kalis:是Li & Stephens模型的现代实现,用于在R中推断本地祖先
Louis J M Aslett1, Ryan R Christ2
1Department of Mathematical Sciences, Durham University, Stockton Road, Durham, DH1 3LE, UK. louis.aslett@durham.ac.uk.
BMC bioinformatics
|February 28, 2024
概括
这项研究介绍了kalis,这是一个R包,用于从基因组数据中快速推断祖先. 它可以有效地分析大数据集,用于人口基因组学研究.
科学领域:
- 人口基因组学 人口基因组学
- 计算生物学 计算生物学
- 统计遗传学 统计遗传学
背景情况:
- 从基因组数据推断最近的进化历史对于理解人口动态至关重要.
- 李·斯蒂芬斯 (LS) 模型是最近祖先隐藏马尔科夫建模的标准.
- 精确的哈普洛型分相对于人口规模的基因组分析至关重要.
研究的目的:
- 开发一个高性能计算引擎用于祖先推理.
- 为研究人员提供可访问的R包,以分析大型基因组数据集.
- 为了促进全基因组的关联,选择和内进研究.
主要方法:
- 实现一个高性能引擎后部解码.
- 利用多核并行和CPU矢量指令集来实现可扩展性.
- 为方便使用和可访问性,开发了一个名为kalis的R包.
主要成果:
- 卡利斯提供快速访问后部解码,最低限度的预处理.
- 该软件可以有效地扩展,分析数十万个基因组.
- 能够在特定的基因组位置快速解决祖先问题.
结论:
- 卡利斯包在大型基因组数据集中促进了当地祖先推断.
- 为选择和关联研究提供高效的全基因组选.
- 支持开发定制变体特定的祖先推断管道.
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