Guruswamy Mahesh, Erik W Martin, Mohammad Aqdas, Kyu-Seon Oh, Myong-Hee Sung
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引用次数: 0
摘要
模式生物基因组 DNA 片段的靶向基因敲除、突变或基因敲入已被广泛用于功能和细胞追踪研究。所需的基因扰动通常通过基于重组或 CRISPR/Cas9 的基因组工程来实现。为了验证预期的基因修饰,通常会根据酶裂解和随之而来的长度模式(如在 Southern 分析中)对目标基因座周围的局部区域进行检查。尽管这种方法被广泛使用,但其读数不完整、不明确。随着高通量测序成本的降低,考虑对定向遗传扰动后的新菌株进行大规模验证正变得可行。在这里,我们描述了四个新型报告小鼠品系的全基因组序列数据集和变异分析结果。这有助于验证这些品系,并确定基因组的所有非目标效应,从而增加基因组序列的遗传多样性,超过近交系小鼠公共数据库中的基因组序列。
Whole genome sequencing of CRISPR/Cas9-engineered NF-κB reporter mice for validation and variant discovery.
Targeted knockout, mutations, or knock-in of genomic DNA fragments in model organisms have been used widely for functional and cell-tracking studies. The desired genetic perturbation is often accomplished by recombination-based or CRISPR/Cas9-based genome engineering. For validating the intended genetic modification, a local region surrounding the targeted locus is typically examined based on enzymatic cleavage and consequent length patterns, e.g. in a Southern analysis. Despite its wide use, this approach is open to incomplete and ambiguous readouts. With decreasing costs of high-throughput sequencing, it is becoming feasible to consider a large-scale validation of a new strain after a targeted genetic perturbation. Here we describe a dataset of whole-genome sequences and the variant analysis results from four novel reporter mouse strains. This served to validate the strains and identified all the off-target effects on the genome, thereby increasing the genetic diversity of genomic sequences over those represented in the public databases for inbred mice.
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.