Microarray comparison

Al Moustafa, A.E., Alaoui-Jamali, M.A., Batist, G., Hernandes-Perez, M., Serruya, C., Alpert, L., Black, M.J., Sladek, R., and Foulkes, W.D., Identification of genes associated with head and neck carcinogenesis by cDNA microarray comparison between matched primary normal epithelial and squamous carcinoma cells. Oncogene 21, 2634—2640, 2002. 

Lindroos K, Liljedahl U, Raitio M, Syvanen AC. Minisequencing on oligonucleotide microarrays comparison of immobilisation chemistries. Nucleic Acids Res 2001 29 e69. 

Taniguchi, M. Miura, K. Iwao, H. Yamanaka, S. Quan-titaive assessment of DNA microarrays - comparison with northern blots. Genomics 2001, 71, 34-39. 

Taniguchi M, Miura K, Iwao H, Yamanaka S. Quantitative assessment of DNA microarrays-comparison with Northern blot analyses. Genomics 2001 71 34-39. 

Natsoulis G, El Ghaoui L, Lanckriet GR, Tolley AM, Leroy F, Dunlea S, et al. Classification of a large microarray data set algorithm comparison and analysis of drug signatures. Genome Res 2005 15 724-36. 

Figure 10.1 Experimental schemes for microarray analysis. All experimental schemes start with a separation step of the cell lysate by velocity sedimentation in a sucrose gradient (top scheme). Collection of the desired fractions is assisted by a continuous ultraviolet (UV) reading of the gradient (an example of such UV reading is shown in each section). This allows determination of the sedimentation position of the 40S, 60S, 80S, and polyribosomal complexes (2,3, and more).Three general ways for fraction collection and analysis are presented (sections A, B, and C) (A) Collection of two fractions (free and polysomes) and direct comparison between them, with the free mRNA fraction labeled with green dye and the polysome fraction labeled with red dye. (B) Collection of two fractions and indirect comparison between them by utilizing an unfractionated reference RNA. (C) Collection of multiple fractions (four in this case), where each fraction is compared to an unfractionated reference sample. The blue arrows indicate the addition of spike-in RNA to each fraction and to the reference RNA.

In an indirect comparison design, the free and polysome samples are labeled with a red fluorescent dye, and each is hybridized independently to a DNA microarray against an RNA sample that is labeled with green fluorescent dye (Fig. 10.IB). Because the green-labeled sample is the same for both samples, it serves as a common reference and the polysome-to-free ratio can... 

In experiments where spike-in controls are added, validation is simpler because one can perform a northern analysis for one of the spiked-in RNAs and use their signals to normalize any differences between fractions. This allows direct comparison between northern blot and microarray results. 

Angenendt P., Gloekler J., Murphy D., Lehrach H., Cahill D.J., Toward optimized antibody microarrays a comparison of current microarray support materials, Anal Biochem. 2002 309 253-260. 

Manduchi, E., Scearce, M., Brestelli, J.E., Grant, G.R., Kaestner, K.H., and Stoeckert, C.J. (2002) Comparison of different labeling methods for two-channel high-density microarray experiments. Physiol. Genom. 10, 169-179. 

Bioresorbable polymers, 3 735-740 Bioselective adsorption, 6 387 Biosensors, 3 794-815 14 154 22 269 affinity DNA biosensors, 3 805-808 affinity immunosensors, 3 800-805 applications, 3 812-813 biomimetic sensors, 3 809-810 catalytic, 3 796-799 cellulose ester applications, 5 408 comparison with microarrays, J6 38It evolution of, 16 380-381 production by thick-film technology, 3 810-812... 

Unlike other methods currently employed for quantitative transcript measurements, including cDNA microarrays and real-time RT-PCR, competitive RT-PCR is amenable to quality control, which is critical for clinical diagnostic and pharmaceutical industry applications. Furthermore, microarray approaches are limited to generating snap-shot like profiles, but they do not control for differences in hybridization efficiencies of different gene probes with their corresponding cDNAs. That is, cross comparisons are relative and not absolute. Real-time PCR has gained acceptance recently largely due to the reduced cost associated... 

Normalization of cDNA microarray data is a very important step in the process of data analysis. With current technology, systematic hias is unavoidable and must he dealt with in a sensible manner. Furthermore, normalization methods need to be consistently apphed to all raw data. Using different normalization methods on different datasets may introduce bias and thereby decrease the validity of the data. Normahzed data should be free of systematic bias and should thereby provide a truer representation of the biological variance. Furthermore, normahzed data increases the validity of shde to shde comparisons. 

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