DNA Chips and Microarrays

Abstract

Parallel analyses of thousands of molecules based on hybridization to nucleotides are carried out on microarrays. The large amount of data generated from scanning such arrays needs bioinformatic handling in the form of noise filtering, calibration and normalization to be ready for experimental comparison with other arrays. This may provide insight into regulatory pathways and lead to reclassification of diseases.

Keywords: DNA chips; microarrays; gene expression; SNP arrays; arrays

Figure 1.

A leukemia cell line was treated with 12‐O‐tetradecanoylphorbol‐13‐acetate (TPA) and harvested after 24 and 72 h. Total RNA was isolated and analyzed on HuGeneFL (Human full length) arrays (Affymetrix). The (a) 24‐h and (b) 72‐h data were compared with untreated control samples using Affymetrix software (Microarray Suite), and subsequently the data were coupled to the Gene Microarray Pathway Profiler (see Web Links section). The numbers to the right of the gene boxes indicate the absolute ‘fold change’ (FC) compared with untreated controls. The color of the boxes is set after criteria defined by the individual scientist. In this case, the criteria are a combination of FC and the ‘difference calls’ reported by the Microarray Suite software: red: decreased; green: increased; yellow: no change or a change below ±2. P: phosphate.

Figure 2.

Single nucleotide polymorphism (SNP)‐based analysis of allelic imbalance in human bladder tumors, revealing areas on the p arm of chromosome 6 that are lost (or gained) in invasive tumors. Each number above the columns identifies a patient. In each pair of columns, the column to the left is the first superficial tumor (stage T1), the column to the right is the first muscle‐invasive tumor (stage T2) in a disease course. The columns indicate chromosome 6p, and each band is the location of an SNP assayed by the Affymetrix microarray.

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References

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Further Reading

Alizadeh AA, Eisen MB, Davis RE, et al. (2000) Distinct types of diffuse large B‐cell lymphoma identified by gene expression profiling. Nature 403: 503–511.

Brazma A, Hingamp P, Quackenbush J, et al. (2001) Minimum information about a microarray experiment (MIAME): toward standards for microarray data. Nature Genetics 29: 365–371.

Celis JE, Kruhøffer M, Gromova I, et al. (2000) Gene expression profiling: monitoring transcription and translation products using DNA microarrays and proteomics. FEBS Letters 25 480(1): 2–16.

Cesareni G (ed.) (2000) Functional genomics. FEBS Letters 480(1) (special issue). Also published online: http://www.elsevier.com/febs/show/special_iss.htt.

Halushka MK, Fan JB, Bentley K, et al. (1999) Patterns of single‐nucleotide polymorphisms in candidate genes for blood‐pressure homeostasis. Nature Genetics 22: 239–247.

Theilgaard‐Monch K, Cowland J and Borregaard N (2001) Profiling of gene expression in individual hematopoietic cells by global mRNA amplification and slot blot analysis. Journal of Immunological Methods 252: 175–189.

Wang DG, Fan JB, Siao CJ, et al. (1998) Large‐scale identification, mapping, and genotyping of single‐nucleotide polymorphisms in the human genome. Science 280: 1077–1082.

Zembutsu H, Ohnishi Y, Tsunoda T, et al. (2002) Genome‐wide cDNA microarray screening to correlate gene expression profiles with sensitivity of 85 human cancer xenografts to anticancer drugs. Cancer Research 62: 518–527.

Web Links

Gene MicroArray Pathway Profiler (GenMAPP). Developed by the Conklin laboratory, Gladstone Institutes, University of California at San Francisco http://www.genmapp.org

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How to Cite close
Orntoft, Torben F, and Kruhøffer, Mogens(Jan 2006) DNA Chips and Microarrays. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1038/npg.els.0005675]