A Fully Automated Method for Noisy cDNA Microarray Image Quantification

DNA microarray is an innovative tool for gene studies in biomedical research, and its applications can vary from cancer diagnosis to human identification. Image processing is an important aspect of microarray experiments, the primary purpose of the image analysis step is to extract numerical foreground and background intensities for the red and green channels for each spot on the microarray. The background intensities are used to correct the foreground intensities for local variation on the array surface, resulting in corrected red and green intensities for each spot that can be considered as a primary data for subsequent analysis. Most techniques divide the overall microarray image processing into three steps: gridding, segmentation, and quantification. In this paper, a  simple automated gridding technique is developed with a great effect on noisy microarray images. A segmentation technique based on ‘edge-detection’ is applied to identify the spots and separate the foreground from the background is known as microarray image segmentation. Finally, a quantification technique is used to calculate the gene expression level from the intensity values of the red and green components of the image. Results revealed that the developed methods can deal with various kinds of noisy microarray images, with high  griddingaccuracy of 92.2% for low quality images and 100% for high quality images resulting in better spot quantification to get more accurate gene expression values.Â

Quantitative trait loci underlying gene product variation: a novel perspective for analyzing regulation of genome expression.

A methodology to dissect the genetic architecture of quantitative variation of numerous gene products simultaneously is proposed. For each individual of a segregating progeny, proteins extracted from a given organ are separated using two-dimensional electrophoresis, and their amounts are estimated with a computer-assisted system for spot quantification. Provided a complete genetic map is available, statistical procedures allow determination of the number, effects and chromosomal locations of factors controlling the amounts of individual proteins. This approach was applied to anonymous proteins of etiolated coleoptiles of maize, in an F2 progeny between two distant lines. The genetic map included both restriction fragment length polymorphism and protein markers. Minimum estimates of one to five unlinked regulatory factors were found for 42 of the 72 proteins analyzed, with a large diversity of effects. Dominance and epistasis interactions were involved in the control of 38% and 14% of the 72 proteins, respectively. Such a methodology might help understanding the architecture of regulatory networks and the possible adaptive or phenotypic significance of the polymorphism of the genes involved.