Preprocessing and Analysis Tools for Contemporary Microarray Applications

描述（由申请人提供）：微阵列是强大的高通量基因组学工具的一个例子，它正在彻底改变生物系统的测量。在这项技术和其他技术中，需要许多关键步骤将原始测量转换为生物学家和临床医生所依赖的结果。这些数据操作对最终测量和依赖于它们的研究的质量有巨大的影响。我们的团队先前已经证明，相对于设计人员和技术制造商引入的临时程序，使用现代统计方法可以大大提高基因表达测量的准确性和精度。许多公司已经将我们的方法整合到他们的数据分析软件中（例如genesspring， GeneTraffic）。微阵列现在被用于测量各种高基因组端点，包括基因型、染色体异常（包括缺失/插入）、蛋白质结合位点、甲基化和选择性剪接。在每种情况下，基因组的测量单位是短的寡核苷酸，称为探针。如果对这些测量的偏差和方差没有适当的理解，基于探针分析的生物学推断将受到损害。在这些新技术中，我们希望我们提出的研究能够产生统计方法，促进类似于表达式数组所获得的改进。近年来，随着微阵列技术新用途的迅速扩展，对这类研究的需求急剧增长。我们的长期目标是通过使用改进的统计方法来提高使用微阵列实验获得的结果的质量。为了实现这一目标，目前的提案有以下具体目标：为最流行的新兴应用开发基本分析工具，开发预处理方法以满足用户群体最迫切的需求，并开发用于人口广泛热点检测的通用统计方法。

DESCRIPTION (provided by applicant): Microarrays are an example of powerful high throughput genomics tools that are revolutionizing the measurement of biological systems. In this and other technologies, a number of critical steps are required to convert the raw measures into the results relied upon by biologists and clinicians. These data manipulation have enormous influence on the quality of the ultimate measurements and studies that rely upon them. Our group has previously demonstrated that the use of modern statistical methodology can substantially improve accuracy and precision of gene expression measurements, relative to ad-hoc procedures introduced by designers and manufacturers of the technology. Various companies have now incorporated our methods into their data analysis software (e.g. GeneSpring, GeneTraffic). Microarrays are now being used to measure diverse high genomic endpoints including genotype, chromosomal abnormalities including deletions/insertions, protein binding sites, methylation, and alternative splicing. In each case, the genomic units of measurement are short oligonucleotides referred to as probes. Without appropriate understanding of the bias and variance of these measurements, biological inferences based upon probe analysis will be compromised. In these new technologies, we expect our proposed research to produce statistical methods that facilitate improvements similar to those attained with expression arrays. The need for more research of this kind has grown dramatically in recent years, with the rapid expansion of novel uses of the microarray technology. Our long-term goal is to improve the quality of results obtained using microarray experiments via the use of improved statistical methodology. Toward this goal, the current proposal has the following specific aims: to develop basic analysis tools for the most popular emerging applications, to develop preprocessing methodology to serve the most urgent needs of the user community, and to develop general statistical methodology for population wide hot-spot detection.