) is anchored onto 80 pseudo-chromosomes across 20 homologous groups. Annotation of the genome assembly identifies 76.42% repetitive elements and 88,106 protein-coding genes, with 94.6% of these genes functionally annotated. Ks analysis of collinear gene pairs indicates a recent species-specific polyploidization event, likely resulting in autotetraploidization. This reference genome provides a valuable resource for functional genomics, evolutionary biology, and molecular breeding studies of A. roxburghii and its related species.

同源四倍体金仙莲凤梨染色体水平的基因组组装。
金仙莲(Anoectochilus roxburghii),俗称金仙莲,是一种价值很高的传统中草药。在这里,我们提出了一个高质量的染色体水平的基因组组装栽培‘金康1号’。基因组调查分析表明该品种为同源四倍体。该基因组全长5.17 Gb, N50为24.90 Mb,其中93.4% (4.82 Gb)固定在20个同源类群的80条伪染色体上。基因组组装注释鉴定出76.42%的重复元件和88,106个蛋白质编码基因,其中94.6%的基因被功能注释。共线基因对的Ks分析表明,最近发生了物种特异性的多倍体化事件,可能导致同源四倍体化。该参考基因组为刺梨及其近缘种的功能基因组学、进化生物学和分子育种研究提供了有价值的资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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