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

通过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取得了重大进展。在过去十年中进行的大量累积微阵列实验已经从几个公共存储库中产生了丰富的表达数据。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，但大多数可用的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，研究人员将开发高性能的并行计算方法，用于大规模基因表达分析和基因网络推断，利用公共存储库中数万个微阵列实验。主要的研究目标是发展同时分析一个生物体的整个基因表达数据范围的能力，并使生物学发现和建立强大的，准确的网络，这是不可能通过有限的，划分的分析。这项研究将利用拟南芥（Arabidopsis thaliana）的基因表达谱进行，拟南芥是一种得到充分研究的模式生物，也是长达十年的美国国家科学基金会拟南芥2010计划的重点。研究人员将开发1)用于大型基因表达矩阵双聚类的并行算法，2)用于利用互信息和贝叶斯方法推断基因网络的并行算法，以及3)用于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团队领导，他们的专业知识涵盖并行算法、可扩展计算和软件开发、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，以及拟南芥的生物特异性知识。它将导致系统生物学中先进计算方法和开源软件程序的发展。

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.