Parallel Algorithms for Big Data from Mass Spectrometry based Proteomics

基于质谱的蛋白质组学大数据并行算法

基本信息

批准号：
9301702
负责人：
Ajay Gupta
金额：
$ 41.85万
依托单位：
WESTERN MICHIGAN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2021-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9301702
关键词：
Algorithm Design Algorithms Architecture Big Data Bioinformatics Biology Career Choice Collaborations Communities Complex Computational Biology Computational Science Computer software Computers Data Data Analyses Data Analytics Data Set Databases Development Engineering Environment Evaluation Galaxy Generations Goals Grant Graph High Performance Computing Libraries Mass Spectrum Analysis Medicine Memory Methods Modernization Peptides Performance Protein Databases Proteomics Research Research Personnel Route Running Sampling Scientist Seminal Software Design Students System Systems Biology Techniques Testing Time base big biomedical data cluster computing computer science cost design experimental study foot high throughput analysis mass spectrometer medical schools parallel computer parallel processing parallelization petabyte portability programs protein aminoacid sequence tool

项目摘要

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.

项目概要/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 (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并实验生物学家该项目旨在 1）确定一组经常使用的核心方法由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，例如多核、众核、分布式集群、GPU 和 FPGA 4) 设计和实现可在我们的 HPC 中使用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，以便它们可以被使用非专家和不熟悉并行处理的人将进行研究。与系统生物学和蛋白质组学领域专家合作进行。将针对的问题是 MS 数据集聚类的并行算法、并行使用多核和这些 MS 数据集中的数据库识别肽的算法 GPU 和高性能算法可以从头开始理解这些 MS 数据集不需要数据库的时尚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。