Microarrays are broadly used in genomic analyses and find several applications in biology and medicine, providing a significant amount of data from a single experiment. Different kinds of microarrays are available which are identifiable by characteristics such as the type of probes, the surface used as support, and the method used for target detection. Although microarrays have been applied in many biological areas, their management, and investigation require advanced computational tools to speed up data analysis and at the same time make the interpretation of the results easier. To better deal with microarray datasets of large size, the development of analysis tools that are simple to use as well as able to produce accurate predictions, and of comprehensible models is essential. The tools have to provide an exhaustive collection of information to discriminate and identify SNPs, which are associated with the activity of particular genes affecting biological functions (e.g., a particular drug response), or involved in the development of complex diseases. The object of this chapter is to provide a review of software tools that are easy to use even for nonexperts of the domain, and that are able to efficiently deal with microarray data.
Keywords: Data mining; Genomics; Genotyping; Microarrays; Statistical analysis.