Development of a Highly Automated Microarray Data Analysis System That Allows

DESCRIPTION (provided by applicant): Though microarray experiments are very popular in life science researches, managing and analyzing microarray data are still challenging tasks for many biologists. Most microarray programs require users to obtain sophist icated knowledge of mathematics, statistics and computer skills for usage. There are very few programs providing highly automated data analysis from where the data are stored. With accumulating microarray data deposited in public databases, easy-to-use pro grams to re-analyze previously published microarray data are in high demand. We have developed EzArray which is a web-based Affymetrix expression array data management and analysis system for researchers who need to organize microarray data efficiently and get data analyzed instantly. EzArray organizes microarray data into projects that can be analyzed on-line with predefined or custom procedures. EzArray performs data preprocessing and detection of differentially expressed genes with statistical methods. A ll analysis procedures are optimized and highly automated so that even novice users with limited pre-knowledge of microarray data analysis can complete initial analysis quickly. Since all input files, analysis parameters, and the executed scripts can be do wnloaded, EzArray provides maximum reproducibility for each analysis. In addition, EzArray integrates with Gene Expression Omnibus (GEO) and allows instantaneous re-analysis of published array data. EzArray is a novel microarray data analysis and sharing s ystem. It represents the most autonomous Affymetrix expression array data analysis system currently available. EzArray provides easy-to-use tools for re-analyzing published microarray data and will help both novice and experienced users perform custom init ial analysis of their microarray data from the site of data storage. In summary, EzArray is a great system for facilities with microarray services and laboratories with multiple members involved in microarray data analysis. In this proposal, we propose to further improve EzArray and extend it to support other microarray platforms including Agilent, NimbleGen, and custom produced spotted cDNA arrays, enhance EzArray with new biostatistician algorithms including EBArrays, GOSim, Ringo, Codelink, CoXpress, BNA rray, and many more, and finally use EzArray to process published microarray data with new algorithms that previous authors had not tried yet. In order to make EzArray available for as many researchers as possible and considering the complexity of microarr ay data management and analysis, we propose to establish an industrial-strength, reliable EzArray server. Our server will have significant competitive advantages over current commercial products in terms of functionality, usability, and cost. Based on feed back of our preliminary version of EzArray, our projects in this application are highly recommended by both life sciences researchers and biostatisticians. One biostatistics professor is willing to contribute 5% effort to speed up our project progress. PUB LIC HEALTH RELEVANCE: We have developed EzArray which is a web-based Affymetrix expression array data management and analysis system for researchers who need to organize microarray data efficiently and get data analyzed instantly. In this proposal, we prop ose to 1) extend EzArray to support other microarray platforms including Agilent, NimbleGen, and custom produced spotted cDNA arrays, 2) enhance EzArray with new biostatistician algorithms including EBArrays, GOSim, Ringo, Codelink, CoXpress, BNArray, and many more, 3) use EzArray to process published microarray data with new algorithms that previous authors had not tried yet, and finally 4) Establish an industrial-strength, reliable EzArray server.