Integrated Expression Analysis and E-support using Bayesian Models for Affymetrix Exon and Gene Arrays

DNA微阵列同时测量数千个基因的表达。在过去的十年里，它们已经成为研究不同实验条件之间基因表达差异的标准技术，例如患有某些疾病的人和未受影响的人之间的差异。最新一代的Affymetrix微阵列提高了从基因水平到外显子水平的基因组表达检测的分辨率，使不同剪接变体的测量成为可能。目前，很少有工具可以利用这些Exon数组中包含的附加信息。该项目的目的是开发强大而可靠的外显子阵列分析方法，包括选择性剪接检测和基因表达分析。该软件将提供给研究人员下载并在他们自己的系统上使用。此外，来自微阵列实验的数据集可能非常大和复杂。这种高通量技术的使用给研究人员在数据分析和存储方面带来了挑战。许多生物学家与统计学家和生物信息学家没有直接的合作，这些统计学家和生物信息学家能够提供建议并进行数据分析。因此，需要随时可用的分析工具，其形式可供生物界使用。因此，本项目还旨在介绍在用户友好的分析环境中开发的分析方法，以及技术支持和培训软件。这项工作将建立在一个名为EMAAS的现有资源的基础上，该资源是为帝国理工学院生物信息学支持服务开发的微阵列数据分析和管理而创建的。这利用了网格计算机集群，允许通过用户友好的Web界面访问非常快速的数据分析。

DNA microarrays measure the expression of thousands of genes simultaneously. In the past decade they have become the standard technology for studying gene expression differences between different experimental conditions, for example between individuals suffering from certain diseases and unaffected individuals. The latest generation of Affymetrix microarrays have increased the resolution of genomic expression detection from the gene level down to the exon level, enabling measurement of different splice variants. Currently there are very few tools to take advantage of the additional information contained within these Exon arrays. The aim of this project is to develop powerful and robust methods for Exon array analysis including alternative splice detection and gene expression analyses. The software will be made available for researchers to download and use on their own sytem. In addition, data sets from microarray experiments can be very large and complex. The use of such high-throughput technology brings challenges for researchers in terms of data analysis and storage. Many biologists do not have direct collaboration with statisticians and bioinformaticians who are able to advise on and carry out data analysis. Thus there is a need for readily available analysis tools, in a form accessible to the biological community. Therefore this project also aims to present the developed analysis methods in a user friendly analysis environment, along with technical support and training software. The work will build on an existing resource called EMAAS, created for microarray data analysis and management which has been developed by the Bioinformatics Support Service at Imperial College. This utilises Grid clusters of computers which allows very fast data analysis, accessed via a user-friendly web interface.