加快开放的修改光谱库在高维空间中搜索张量核心
Jaeyoung Kang1, Weihong Xu2, Wout Bittremieux3
1Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA 92093, United States.
Bioinformatics (Oxford, England)
|June 27, 2023
概括
我们开发了HOMS-TC,这是一个用于在质谱 (MS) 蛋白质组学中进行开放修改搜索 (OMS) 的新算法. 这种并行方法显著加快了改性的识别,改善了在大数据集中的发现.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 计算生物学 计算生物学
- 生物技术是生物技术.
背景情况:
- 质谱 (MS) 蛋白质组学已经取得了重大进展,改善了吞吐量并降低了成本.
- 谱图书馆搜索是标注MS数据的标准,但错过了新,包括具有后翻译修饰 (PTM) 的.
- 开放式修改搜索 (OMS) 解决了这个问题,但面临着巨大的搜索空间和长时间的挑战.
研究的目的:
- 引入HOMS-TC,这是一种新的算法,旨在加速质谱 (MS) 蛋白质组学中的开放修饰搜索 (OMS).
- 为了提高效率,利用整个光谱图书馆搜索管道的并行性.
- 为了能够在大规模的MS蛋白质组学数据集中发现新型,特别是具有意想不到的PTM的.
主要方法:
- 开发了一种使用超维计算的新型高度并行编码方法,以以最小的信息损失将光谱数据表示为超向量.
- 实施了HOMS-TC来并行处理现有级联搜索的两个阶段,有效地识别与已知的类似的光谱,同时考虑PTMs.
- 在图形处理单元 (GPU) 中加快了NVIDIA的张量核心单元上的HOMS-TC算法,以大幅提高性能.
主要成果:
- 与现有的OMS.替代搜索引擎相比,HOMS-TC的平均速度提高了31倍.
- 该算法的准确性与当前的竞争搜索工具相提并论,确保可靠的标识.
- 平行超维计算方法有效地管理了OMS固有的大型搜索空间.
结论:
- 在MS蛋白质组学中,HOMS-TC在MS蛋白质组学中开放修饰搜索 (OMS) 的速度和效率方面取得了重大进展.
- 该算法的并行架构和GPU加速使其适合分析不断增长的蛋白质组学数据规模.
- HOMS-TC促进了以前未知的和PTM的发现,扩大了蛋白质组分析的范围.
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