Briefings in Functional Genomics and Proteomics Advance Access published online on January 22, 2008
Briefings in Functional Genomics and Proteomics, doi:10.1093/bfgp/elm034
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Microarray data analysis and mining approaches
Corresponding author: Raffaele A. Calogero, Department of Clinical and Biological Sciences, University of Torino, Italy. Tel: +39 0116705417; Fax: +39 0119038639; E-mail: raffaele.calogero{at}unito.it
Microarray based transcription profiling is now a consolidated methodology and has widespread use in areas such as pharmacogenomics, diagnostics and drug target identification. Large-scale microarray studies are also becoming crucial to a new way of conceiving experimental biology. A main issue in microarray transcription profiling is data analysis and mining. When microarrays became a methodology of general use, considerable effort was made to produce algorithms and methods for the identification of differentially expressed genes. More recently, the focus has switched to algorithms and database development for microarray data mining. Furthermore, the evolution of microarray technology is allowing researchers to grasp the regulative nature of transcription, integrating basic expression analysis with mRNA characteristics, i.e. exon-based arrays, and with DNA characteristics, i.e. comparative genomic hybridization, single nucleotide polymorphism, tiling and promoter structure. In this article, we will review approaches used to detect differentially expressed genes and to link differential expression to specific biological functions.
Keywords: transcription, microarray, bioinformatics, statistics, pathways, transcription factors