Abstract
Gene Expression Comparative Analysis allows bio-informatics researchers to discover the functional regulation of genes. This is achieved through comparisons between data-sets representing the quantities of substances in a biological system. Unnatural variations can be introduced during the data collection and digitization process so normalization algorithms must be applied to data before any accurate comparison can be made. There exist many different normalization methods each of which gives a different result. Comparing differently normalized datasets can allow for discovery of crucial regulated genes that may be otherwise hidden due to errors in a single normalization study. In this paper we introduce a web-based software package called EXP-PAC which makes use of a high performance computing platform of computer clusters to run multiple normalization methods in parallel. By generating multiple normalized datasets concurrently, we allow researchers the ability to improve the accuracy of their research with almost no extra time-cost.
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Church, P., Wong, A., Goscinski, A., Lefèvre, C. (2010). Harnessing Clusters for High Performance Computation of Gene Expression Microarray Comparative Analysis. In: Hsu, CH., Yang, L.T., Park, J.H., Yeo, SS. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2010. Lecture Notes in Computer Science, vol 6082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13136-3_19
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DOI: https://doi.org/10.1007/978-3-642-13136-3_19
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