Abstract
Microarrays are one of the latest breakthroughs in experimental molecular biology that allow monitoring the expression levels of tens of thousands of genes simultaneously. Arrays have been applied to studies in gene expression, genome mapping, SNP discrimination, transcription factor activity, toxicity, pathogen identification and many other applications. In this paper we concentrate on discussing various bioinformatics tools used for microarray data mining tasks with its underlying algorithms, web resources and relevant reference. We emphasize this paper mainly for digital biologists to get an aware about the plethora of tools and programs available for microarray data analysis. First, we report the common data mining applications such as selecting differentially expressed genes, clustering, and classification. Next, we focused on gene expression based knowledge discovery studies such as transcription factor binding site analysis, pathway analysis, protein- protein interaction network analysis and gene enrichment analysis.
Highlights
Microarray is one such technology which enables the researchers to investigate and address issues which were once thought to be non traceable by facilitating the simultaneous measurement of the expression levels of thousands of genes [1, 2]
A microarray is a glass slide on which DNA molecules are fixed on an ordered manner at specific locations called spots or probes [3]
With the generation of large amounts of microarray data, it has become increasingly important to address the challenges of data quality and standardization related to this technology [4]
Summary
Computational data analysis tasks such as data mining which includes classification and clustering used to extract useful knowledge from microarray data. There are two common methods for in depth microarray data analysis, i.e. clustering and classification [6]. Cluster Analysis: Clustering is the most popular method currently used in the first step of gene expression data matrix analysis It is used for finding co-regulated and functionally related groups [14]. The general data mining and machine learning application tools are used for classification tasks are illustrated in the Table 3 (see Supplementary material). Knowledge Discovery with Microarray Data: Classification, clustering and identification of differential genes can be considered as basic microarray data analysis tasks with gene expression profiles alone. The freely available software packages for gene enrichment [34] Subramanian A et al Proc Natl Acad Sci U S A. 2005 102(43): 15545 are illustrated in Table 6 (see Supplementary material)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
