five

Genomic characterization and gene bank curation of Aegilops using genotyping-by-sequencing

收藏
NIAID Data Ecosystem2026-05-01 收录
下载链接:
http://datadryad.org/dataset/doi%253A10.5061%252Fdryad.mgqnk994n
下载链接
链接失效反馈
官方服务:
资源简介:
In this study, genotyping-by-sequencing (GBS) was performed on 1041 Aegilops accessions, representing 23 different species. These accessions have been maintained by the Wheat Genetics and Resource Center (WGRC) at Kansas State University. The GBS FASTQ files have been uploaded to the NCBI SRA public repository under the BioProject accession number # PRJNA985892. We have provided other files related to data analysis, such as the barcode key file, SNP matrices, and taxonomic information of the accessions in this Dryad repository, which can be accessed through the provided link.  The aim of the study was to explore the genetic and genomic characteristics of wild wheat relatives, Aegilops, using a larger number of SNP markers. Here, we also curated the WGRC gene bank Aegilops collection via the identification of misclassified accessions and genetically identical redundant accessions. Further, we explored the genomic relationship between wheat and the different Aegilops species.  Methods In this study, Aegilops accessions preserved and maintained as single seed descent (SSD) at the Wheat Genetics Resource Center at Kansas State University were subjected to genotyping-by-sequencing. The DNA was extracted from the seedlings of individual plants and GBS library was made as described in the manuscript. The GBS data were utilized to detect variants using a reference-based pipeline, such as TASSEL GBS, in cases where genome assemblies were available. For the accessions without available genome assemblies, the sequence FASTQ files were aligned to a mock reference generated from the raw GBS data. The accessions with a higher amount of data were chosen to generate the mock reference. Variants were called using both GBS-SNP-CROP and bcfools pipeline. Subsequently, the SNP matrices were filtered and utilized for various analyses.
创建时间:
2024-01-08
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