单倍型分辨的upas树(Antiaris toxicaria)基因组组装。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Ke Miao, Ya Wang, Luxiao Hou, Yan Liu, Haiyang Liu, Yunheng Ji
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引用次数: 0

摘要

upas树(Antiaris toxicaria Lesch.)是一种具有重要医学价值的植物,含有多种具有显著生物活性的特殊代谢物。参考基因组的缺乏阻碍了对这种植物的深入研究以及合理开发和保护。在这里,我们首次展示了已解析染色体组规模的upas树全模式基因组。该基因组由 26 条染色体组成,包含 1.34 Gb 的测序数据,等位基因 N50 长度为 60 Mb。基因组注释确定了乌帕斯树基因组中的 4.35 万个蛋白编码基因,其中 98.75% 的基因得到了功能注释。这一高质量的参考基因组将为进一步研究乌帕斯树的进化和功能基因组学奠定基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Haplotype-resolved genome assembly of the upas tree (Antiaris toxicaria).

The upas tree (Antiaris toxicaria Lesch.) is a medically important plant that contains various specialized metabolites with significant bioactivity. The lack of a reference genome hinders the in-depth study as well as rational exploitation and conservation of this plant. Here, we present the first holotype-resolved chromosome-scale genome of the upas tree. The assembled genome consisted of 26 chromosomes that contain 1.34 Gb of sequencing data with a contig N50 length of 60 Mb. Genome annotation identified 43,500 protein-coding genes in the upas tree genome, of which 98.75% were functionally annotated. This high-quality reference genome will lay the foundation for further studies on the evolution and functional genomics of the upas tree.

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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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