FPGA supercomputing technology for high-throughput identification and quantitation in proteomics

用于蛋白质组学高通量识别和定量的 FPGA 超级计算技术

• 批准号: BB/F004893/1
• 负责人: Daniel Coca
• 金额: $ 45.42万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FF004893%2F1
• 关键词: FPGA; supercomputing; technology; throughput; identification

Proteomics is the study of the entire complement of a cell in a particular state. It is the proteins that 'act out' the information in the genome, and we cannot really understand cellular function without a detailed knowledge of the activity, dynamics and interplay between the 'actors'. .However, the science and technology of proteomics does not lend itself to the same highly multiplexed approaches that can be applied to nucleic acids, and strategies for protein identification and quantification are still highly serial, require complex and sometimes arcane data processing, and are slow. We have almost completed a proof-of-concept BBSRC e-science project that aimed to implement two common methods in proteomics: mass spectrum preprocessing and peptide mass fingerprint database searching, as a hardware implementation using reconfigurable computer chips known as field programmable gate arrays (FPGAs). A key feature of this computational platform is that the bioinformatics algorithms which are normally implemented as a software program were translated into optimized digital hardware processors that could process data significantly faster by running multiple analyses in parallel. The successful outcome of this project was a complete implementation that has achieved a phenomenal 2000-fold speed increase. We now wish to build on our previous success, capitalize upon the capabilities we have developed thus far, and deliver similar speed gains to the most commonly used method of proteome analysis, based on tandem mass spectrometry. At the same time, we will address an emergent and pressing need for faster and enhanced quantification to deliver new quantitative approaches and capabilities to proteomics researchers. Such tools are critical if proteomics is to deliver what we expect of it as a science.

蛋白质组学是对特定状态细胞的整个补体的研究。正是蛋白质“阐明”基因组中的信息，如果没有详细了解“参与者”之间的活动，动力学和相互作用，我们就无法真正理解细胞功能。但是，蛋白质组学的科学和技术并不适合可应用于核酸的高度多重方法，蛋白质识别和定量的策略仍然高度序列，需要复杂的，有时甚至是神秘的数据处理，并且很慢。我们几乎完成了概念证明的BBSRC电子科学项目，该项目旨在实施蛋白质组学中的两种常见方法：质谱预处理和肽质量指纹数据库搜索，作为硬件实现，是一种可重新配置的计算机芯片，称为现场可编程门阵列（FPGAS）。该计算平台的一个关键功能是，通常将其作为软件程序实现的生物信息学算法被转化为优化的数字硬件处理器，通过并行运行多个分析，可以更快地处理数据。该项目的成功结果是一个完整的实施，它实现了2000倍速度的惊人速度。现在，我们希望以我们先前的成功为基础，利用迄今为止我们已经开发的能力，并基于串联质谱法提供了与最常用的蛋白质组分析方法相似的速度增长。同时，我们将解决对更快和增强的量化的紧迫和紧迫的需求，以向蛋白质组学研究人员提供新的定量方法和能力。如果蛋白质组学是将我们作为一门科学提供的期望，那么此类工具至关重要。

项目成果

FPGA Implementation of Database Search Engine for Protein Identification by Peptide Fragment Fingerprinting

肽片段指纹识别蛋白质数据库搜索引擎的 FPGA 实现

• 作者: Daniel Coca (Author)

Reconfigurable computing solution for Peptide Mass Fingerprinting

用于肽质量指纹图谱的可重构计算解决方案

• 作者: I. Bogdan (Author)

Proteome Bioinformatics

• DOI: 10.1007/978-1-60761-444-9
• 发表时间: 2010-01-01
• 期刊: PROTEOME BIOINFORMATICS