Chromosome-scale genome assembly of the mangrove climber species Dalbergia candenatensis.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Miaomiao Shi, Yu Zhang, Huiwen Huang, Shiran Gu, Xiangping Wang, Shijin Li, Zhongtao Zhao, Tieyao Tu
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引用次数: 0

Abstract

Consisting of trees, climbers and herbs exclusively in the intertidal environments, mangrove forest is one of the most extreme and vulnerable ecosystems of our planet and has long been of great interest for biologists and ecologists. Here, we first assembled the chromosome-scale genome of a climber mangrove plant, Dalbergia candenatensis. The assembled genome size is approximately 474.55 Mb, with a scaffold N50 of 48.1 Mb, a complete BUSCO score of 98.4%, and a high LTR Assembly Index value of 21. The genome contained 283.46 Mb (59.74%) repetitive sequences, and 29,554 protein-coding genes were predicted, of which 87.54% were functionally annotated in five databases. The high-quality genome assembly and annotation presented herein provide a valuable genomic resource that will expedite genomic and evolutionary studies of mangrove plants and facilitate the elucidation of molecular mechanisms underlying the salt- and water-logging-tolerance of mangrove plants.

红树林攀缘物种 Dalbergia candenatensis 的染色体级基因组组装。
红树林是地球上最极端和最脆弱的生态系统之一,由树木、攀缘植物和草本植物组成,只生长在潮间带环境中,长期以来一直备受生物学家和生态学家的关注。在这里,我们首次组装了攀缘植物红树(Dalbergia candenatensis)的染色体级基因组。组装的基因组大小约为 474.55 Mb,支架 N50 为 48.1 Mb,完整 BUSCO 得分为 98.4%,LTR 组装指数值高达 21。该基因组包含 283.46 Mb(59.74%)重复序列,预测了 29,554 个蛋白编码基因,其中 87.54% 的基因在五个数据库中进行了功能注释。本文介绍的高质量基因组组装和注释提供了宝贵的基因组资源,将加速红树植物的基因组和进化研究,并有助于阐明红树植物耐盐和耐涝的分子机制。
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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