微调GBS数据,比较参考基因组和模拟基因组方法,以推进在较少研究的养殖物种中进行基因组选择
Daniel Fischer1, Miika Tapio2, Oliver Bitz2
1Applied Statistical Methods, Natural Resources, Natural Resources Institute Finland (Luke), Jokioinen, 31600, Finland. daniel.fischer@luke.fi.
BMC genomics
|February 5, 2025
概括
这项研究优化了一种具有成本效益的基因型测序 (GBS) 双消化RAD测序 (ddRAD) 管道,用于各种养殖物种. 这种用户友好的工作流程使得即使没有参考基因组,也能有效地进行基因组定型,从而促进非模型生物体的基因组选择.
科学领域:
- 动物遗传学和动物繁殖
- 生物信息学和计算生物学
- 水产养殖和畜牧管理
背景情况:
- 高效的基因型定型对于动物培养中的基因组选择至关重要.
- 非模型物种往往缺乏可访问和高效的基因型化解决方案.
- 现有的方法可能不具有成本效益或适用于各种应用的可扩展性.
研究的目的:
- 为了优化基因型测序 (GBS) 双位数RAD测序 (ddRAD) 管道.
- 用牛数据自动化生物信息分析.
- 为了证明管道对非模型欧洲白鱼的应用.
主要方法:
- 一个ddRAD测序管道的开发和优化.
- 生物信息分析自动化使用Snakemake.
- 使用三个样本进行变异调用,创建一个de-novo参考基因组 (模拟参考).
主要成果:
- DdRAD数据生成可扩展到384个样本以进行可靠的相关性估计.
- 通过模拟参考,每个物种确定了超过15,000个GBS变体.
- 优化的管道显示在牛中具有很高的重复性,结果与现有的参考基因组相比较.
结论:
- 具有成本效益的ddRAD策略与高效的生物信息工作流程使其能够广泛采用.
- 一个参考基因组不是成功的基因型定型的必需品.
- 与Snakemake集成的管道对模型和非模型物种都是用户友好的.
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