HiCOPS：基于质谱的组学数据万亿级数据库检索的高性能计算框架
HiCOPS: High Performance Computing Framework for Tera-Scale Database Search of Mass Spectrometry based Omics Data
摘要：
数据库搜索算法，从质谱（MS）数据推断肽，试图提高计算效率，以完成更大，更复杂的系统生物学研究。已知现有的串行和高性能计算（HPC）搜索引擎在其他方面非常成功，但随着现代非模型、多物种的基于MS的组学分析的复杂性增加所需的理论搜索空间的大小增加而表现出较差的可扩展性。因此，计算技术的瓶颈是在存储器或处理单元的层次结构之间移动数据的通信成本，而不是算术运算。这种后摩尔架构的变化，以及现代系统生物学实验的需求，削弱了现有HPC工作流程的整体有效性。我们提出了一种新的高效的并行计算方法，其内存分布式架构上的肽识别工具称为HiCOPS，使超过100倍的速度提高了大多数现有的HPC蛋白质组数据库搜索工具的实现。HiCOPS使超级计算数据库搜索概念能够在合理的时间范围内全面识别肽及其所有修饰形式。我们通过针对TB数据库搜索100字节的实验MS数据来证明这一点，其中HiCOPS使用72个并行节点（1728个核心）在几分钟内完成肽鉴定，而使用1个节点（24个核心）的现有最先进的工具需要几周; 100分钟vs 5周; 500倍加速。最后，我们制定了一个理论框架，我们的开销避免策略，并报告优越的性能评估结果的关键指标，包括执行时间，CPU利用率，加速和I/O效率。该软件将在hicops.github.io上提供
英文摘要：
Database-search algorithms, that deduce peptides from Mass Spectrometry (MS) data, have tried to improve the computational efficiency to accomplish larger, and more complex systems biology studies. Existing serial, and high-performance computing (HPC) search engines, otherwise highly successful, are known to exhibit poor-scalability with increasing size of theoretical search-space needed for increased complexity of modern non-model, multi-species MS-based omics analysis. Consequently, the bottleneck for computational techniques is the communication costs of moving the data between hierarchy of memory, or processing units, and not the arithmetic operations. This post-Moore change in architecture, and demands of modern systems biology experiments have dampened the overall effectiveness of the existing HPC workflows. We present a novel efficient parallel computational method, and its implementation on memory-distributed architectures for peptide identification tool called HiCOPS, that enables more than 100-fold improvement in speed over most existing HPC proteome database search tools. HiCOPS empowers the supercomputing database search concept for comprehensive identification of peptides, and all their modified forms within a reasonable time-frame. We demonstrate this by searching Gigabytes of experimental MS data against Terabytes of databases where HiCOPS completes peptide identification in few minutes using 72 parallel nodes (1728 cores) compared to several weeks required by existing state-of-the-art tools using 1 node (24 cores); 100 minutes vs 5 weeks; 500x speedup. Finally, we formulate a theoretical framework for our overhead-avoiding strategy, and report superior performance evaluation results for key metrics including execution time, CPU utilization, speedups, and I/O efficiency. The software will be made available at: hicops.github.io
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