Alternative splicing of premessenger RNA is an important layer of regulation in eukaryotic gene expression. Splice variation of a large number of genes has been implicated in various cell growth and differentiation processes. To measure tissue-specific splicing of genes on a large scale, we collected gene expression data from 11 rat tissues using a high-density oligonucleotide array representing 1600 rat genes. Expression of each gene on the chip is measured by 20 pairs of independent oligonucleotide probes. Two algorithms have been developed to normalize and compare the chip hybridization signals among different tissues at individual oligonucleotide probe level. Oligonucleotide probes (the perfect match [PM] probe of each probe pair), detecting potential tissue-specific splice variants, were identified by the algorithms. The identified candidate splice variants have been compared to the alternatively spliced transcripts predicted by an EST clustering program. In addition, 50% of the top candidates predicted by the algorithms were confirmed by RT-PCR experiment. The study indicates that oligonucleotide probe-based DNA chip assays provide a powerful approach to detect splice variants at genome scale

The role of thermal unfolding as it pertains to thermodynamic properties of proteins and their stability has been the subject of study for more than 50 years. Moreover, exactly how the unfolding properties of a given protein system may influence the kinetics of aggregation has not been fully characterized. In the study of recombinant human Interleukin-1 receptor type II (rhuIL-1R(II)) aggregation, data obtained from size exclusion chromatography and differential scanning calorimetry (DSC) were used to model the thermodynamic and kinetic properties of irreversible denaturation. A break from linearity in the initial aggregation rates as a function of 1/T was observed in the vicinity of the melting transition temperature (T(m) approximately 53.5 degrees C), suggesting significant involvement of protein unfolding in the reaction pathway. A scan-rate dependence in the DSC experiment testifies to the nonequilibrium influences of the aggregation process. A mechanistic model was developed to extract meaningful thermodynamic and kinetic parameters from an irreversibly denatured process. The model was used to simulate how unfolding properties could be used to predict aggregation rates at different temperatures above and below the T(m) and to account for concentration dependence of reaction rates. The model was shown to uniquely identify the thermodynamic parameters DeltaC(P) (1.3 +/- 0.7 kcal/mol-K), DeltaH(m) (74.3 +/- 6.8 kcal/mol), and T(m) with reasonable variances.

Effective analysis of high throughput screening (HTS) data requires automation of dose-response curve fitting for large numbers of datasets. Datasets with outliers are not handled well by standard non-linear least squares methods, and manual outlier removal after visual inspection is tedious and potentially biased. We propose robust non-linear regression via M-estimation as a statistical technique for automated implementation. The approach of finding M-estimates by Iteratively Reweighted Least Squares (IRLS) and the resulting optimization problem are described. Initial parameter estimates for iterative methods are important, so self-starting methods for our model are presented. We outline the software implementation, done in Matlab and deployed as an Excel application via the Matlab Excel Builder Toolkit. Results of M-estimation are compared with least squares estimates before and after manual editing.

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An important aspect of the drug development process is prediction of efficacious and toxic side effects. Profiling of mRNA expression is a powerful approach to analyze the molecular phenotype of cells under various conditions, for example, in response to stimulation by compounds. We attempt to explore the approach of using expression profiling to identify patterns or fingerprints that are correlated with specific drug properties or behaviors. Identification of such expression patterns may also lead to revelation of the potential action mechanism of drugs and fingerprints indicative of certain drug efficacy or side effects. We describe here a strategy that was used to identify a set of genes whose differential expression pattern correlates with activation mode and target specificity of a specific group of drug compounds.