What are they?

DNA microarrays consist in an ordered set of single strand DNA (probes) attached to a surface. Based on the pairing nature of DNA strands, if the complementary DNA (target) is present in a sample, probe and target will bind together (hybridise). In case the targets were labelled (by fluorescence or radioactivity), it is easy to detect the spots in the surface in which the hibidization did occur. Therefore, since the identity of the probes is known, the DNA molecules present in the sample are identified.

How are they done?

DNA microarrays are fabricated by means of robotics. The surface in which the probes are attached is usually glass, but sometimes nylon is used. There are different ways of attaching DNA molecules to the surface:

  • cDNAs of variable lengths are attached to the surface, which has been coated with poly-L-lysine, that provides the attaching point. These arrays are informally known as "spotted" arrays.

  • Oligonoucleotides from 20 to 80 bases length are sintetized in situ on the surface using photolithographic techniques. Alternatively, the oligos can be sintetized externally and then attached to the surface (Warrington et al., 2000).

Spotted arrays are used for studies involving expression profiles of the genes. Their major advantage is that allow the user to make custom arrays easily. Their major drawback is the quality of the clones themselves (Knight, 2001), and the need for more machinery.

Oligonucleotide arrays (known as DNA chips) are also used for expression profiles, but since the parallel synthesis of variants of the sequences is possible (i.e. in addition to the generation of a given sequence, all possible point mutations of that sequence can be synthetized simultaneously), they allow detection of polymorphisms (Chee et al., 1996) and better quality controls for the experiment (Warrington et al., 2000). DNA chips are manufactured by several companies. Their major advantage is a higher quality and reproducibility in the results, their major drawback that they allow very reduced customisation of the experiments.

In addition, macroarray techniques exist, that allow the placement of DNA probes on membranes. The main difference with microarrays is the size of the spots, being much larger in macroarrays.

The experiment

The experiment involves several steps. We will focus in the widely used expression profiling experiments. In brief:

  • In case of a spotted array, one starts selecting the clones to place on the array. The clones can be obtained from commercial vendors. The clones are printed on the chip using a spotter robot.

  • In case of a oligo array, this is usually bought from a supplier. Actually vendors provide chips representing many different choices, with several complete genomes between them (human genome included).

  • Target RNA is isolated, and cDNA is obtained and labelled, normally using fluorescent dyes. a reference state is used, such that the experiment is directed to measure the differences between a given experimental condition and the reference state (the ratio between the two). Therefore, two different samples are used with different labelling, one for the reference state and another for the experimental condition. Several different experimental conditions can be tested in this way (Schena and Davis, 2000).

  • Hybridization is carried on between the array and the labelled samples.

  • Image is obtained with a scanner robot, and the ratio between the two different fluorescent intensities is measured. That yield a quantification of the difference in the abundance of the transcripts between the two samples.

  • Analysis is performed on the image, such that groups of related spots are identified. Clustering algorithms are widely used for this purpose. The groups represent normally sets of genes that are correlated in expression levels.

  • Correlation with other information (genomics, proteomics, literature, etc.) is linked to the results obtained (Blaschke et al., 2001)

Clustering algorithms

Several different algorithms are being used for the purpose of the clustering. We can cite hierarchical clustering(Eisen et al., 1998), Self-Organizative maps (SOMs) (Tamayo et al., 1999), K-means, and growing self-organizing trees (Herrero et al., 2001). Ideally, a good clustering algorithm must offer different levels of clustering, robustness against noise and reduced computing time. This latter condition is becoming a must, since size of the arrays (number of probes) is steadily increasing.


DNA microarrays is a really powerful technique. Some of present or potential uses are listed below:

  • Gene discovery

  • Discovery of regulatory elements (Leemans et al., 2001)

  • Genetic network unveiling (Tavazoie et al., 1999)

  • Function prediction (Brown et al., 2000; Cummings and Relman, 2000)

  • Studies on cell cycle-related mechanisms (Ferea and Brown, 1999)

  • Diagnosis using disease-characteristic expression profiles: Cancer (Golub et al., 1999) (Scherf et al., 2000)

  • Disease progression and study: HIV

  • Pharmacogenomics: Drug response and finding (Wilson et al., 1999) (Debouck and Goodfellow, 1999; Evans and Relling, 1999)

  • Pathogenicity mechanisms: Study of infections and drug-induced alterations (Cummings and Relman, 2000)

  • Genomic content of new species: Comparison with known ones (Akman and Aksoy, 2001)

  • Genomic exploration (Hayward et al., 2000)


  • Akman, L., and Aksoy, S. (2001). A novel application of gene arrays: Escherichia coli array provides insight into the biology of the obligate endosymbiont of tsetse flies. Proc Natl Acad Sci USA 98, 7546-7551.

  • Blaschke, C., Oliveros, J. C., and Valencia, A. (2001). Mining functional information associated with expression arrays. Funct Integr Genomics 1, 256-268.

  • Brown, M. P., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C. W., Furey, T. S., Ares, M. J., and Haussler, D. (2000). Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci USA 97, 262-267.

  • Chee, M., Yang, R., Hubbell, E., Berno, A., Huang, X. C., Stern, D., Winkler, J., Lockhart, D. J., Morris, M. S., and Fodor, S. P. (1996). Accessing genetic information with high-density DNA arrays. Science 274, 610-614.

  • Cummings, C. A., and Relman, D. A. (2000). Using DNA microarrays to study host-microbe interactions. Emerg Infect Dis 6, 513-525.

  • Debouck, C., and Goodfellow, P. N. (1999). DNA microarrays in drug discovery and development. Nat Genet 21, 48-50.

  • Eisen, M. B., Spellman, P. T., Brown, P. O., and Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95, 14863-14868.

  • Evans, W. E., and Relling, M. V. (1999). Pharmacogenomics: translating functional genomics into rational therapeutics. Science 286, 487-491.

  • Ferea, T. L., and Brown, P. O. (1999). Observing the living genome. Curr Opin Genet Dev 9, 715-722. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531-537.

  • Hayward, R. E., DeRisi, J. L., Alfadhli, S., Kaslow, D. C., Brown, P. O., and Rathod, P. K. (2000). Shotgun DNA microarrays and stage-specific gene expression in Plasmodium falciparum malaria. Mol Microbiol 35, 6-14.

  • Herrero, J., Valencia, A., and Dopazo, J. (2001). A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics 17, 126-136.

  • Knight, J. (2001). When the chips are down. Nature 410, 860-861.

  • Leemans, R., Loop, T., Egger, B., He, H., Kammermeier, L., Hartmann, B., Certa, U., Reichert, H., and Hirth, F. (2001). Identification of candidate downstream genes for the homeodomain transcription factor Labial in Drosophila through oligonucleotide-array transcript imaging. Genome Biol 2, research0015.0011-0015.0019.

  • Schena, M., and Davis, R. W. (2000). Technology standards for microarray research. In Microarray Biochip Technology, M. Schena, ed. (Natick, MA, BioTechniques Books), pp. 1-18.

  • Scherf, U., Ross, D. T., Waltham, M., Smith, L. H., Lee, J. K., Tanabe, L., Kohn, K. W., Reinhold, W. C., Myers, T. G., Andrews, D. T., et al. (2000). A gene expression database for the molecular pharmacology of cancer. Nat Genet 24, 236-244.

  • Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E. S., and Golub, T. R. (1999). Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 96, 2907-2912.

  • Tavazoie, S., Hughes, J. D., Campbell, M. J., Cho, R. J., and Church, G. M. (1999). Systematic determination of genetic network architecture. Nat Genet 22, 281-285.

  • Warrington, J. A., Dee, S., and Trulson, M. (2000). Large-scale genomic analysis using Affymetrix GeneChip probe arrays. In Microarray Biochip Technology, M. Schena, ed. (Natick, MA, BioTechniques Books), pp. 119-148.

  • Wilson, M., DeRisi, J., Kristensen, H. H., Imboden, P., Rane, S., Brown, P. O., and Schoolnik, G. K. (1999). Exploring drug-induced alterations in gene expression in Mycobacterium tuberculosis by microarray hybridization. Proc Natl Acad Sci USA 96, 12833-12838.

2002 ALMA Bioinformatics, SL. All rights reserved.