Microarrays are extensively used in genomic research and have several ranges of applications in biology and medicine, providing a large amount of data. Several different kinds of microarrays are available, distinguishable by characteristics such as the kind of probes, the surface support used, and the method employed for the target detection. Although microarrays have been developed and applied in many biological contexts, the management and investigation, require advanced computational tools to speed up data analysis and the interpretation of the results. To better deal with microarray data sets with characteristics is the huge dimension, the development of easy to use analysis tools as well as to produce accurate predictions, and comprehensible models arise. The object of this paper is to provide a review of software tool easy to use even from not expert of the domain, as well as able to efficiently deal with microarray data to derive a set of information to discriminate and identify SNPs, associated with genes related with particular drug response, phenotypes and complex diseases developing.