five

Data from: Efficient detection of novel nuclear markers for Brassicaceae by transcriptome sequencing

收藏
DataONE2015-07-24 更新2024-06-27 收录
下载链接:
https://search.dataone.org/view/null
下载链接
链接失效反馈
官方服务:
资源简介:
The lack of DNA sequence information for most non-model organisms impairs the design of primers that are universally applicable for the study of molecular polymorphisms in nuclear markers. Next-generation sequencing (NGS) techniques nowadays provide a powerful approach to overcome this limitation. We present a flexible and inexpensive method to identify large numbers of nuclear primer pairs that amplify in most Brassicaceae species. We first obtained and mapped NGS transcriptome sequencing reads from two of the distantly related Brassicaceae species, Cardamine hirsuta and Arabis alpina, onto the Arabidopsis thaliana reference genome, and then identified short conserved sequence motifs among the three species bioinformatically. From these, primer pairs to amplify coding regions (nuclear protein coding loci, NPCL) and exon-primed intron-crossing sequences (EPIC) were developed. We identified 2,334 universally applicable primer pairs, targeting 1,164 genes, which provide a large pool of markers as readily usable genomic resource that will help addressing novel questions in the Brassicaceae family. Testing a subset of the newly designed nuclear primer pairs revealed that a great majority yielded a single amplicon in all of the 30 investigated Brassicaceae taxa. Sequence analysis and phylogenetic reconstruction with a subset of these markers on different levels of phylogenetic divergence in the mustard family were compared with previous studies. The results corroborate the usefulness of the newly developed primer pairs, e.g., for phylogenetic analyses or population genetic studies. Thus, our method provides a cost-effective approach for designing nuclear loci across a broad range of taxa and is compatible with current NGS technologies.
创建时间:
2015-07-24
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