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在家族遗传树上的高维度梯度的多核算法
Karthik Gangavarapu1, Xiang Ji2, Guy Baele3
1Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, United States.
Bioinformatics (Oxford, England)
|January 20, 2024
概括
我们开发了更快的GPU算法用于遗传学分析,显著加快传染病监测. 这些方法使以前难以处理的大数据集的进化推理成为可能.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 进化生物学 进化生物学
背景情况:
- 高通量测序产生了大量的病原体基因组数据,需要有效的基因组学分析来监测全球传染病.
- 哈密尔顿蒙特卡洛 (HMC) 是一个关键的贝叶斯推理技术,但它的计算成本随着数据集大小 (N) 的增加而增加.
- 计算日志概率梯度对HMC至关重要,传统上需要O(N^2) 运算,限制了它的应用.
研究的目的:
- 开发和实施新的,大规模并行算法,用于计算关于分支长度特定参数的日志概率梯度.
- 利用图形处理单元 (GPU) 加快这些计算,克服了以前基于CPU的复杂模型方法的局限性.
- 为大型基因组数据集提供更高效,更可扩展的基因组推断.
主要方法:
- 开发了大量并行算法来计算日志概率和分支长度特定参数的梯度.
- 在图形处理单元 (GPU) 上实现这些算法,以显著加快计算速度.
- 将GPU算法集成到BEAGLE库 (v4.0.0) 中,以便在统计遗传学中获得广泛的访问.
主要成果:
- 与CPU实现相比,基于编码子的模型的速度提高了>128倍,基于核酸的模型的速度提高了>8倍.
- 通过估计西尼罗河病毒在美国的引入时间来证明其实际实用性,这是以前难以解决的任务.
- 在各种数据集上成功对比算法,包括登革热病毒,食肉类线粒体和酵母.
结论:
- 新的GPU算法为家族遗传推断提供了大量的计算加速.
- 这些进步使复杂的遗传学模型和大规模的基因组数据集能够通过计算处理.
- 在BEAGLE的实施促进了在传染病动态和进化研究中的更广泛的采用和应用.
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