DNA microarray experimental design and software based data normalization and analysis

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Tool for the identification of differentially expressed genes using a user-defined threshold

Microarray and 2D gel experiments are used for the large scale measurement, and comparison of gene expression. Since these experiments generate large and complex amounts of data, a great challenge the researcher faces is trying to find ways to analyze this data. This paper focuses on the tool DiffExpress, which was designed to make the gene expression analysis process easier. One of the main features of DiffExpress is the user defined threshold which allows users to set their personal restriction of the expression change at which genes are differentially expressed. DiffExpress also makes use of graphs such as the Scatter Plot, Box and Whisker Plot and Volcano Plot for easier visualization of data

The application of gene expression profiling in the characterization of physiological effects of genetically modified feed components in rats

The study was conducted to evaluate the adequacy of expression profiling for the characterization of potential physiological side effects of genetically modified (GM) feed components. Feeding experiments with rats fed either GM or non-GM feed components were conducted and a comparative expression profiling, using DNA-chip-technology, was done. As a prerequisite for these expression studies data analysis parameters were optimized. Diet-associated expression differences between the two feeding groups were observed in spleen, small intestine and liver. It was shown that expression profiling provides great sensitivity in monitoring physiological reactions of an organism to such diets.In der vorliegenden Arbeit wurde die Eignung von "Genexpressionsprofiling" zur Charakterisierung physiologischer Nebeneffekte genetisch veränderter (GV) Futtermittel untersucht. In Fütterungsversuchen wurden Ratten entweder GV- oder Nicht-GV-Futterkomponenten verabreicht und ihre Expressionsprofile mittels DNA-chip-Technologie verglichen. Als Vorraussetzung für diese Expressionsanalysen erfolgte vorab eine Optimierung der Datenauswertung. In Milz, Leber und Dünndarm ergaben sich Diät assoziierte Expressionsunterschiede zwischen den Fütterungsgruppen. Insgesamt zeigte sich, dass „Genexpressionsprofiling“ eine hohe Sensitivität zur Erfassung physiologischer Reaktionen eines Organismus auf solche Diäten ermöglicht

Feature selection of microarray data using genetic algorithms and artificial neural networks

Microarrays, which allow for the measurement of thousands of gene expression levels in parallel, have created a wealth of data not previously available to biologists along with new computational challenges. Microarray studies are characterized by a low sample number and a large feature space with many features irrelevant to the problem being studied. This makes feature selection a necessary pre-processing step for many analyses, particularly classification. A Genetic Algorithm -Artificial Neural Network (ANN) wrapper approach is implemented to find the highest scoring set of features for an ANN classifier. Each generation relies on the performance of a set of features trained on an ANN for fitness evaluation. A publically-available leukemia microarray data set (Golub et al., 1999), consisting of 25 AML and 47 ALL Leukemia samples, each with 7129 features, is used to evaluate this approach. Results show an increased performance over Golub\u27s initial findings

Integrative analysis of high-throughput biological data: shrinkage correlation coefficient and comparative expression analysis

textThe focus for this research is to develop and apply statistical methods to analyze and interpret high-throughput biological data. We developed a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. This computational approach is not only applicable to DNA microarray analysis but is also applicable to proteomics data or any other high-throughput analysis methodology. The suppression of APY1 and APY2 in mutants expressing an inducible RNAi system resulted in plants with a dwarf phenotype and disrupted auxin distribution, and we used these mutants to discover what genes changed expression during growth suppression. We evaluated the gene expression changes of apyrase-suppressed RNAi mutants that had been grown in the light and in the darkness, using the NimbleGen Arabidopsis thaliana 4-Plex microarray, respectively. We compared the two sets of large-scale expression data and identified genes whose expression significantly changed after apyrase suppression in light and darkness, respectively. Our results allowed us to highlight some of the genes likely to play major roles in mediating the growth changes that happen when plants drastically reduce their production of APY1 and APY2, some more associated with growth promotion and others, such as stress-induced genes, more associated with growth inhibition. There is a strong rationale for ranking all these genes as prime candidates for mediating the inhibitory growth effects of suppressing apyrase expression, thus the NimbleGen data will serve as a catalyst and valuable guide to the subsequent physiological and molecular experiments that will be needed to clarify the network of gene expression changes that accompany growth inhibition.Institute for Cellular and Molecular Biolog

Dissection of the Complex Phenotype in Cuticular Mutants of Arabidopsis Reveals a Role of SERRATE as a Mediator

Mutations in LACERATA (LCR), FIDDLEHEAD (FDH), and BODYGUARD (BDG) cause a complex developmental syndrome that is consistent with an important role for these Arabidopsis genes in cuticle biogenesis. The genesis of their pleiotropic phenotypes is, however, poorly understood. We provide evidence that neither distorted depositions of cutin, nor deficiencies in the chemical composition of cuticular lipids, account for these features, instead suggesting that the mutants alleviate the functional disorder of the cuticle by reinforcing their defenses. To better understand how plants adapt to these mutations, we performed a genome-wide gene expression analysis. We found that apparent compensatory transcriptional responses in these mutants involve the induction of wax, cutin, cell wall, and defense genes. To gain greater insight into the mechanism by which cuticular mutations trigger this response in the plants, we performed an overlap meta-analysis, which is termed MASTA (MicroArray overlap Search Tool and Analysis), of differentially expressed genes. This suggested that different cell integrity pathways are recruited in cesA cellulose synthase and cuticular mutants. Using MASTA for an in silico suppressor/enhancer screen, we identified SERRATE (SE), which encodes a protein of RNA–processing multi-protein complexes, as a likely enhancer. In confirmation of this notion, the se lcr and se bdg double mutants eradicate severe leaf deformations as well as the organ fusions that are typical of lcr and bdg and other cuticular mutants. Also, lcr does not confer resistance to Botrytis cinerea in a se mutant background. We propose that there is a role for SERRATE-mediated RNA signaling in the cuticle integrity pathway