scalepopgen:生物信息工作流资源在Nextflow中实现全面的人口基因组分析
Maulik Upadhyay1, Neža Pogorevc1, Ivica Medugorac1
1Population Genomics Group, Department of Veterinary Sciences, LMU Munich, Martinsried 82152, Germany.
Molecular biology and evolution
|March 20, 2024
概括
可扩展的人口基因组学 (scalepopgen) 自动化复杂的人口基因组分析,简化人口结构推断和选择签名识别. 这个工作流管理系统简化了使用Nextflow的研究人员的数据处理.
科学领域:
- 人口基因组学是人口的基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学是一种计算生物学.
背景情况:
- 种群基因组分析通常需要许多工具,导致复杂的安装和数据预处理挑战.
- 容器技术简化了工具和依赖管理,但多步分析仍然受益于工作流系统.
- 像Nextflow和Snakemake这样的工作流管理系统可以进行复杂的人口基因组分析.
研究的目的:
- 为了介绍skalepopgen,一个集合为人口基因组分析的自动化工作流程.
- 为了促进广泛使用的双单核酸多态 (SNP) 数据的种群基因组分析.
- 为人口结构推断和选择签名识别提供简化方法.
主要方法:
- 在Nextflow中开发,scalepopgen可以自动化分析VCF或PLINK二进制格式的SNP数据.
- 工作流包括个人/基因型过,主要成分分析 (PCA),添加剂分析,TreeMix和选择签名识别.
- 可扩展的人口基因组学 (scalepopgen) 可以在本地或使用Conda,Singularity或Docker的HPC系统上运行.
主要成果:
- 可扩展的人口基因组学 (scalepopgen) 自动化关键的人口基因组分析,包括人口结构和选择特征.
- 该管道集成了各种开源工具,并提供Python/R脚本用于结果收集和可视化.
- 自动化工作流程简化了人口基因组学复杂数据转换和分析.
结论:
- 可扩展的人口基因组学 (scalepopgen) 为常见的人口基因组分析提供了强大的和自动化的解决方案.
- 该系统简化了复杂的人口基因组方法的应用,提高了研究效率.
- 在GitHub上免费提供,scalepopgen支持本地和HPC执行,促进可访问性.
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