Nazari, K., Karami, A., Mahdavi Amiri, N., Pourali, F. (2014). Microarray Data Analysis for Detection and Classification of Viral Infection. Journal of Applied Biotechnology Reports, 1(1), 22-27.
Khadijeh Nazari; Ali Karami; Nezameddin Mahdavi Amiri; Fateme Pourali. "Microarray Data Analysis for Detection and Classification of Viral Infection". Journal of Applied Biotechnology Reports, 1, 1, 2014, 22-27.
Nazari, K., Karami, A., Mahdavi Amiri, N., Pourali, F. (2014). 'Microarray Data Analysis for Detection and Classification of Viral Infection', Journal of Applied Biotechnology Reports, 1(1), pp. 22-27.
Nazari, K., Karami, A., Mahdavi Amiri, N., Pourali, F. Microarray Data Analysis for Detection and Classification of Viral Infection. Journal of Applied Biotechnology Reports, 2014; 1(1): 22-27.
Microarray Data Analysis for Detection and Classification of Viral Infection
1Faculty of Mathematics, Sharif University of Technology, Tehran, Iran
2Research Center of Molecular Biology, Baqiyatallah University of Medical Sciences, Tehran, Iran
Receive Date: 10 February 2014,
Revise Date: 14 March 2014,
Accept Date: 14 March 2014
Abstract
DNA microarrays consist of collection of DNA microscopic spots that In order to form an array attached to a solid surface such as glass, plastic or silicon chip. The pieces of fixed DNA considered as a searcher. In this technology it is possible to test sample against thousands probes for specific genes. With this ability, arrays accelerate the biological investigations, gene finding, molecular detection and disease diagnosis. Microarray technology can be seen as a continued development of southern blotting. The most important stage in this technology is data analysis. To analysis such large data whit high degree of confidence and reliability needs reliable bioinformatics tools. Infectious diseases still is major problem for human. One of the most important application of microarray technology is the possibility of testing for the presence of thousands micro-organism in environmental and clinical samples only in a single excrement. Thereby we take an important step in rapid and accurate detection of infectious diseases. Here, we present E-Predict algorithm and DetectiV package that is based on species identification in microarray. We demonstrate the application of E-Predict and DetectiV for viral detection in a large publicly available dataset and show that DetectiV performs better than E-Predict. DetectiV is implemented as a package for R - powerful, open source software for statistical programming - that containing visualization, normalization and significance testing functions.
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