five

Transcriptome assemblies of three Bauhinia species.

收藏
DataCite Commons2025-05-26 更新2025-04-15 收录
下载链接:
http://gigadb.org/dataset/100345
下载链接
链接失效反馈
官方服务:
资源简介:
<em>Bauhinia blakeana Dunn</em> commonly known as the Hong Kong Orchid Tree, was adopted as the city flower of Hong Kong in 1965 and became the official emblem of the Hong Kong Special Administrative Region (HKSAR) in 1997. It was first discovered in the 1880s and is now planted all over the territory. It has long been suspected that <em>Bauhinia blakeana</em> is the sterile hybrid of two related species, <em>B. purpurea</em> and <em>B. variegata</em>, although limited genetic information of this plant has been available to confirm this. In this study utilizing crowdfunded data from the Bauhinia Genome project, unigenes of <em>B. blakeana</em> (94,755), <em>B. purpurea</em> (111,976) and <em>B. variegata</em> (81,757) were obtained after de novo transcriptome assembly of the three species leaves. We also pooled the reads of flowers and leaves from <em>B. blakeana</em>, resulting in 123,668 clusters. Data from mapping reads of the tested species to chloroplast sequences demonstrates that <em>B. purpurea</em> is the maternal parent. Differential expressed genes up-regulated in <em>B. blakeana</em> vs <em>B. purpurea</em> and <em>B. variegata</em> showed more specific bacterial and pathogen resistance, UV tolerance and drought avoidance. Using SNP parentage testing, a total of 79,302 SNPs were observed in 27 possible scenarios, in which 65,159 SNPs follow Mendelian law and yielded 82.17% in support of their parentage relationship. Raw data (see: GigaDB:100245) and RNA-extraction protocols (see: DOI:10.17504/protocols.io.gsnbwde) have already been published, and included here are the transcriptome assemblies, annotations and SNPs, along with other materials and results supporting this research. For more information see Buahinia Project pages.
提供机构:
GigaScience Database
创建时间:
2018-02-12
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作