Challenges in microarray data management and analysis

Microarray is a key technology in genomics and is increasingly used in molecular biology as well as in molecular medicine and clinical applications. The availability of different microarray types and vendors and the increasing number of samples forming microarray studies pose new challenges in the management and analysis of microarray data. The paper recalls main microarray types and goals and discusses the most important challenges in microarray management and analysis, including the following issues. Heterogeneity in microarray data format requires the application of different preprocessing and annotation tools and the use of different, vendor-specific, preprocessing libraries. Multiplicity of microarray types (e.g. gene expression, SNPs and miRNA arrays, to cite a few) requires the application of different analysis tools. The application of various preprocessing steps produces a number of different files for each study, such as raw, preprocessed, annotated and eventually filtered data, that must be managed and stored properly. Finally, the increasing volume of microarray data due to the use of large sets of samples poses further challenges for the storage of such data. Finally, some emerging directions to face such challenges are also described.