Abstract
BackgroundThough microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists. Most microarray programs require users to have sophisticated knowledge of mathematics, statistics and computer skills for usage. With accumulating microarray data deposited in public databases, easy-to-use programs to re-analyze previously published microarray data are in high demand.ResultsEzArray 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 online with predefined or custom procedures. EzArray performs data preprocessing and detection of differentially expressed genes with statistical methods. All 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 executed scripts can be downloaded, 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.ConclusionEzArray is a novel Affymetrix expression array data analysis and sharing system. EzArray provides easy-to-use tools for re-analyzing published microarray data and will help both novice and experienced users perform initial analysis of their microarray data from the location of data storage. We believe EzArray will be a useful system for facilities with microarray services and laboratories with multiple members involved in microarray data analysis. EzArray is freely available from .
Highlights
Though microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists
Web-based systems based on R and Bioconductor packages are being developed, such as CARMAweb [3], MAGMA [4], GEPAS [5], Asterias [6], ArrayPipe [7], MIDAW [8], RACE [9], WebArray [10], and Expression Profiler [11]
EzArray system architecture We propose that an ideal microarray system should be easy-to-use for all levels of users, have minimal software and hardware requirements for installation and usage, have data privacy for each user and allow data sharing with others, be flexible to integrate custom tools, and provide maximum data analysis reproducibility
Summary
Though microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists. More and more laboratories have begun adopting Structured Query Language (SQL)based relational databases, such as Oracle and MySQL, to solve life sciences data management problems. Though microarray-based experiments are becoming popular in life science research, microarray data management and analysis are still challenging tasks for many biologists. Web-based systems based on R and Bioconductor packages are being developed, such as CARMAweb [3], MAGMA [4], GEPAS [5], Asterias [6], ArrayPipe [7], MIDAW [8], RACE [9], WebArray [10], and Expression Profiler [11] While these systems have made microarray data analysis much easier for experienced users, much improvement is needed to further automate the common data analysis processes so that they can be readily accessible to novice users
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