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BioINFO 103 - Gene Expression Microarray Analysis

We are made out of approximately 6 X10^13 cells, and each cell has two sets of ~ 3.2 billion nucleotides that makes our DNA. Approximately 5% of in DNA contains genes, which expressed and transcribed as RNA,  determines the identity and function of each cell, and of the entire organism. RNA expression profiling is paramount for discovering functional correlation between genes in normal and diseased state. The 20000 genes expressed generate an overwhelming amount of data that needs to be placed in a biological context, and sorted out in a meaningful, reproducible manner. In this section we will explore practical aspects of microarray analysis, using as pivot TM4 Microrarray Software Suite. Since very elaborate and expensive experiments have placed their raw data-sets in public databases such as OMNIBUS database or  Array Express, using Open Source tools we are going to reanalyze data from few top notch, best designed experiments. We will try as first step to reproduce the authors analysis and results, then we will formulate new hypothesis and re-explore the same data. In order to identify patterns in gene expression you are going to apply clustering algorithms (e.g. hierarchical clustering, k-means clustering) or neural-network-based divisive clustering such as self organizing maps (SOM), principal component analysis and other modern techniques. This course is hands-on, directed to newcomers that needs functional usage of the software. This is not a statistic course, having good handle of the application will allow you to explore multiple facets of analysis, explore your own data with ease and confidence, and allow more productive interaction with bioinformatics professionals and statisticians. We want to help you gain confidence and break the barrier into bioinformatics.

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