Parallel Algorithms for Big Data from Mass Spectrometry based Proteomics

项目摘要/摘要 拟议项目的目标是开发核心算法、技术和软件库 为基于质谱仪(MS)的可扩展、高效和并行计算解决方案提供支持 高通量蛋白质组学数据集。为更大的蛋白质组学社区提供支持 实验生物学家该项目寻求1)确定一套经常被 供蛋白质组学从业者使用2)开发高效且可扩展的并行算法和 这些方法的实现3)追求将这些并行计算技术映射到 多种架构，如多核、多核、分布式集群、GPU和 4)设计和实现可用于我们的高性能计算的大数据分析技术 实现以及其他研究人员对顺序和/或并行算法的使用5) 使用Galaxy框架为这些并行程序设计接口，以便 非专家和不熟悉并行处理的人。这项研究将是 与系统生物学和蛋白质组学领域的专家合作进行。具体的 将针对的问题是MS数据集的并行算法，并行 使用这些MS数据集中的数据库识别多肽的算法使用多核和 GPU和高性能算法，可以从这些MS数据集中获得有意义的数据 时尚，而不需要数据库。并行算法将使用模拟AS进行测试 以及真实的实验数据集，并将供免费学术使用。

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.