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New face of the “RNA world”

100%

Tyczewska A. , Figlerowicz M.

nr 2

For a very long time, RNA was considered just the medium by which information flows from DNA into the cell. The model proposed in the 1960s assumed that proteins are the main products and regulators of the gene expression process. In this context, the results of the Human Genome Project and the discoveries of RNA interference and small regulatory RNAs (srRNAs) came as a true surprise. The first ones demonstrated that less than 5% of the human genome encodes proteins. The second showed that RNA, especially 20-30 nt-long molecules should be placed among the most important factors controlling gene expression. srRNAs are capable of affecting the release and flow of genetic information in many different ways. They can induce changes in the genome structure, inhibit transcription, mediate mRNA degradation and repress translation. Interestingly, in different organisms, different pathways are used to regulate gene expression. It has recently been estimated that, in humans, the expression of 35-40% of genes is controlled by srRNA. As a result, RNA is currently believed to be a central molecule in many biological processes.

Mikromacierze DNA

80%

Kisiel A. , Skąpska A. , Markiewicz W. T. , Figlerowicz M.

tom 53

nr 3-4

295-303

Summary The breakthrough in DNA sequencing technology and completion of sequencing of first eucaryotic genomes raised the need for high-throughput methods of gene function analysis. To solve this problem the DNA microarray technology has been developed. It is based on traditional transcript profiling methods which use the hybridization ability of DNA and RNA to monitor gene expression. Due to miniaturisation and the use of fluorescent dyes DNA microarrays allow for simultaneous monitoring of the expression of tens of thousands genes. There are two main types of DNA microarrays: cDNA microarrays and oligonucleotide microarrays, also called DNA chips. In the former, several- hundred-nucleotide long cDNA probes are printed on a glass plate and hybridized to a fluorescently labeled target cDNA obtained from the tissue of interest. In the latter, type of microarrays, each gene is represented by several short oligonucleotides (about 30 nt), perfectly matching the target gene, and several oligomers with a single mismatch. Oligonucleotides are usually synthetised on a glass slide using the photolithographic technique and the slide is hybridized to fluorescently labeled target RNA. Analysis ofmicroarray data includes the comparison of gene expression in the experimental and control probes or the comparison of gene expression profiles obtained in several experiments, by different clustering methods. Medicine is one of the fields where DNA microarrays have already found practical application. At present they are especially useful in cancer research. DNA microarray analysis of tumor tissues allows to differentiate among various cancer types, to prognose the illness progress and plan the therapy. DNA microarrays are also an irreplaceable tool in a search for new drugs.

Impact of DNA microarray data transformation on gene expression analysis - comparison of two normalization methods

61%

Schmidt M. T. , Handschuh L. , Zyprych J. , Szabelska A. , Olejnik-Schmidt A. K. , Siatkowski I. , Figlerowicz M.

nr 4

573-580

Two-color DNA microarrays are commonly used for the analysis of global gene expression. They provide information on relative abundance of thousands of mRNAs. However, the generated data need to be normalized to minimize systematic variations so that biologically significant differences can be more easily identified. A large number of normalization procedures have been proposed and many softwares for microarray data analysis are available. Here, we have applied two normalization methods (median and loess) from two packages of microarray data analysis softwares. They were examined using a sample data set. We found that the number of genes identified as differentially expressed varied significantly depending on the method applied. The obtained results, i.e. lists of differentially expressed genes, were consistent only when we used median normalization methods. Loess normalization implemented in the two software packages provided less coherent and for some probes even contradictory results. In general, our results provide an additional piece of evidence that the normalization method can profoundly influence final results of DNA microarray-based analysis. The impact of the normalization method depends greatly on the algorithm employed. Consequently, the normalization procedure must be carefully considered and optimized for each individual data set.