CPA-ACR: Parallel Algorithms and Software for Large Scale Microarry Data Analysis and Gene Network Inference

CPA-ACR：大规模微阵列数据分析和基因网络推理的并行算法和软件

• 批准号: 0811804
• 负责人: Srinivas Aluru
• 金额: $ 37.5万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811804&HistoricalAwards=false
• 关键词: CPA; ACR; Parallel; Algorithms; Software

High-throughput gene expression profile measurements enabled by microarrays have spawned significant advances in functional genomics and systems biology. The vast numbers of cumulative microarray experiments conducted over the past decade have generated a wealth of expression data available from several public repositories. While algorithms for microarray data analysis and gene network inference have been well studied, most available methods and programs are sequential and cannot scale up to analyzing large number of experiments due to both memory and time constraints.In this project, the investigators will develop high performance, parallel computational methods for large-scale gene expression analysis and gene network inference utilizing tens of thousands of microarray experiments available in public repositories. The primary research goal is to develop capability to simultaneously analyze the entire gamut of gene expression data available for an organism, and make biological discoveries and build robust, accurate networks which would not be possible through limited, compartmentalized analysis. The research will be carried out using gene expression profiles of the plant Arabidopsis thaliana, a well-studied model organism and the focus of the decade-long NSF Arabidopsis 2010 initiative. The investigators will develop 1) parallel algorithms for biclustering large gene expression matrices, 2) parallel algorithms for inferring gene networks using Mutual Information and Bayesian approaches, and 3) methods for querying and analyzing large-scale biological networks. The project will be led by an interdisciplinary team of investigators whose expertise spans parallel algorithms, scalable computing and software development, bioinformatics and systems biology, statistical analysis, microarray experimental techniques and analysis, and organism specific knowledge of Arabidopsis. It will lead to the development of advanced computational methods and open source software programs in systems biology.

由微阵列实现的高通量基因表达谱测量已经在功能基因组学和系统生物学方面产生了重大进展。在过去十年中进行的大量累积微阵列实验产生了从几个公共储存库获得的丰富的表达数据。虽然微阵列数据分析和基因网络推断的算法已经得到了很好的研究，但现有的大多数方法和程序都是顺序的，由于内存和时间的限制，无法扩展到分析大量实验。在这个项目中，研究人员将利用公共资源库中的数万个微阵列实验，开发高性能的并行计算方法，用于大规模的基因表达分析和基因网络推断。主要的研究目标是发展同时分析有机体可用的全部基因表达数据的能力，并做出生物学发现并建立强大、准确的网络，这是有限的、区隔的分析所不可能实现的。这项研究将利用植物拟南芥的基因表达谱进行，拟南芥是一种研究得很好的模式生物，也是NSF拟南芥2010倡议的重点。研究人员将开发1)双聚类大型基因表达矩阵的并行算法，2)使用互信息和贝叶斯方法推断基因网络的并行算法，以及3)查询和分析大规模生物网络的方法。该项目将由一个跨学科的研究小组领导，他们的专业知识涵盖并行算法、可伸缩计算和软件开发、生物信息学和系统生物学、统计分析、微阵列实验技术和分析，以及关于拟南芥的特定生物体知识。它将导致系统生物学中先进计算方法和开放源码软件程序的发展。