SV training benchmark datasets
收藏NIAID Data Ecosystem2026-03-12 收录
下载链接：
https://zenodo.org/record/4603454
下载链接
链接失效反馈官方服务：
资源简介：
Read sets for each type of SV sniffles can identify - insertions, deletions, inversions, chromosomal translocations, tandem duplications.
SVs were added to an assembly of E. coli sakai (GenBank accession GCF_000008865.2), then 10x coverage worth of reads for the mutated genome were simulated using Badread (https://github.com/rrwick/Badread). The intention is for these reads to be aligned to GCF_000008865.2 (unmutated), then SV calling using sniffles to be performed. Sniffles performance at detecting the added SVs will be assessed. Detailed records of the SVs added per read set are also available on zenodo.
创建时间：
2021-03-22
