Why microarrays in a bioinformatics class? Design of chips Quantitation of signals Integration of the data Extraction of groups of genes with linked expression profiles (gene clustering)

2 important topics requiring computers: Data Integration It’s important to link the data from the array experiment with other sequence databases (Genbank, SwissProt, etc). If the activity of a gene has changed, you want to be able to view pre-existing information about the gene in order to explain the experimental results. To exchange array data with other researchers, you need some standardized format.

Gene Clustering Group together genes with similar patterns of expression: Clustering can be thought of as forming a phylogenetic tree of genes or tissues. Genes are near each other on the "gene tree" if they show a strong correlation across experiments, and tissues are near each other on the "tissue tree" if they have similar gene expression patterns.

Important ?? This opens the possibility of identifying patterns of coregulation among genes, which, in turn, reflects underlying regulatory mechanisms and function interrelationships.

PNAS -- Alon et al. 96 (12): 6745 Pattern searching and gene clustering of promoter regions of Drosophila olfactory receptorsPattern searching and gene clustering of promoter regions of Drosophila olfactory receptors

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The End

Arrays: Narrower terms include bead arrays, bead based arrays, bioarrays, bioelectronic arrays, cDNA arrays, cell arrays, DNA arrays, gene arrays, gene expression arrays, genome arrays, high density oligonucleotide arrays, hybridization arrays, microelectronic arrays, multiplex DNA hybridization arrays, nanoarrays, oligonucleotide arrays, oligosaccharide arrays, protein arrays, solution arrays, spotted arrays, tissue arrays, exon arrays, filter arrays, macroarrays, small molecule microarrays, suspension arrays, tiling arrays, transcript arrays. Related terms include arrayed library. See also chips, microarrays.

To me, the most important thing that has happened in recent years is the rapid development of DNA chips. Technologies have allowed high- throughput ‘transcriptome’ analysis. That capability was introduced in the ’90s, but since then, it has become much more powerful as the genome project progressed. There are now many transcriptome centers already set up or being established. People are using this technology for mapping gene expression in cancer cells or across stages of development. Now, we’re going a step further, and we are seeing the introduction of pre- processing techniques — ways of sample preparation that let you selectively choose the proteins or the transcripts you want to look at. For example, you can say, “Give me all the proteins that bind to DNA.” You are using the technology to scan a subset of the whole genome. Protein expression is moving more slowly, but still in tandem with DNA chips.

Many microarray experiments are now being performed on human cells. Genome is completely sequenced and well annotated. Scientists are attempting to compare normal vs. diseased tissue.

How to build a Gene Chip Add nucleotides one by one to the growing end of an oligonucleotide, the sequence determined by the order in which the dNTPs are added to the reaction mix.  ? Modified dNTPs are added. dNTPs can not attach to another until they are light activated. The dNTPs are added one after another to the chip surface, and photolithography is used to direct pulses of light at individual positions in the array and determine which of the growing oligonucleotides will be extended by one unit.