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

高通量基因表达谱的测量，使微阵列产生了显着的进步，功能基因组学和系统生物学。在过去的十年中进行了大量的累积微阵列实验，产生了丰富的表达数据，可从几个公共存储库。虽然微阵列数据分析和基因网络推理的算法已经得到了很好的研究，但由于内存和时间的限制，大多数可用的方法和程序都是顺序的，无法扩展到分析大量的实验。并行计算方法的大型规模基因表达分析和基因网络推理利用数以万计的微阵列实验可在公共仓库。主要研究目标是开发同时分析生物体可用的整个基因表达数据的能力，并进行生物学发现，建立强大，准确的网络，这是通过有限的，分区的分析不可能实现的。这项研究将使用植物拟南芥的基因表达谱进行，拟南芥是一种经过充分研究的模式生物，也是NSF拟南芥2010年计划的重点。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人员团队领导，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，以及拟南芥的生物体特定知识。它将导致系统生物学中先进的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.