SHF: Small: Collaborative Research: The Automata Programming Paradigm for Genomic Analysis

SHF：小型：协作研究：基因组分析的自动机编程范式

• 批准号: 1740583
• 负责人: Michela Becchi
• 金额: $ 23.47万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740583&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Automata

Inferring knowledge from genetic data will drive future advances in the life sciences. However, DNA sequences are being generated faster than they can be analyzed with existing computing technologies and algorithms. The core computations performed by many genomic applications involve pattern matching. This operation is normally implemented using automata-based algorithms and can be efficiently mapped onto non-general purpose platforms such as Field Programmable Gate Arrays (FPGA) and Micron?s recently announced Automata Processor (AP). However, the lack of high-level programming interfaces for these devices hampers their adoption in the bioinformatics community.This project fills this gap by developing novel programmatic descriptions of several genomic analyses and mapping them onto these two non-traditional architectures. The work advances the state-of-the-art in several ways. At an algorithmic level, new methods to address the biological problems of genome-scale orthology inference and regulatory motif search are being developed. At a computational abstraction level, the researchers are designing an extended finite automaton abstraction suitable to support diverse computations, and are mapping new and existing computational kernels onto it. At a hardware mapping level, automatic tuning techniques for the effective deployment of automata-based computations on FPGA and Micron?s AP are being developed. This interdisciplinary project will facilitate the adoption of FPGA and Micron?s Automata Processor by biologists by providing a new library of pattern matching routines and a high-level automata-based programming interface for these platforms. In addition, the researchers are developing instructional material in a variety of topics, such as genomic analysis, pattern matching, automata processing and high-performance computing. Finally, this project provides research opportunities and access to pre-production hardware to undergraduate and graduate students, interdisciplinary training, and technology transfer to industry. The results of this research will be made available through the release of software tools and publication in international conferences and journals.

从遗传数据中推断知识将推动生命科学的未来进步。 但是，与现有的计算技术和算法可以分析的DNA序列的生成更快。许多基因组应用执行的核心计算涉及模式匹配。该操作通常使用基于自动机的算法实施，并且可以有效地映射到非务必的目的平台上，例如现场可编程门阵列（FPGA）和Micron？s最近宣布的自动机处理器（AP）。但是，这些设备缺乏高级编程接口阻碍了它们在生物信息学界的采用。该项目通过对几种基因组分析的新型程序描述进行了新的差距，并将它们映射到这两种非传统体系结构上。这项工作以多种方式推进了最新的。在算法层面上，正在开发解决基因组尺度矫正推断和调节基序搜索的生物学问题的新方法。在计算抽象级别上，研究人员正在设计一种适合支持各种计算的扩展有限自动机抽象，并将新的和现有的计算内核映射到其中。在硬件映射级别上，正在开发基于自动机的计算有效部署自动调整技术。 这个跨学科项目将通过为这些平台提供新的模式匹配例程和高级自动机的编程界面来促进生物学家采用FPGA和Micron的自动机处理器。此外，研究人员正在开发各种主题的教学材料，例如基因组分析，模式匹配，自动机处理和高性能计算。最后，该项目为本科和研究生，跨学科培训以及技术转移到行业提供了研究机会和访问预生产硬件。这项研究的结果将通过在国际会议和期刊上发布软件工具和出版物提供。