Fast parallel tandem mass spectral library searching using GPU hardware acceleration.
Fast parallel tandem mass spectral library searching using GPU hardware acceleration.
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DOI：
10.1021/pr200074h
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发表时间：
2011-06-03
影响因子：
4.4
通讯作者：
Martin, Daniel B.
中科院分区：
文献类型：
--
作者：
Baumgardner, Lydia Ashleigh;Shanmugam, Avinash Kumar;Lam, Henry;Eng, Jimmy K.;Martin, Daniel B.
基于质谱的蛋白质组学是一门成熟的生物学研究学科，正在经历实质性的增长。随着更快、更灵敏的仪器收集更大的数据文件，仪器随着时间的推移而稳步改进。因此，将肽片段化模式与其序列匹配的计算过程，传统上通过序列数据库搜索来完成，最近也通过光谱库搜索来完成，已经成为许多质谱实验中的瓶颈。在这两种方法中，主要的速率限制步骤是将获得的光谱与来自光谱库或序列数据库的所有潜在匹配进行比较。这是一个高度并行化的过程，因为核心计算元素可以表示为两个向量的简单但算术密集的乘法。在本文中，我们提出了一个概念验证项目，利用图形处理单元（GPU）上的大规模并行计算，分布和加速使用光谱库搜索的光谱分配过程。这个程序，我们命名为FastPaSS（快速频谱搜索）是在NVIDIA的CUDA（计算统一设备架构）中实现的，它允许直接访问NVIDIA GPU中的处理器。通过在CUDA环境下实现经过验证的光谱搜索算法SpectraST，我们的努力证明了GPU计算用于光谱分配的可行性。
Mass spectrometry-based proteomics is a maturing discipline of biologic research that is experiencing substantial growth. Instrumentation has steadily improved over time with the advent of faster and more sensitive instruments collecting ever larger data files. Consequently, the computational process of matching a peptide fragmentation pattern to its sequence, traditionally accomplished by sequence database searching and more recently also by spectral library searching, has become a bottleneck in many mass spectrometry experiments. In both of these methods, the main rate limiting step is the comparison of an acquired spectrum with all potential matches from a spectral library or sequence database. This is a highly parallelizable process because the core computational element can be represented as a simple but arithmetically intense multiplication of two vectors. In this paper we present a proof of concept project taking advantage of the massively parallel computing available on graphics processing units (GPUs) to distribute and accelerate the process of spectral assignment using spectral library searching. This program, which we have named FastPaSS (for Fast Parallelized Spectral Searching) is implemented in CUDA (Compute Unified Device Architecture) from NVIDIA which allows direct access to the processors in an NVIDIA GPU. Our efforts demonstrate the feasibility of GPU computing for spectral assignment, through implementation of the validated spectral searching algorithm SpectraST in the CUDA environment.
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