Parallel Algorithms for Big Data from Mass Spectrometry based Proteomics

项目总结/摘要 该项目的目标是开发核心算法、技术和软件库 为基于质谱分析（MS）的应用提供可扩展、高效和并行的计算解决方案 高通量蛋白质组学数据集。为了使更大的蛋白质组学社区和 实验生物学家该项目旨在1）确定一套核心方法， 2）开发高效和可扩展的并行算法， 3）追求将这些并行计算技术映射到 各种各样的架构，如多核、众核、分布式集群、GPU和 FPGA的4）设计和实现可用于我们HPC的大数据分析技术 实现以及其他研究人员用于顺序和/或并行算法5） 使用Galaxy框架为这些并行程序设计接口，以便它们可以被 非专家和不熟悉并行处理的人。这项研究将 与系统生物学和蛋白质组学领域专家合作进行。具体 将针对的问题是MS数据集的聚类的并行算法，并行 使用来自这些MS数据集的数据库，使用多核和 GPU和高性能算法，可以使这些MS数据集的意义在一个新的 不需要数据库。并行算法将使用模拟进行测试 以及真实的实验数据集，并将可供免费学术使用。

Project Summary/Abstract The goal of the proposed project is to develop core algorithms, techniques and software libraries to enable scalable, efficient and parallel computing solutions for mass spectrometry (MS) based high-throughput proteomics data sets. To empower the larger proteomics community and experimental biologist the project seeks to 1) identify a set of core methods that are frequently used by proteomics practitioners 2) develop efficient and scalable parallel algorithms and implementations for these methods 3) pursue mapping of these parallel computing techniques to a wide variety of architectures such as multicores, manycores, distributed clusters, GPU’s and FPGA’s 4) design and implement big data analytic techniques that can be used in our HPC implementation as well as used by other researchers for sequential and/or parallel algorithms 5) design interfaces using Galaxy framework for these parallel programs so that they can be used by non-experts and people who are not familiar with parallel processing. The research will be conducted in collaboration with domain experts in systems biology and proteomics. The specific problems that will be targeted are parallel algorithms for clustering of MS data sets, parallel algorithms for identifying peptides using databases from these MS data sets using multicore and GPU’s and high performance algorithms that can make sense out of these MS data sets in a denovo fashion without a need for a database. The parallel algorithms will be tested using simulated as well as real experimental data sets and will be available for free academic use.