It involves five-step exploratory approach to analyzing microarray data. The first step involves posing the biological question the experiment should address. Sample preparation is the next step, which includes experiments to be performed such as DNA/ RNA isolation, probe amplification, and microarray manufacture. The next step is the biochemical reactions between the target and probe molecules, for example, hybridization. The fourth step detects captured images from the microarray using scanners or imaging instruments. Finally, the captured images are analyzed and modeled.
Published in Chapter:
C-MICRA: A Tool for Clustering Microarray Data
Emmanuel Udoh (Sullivan University, USA) and Salim Bhuiyan (Indiana University–Purdue University, USA)
Copyright: © 2009
|Pages: 8
DOI: 10.4018/978-1-60566-242-8.ch061
Abstract
In the field of bioinformatics, small to large data sets of genes, proteins, and genomes are analyzed for biological significance. A technology that has been in the forefront of generating large amounts of gene data is the microarray or hybridization technique. It has been instrumental in the success of the human genome project and paved the way for a new era of genetic screening, testing, and diagnostics (Scheena, 2003). The microarray data set can be made of thousands of rows and columns. It often contains missing values, exhibits high-dimensional attributes, and is generally too large for manual management or examination (Tseng & Kao, 2005; Turner, Bailey, Krzanowski, & Hemingway, 2005). Database technology is necessary for the extraction, sorting, and analyzing of microarray data sets.