MOTIVATION: Reverse-phase protein arrays (RPPAs) allow sensitive quantification of relative protein abundance in thousands of samples in parallel. Typical challenges involved in this technology are antibody selection, sample preparation and optimization of staining conditions. The issue of combining effective sample management and data analysis, however, has been widely neglected. RESULTS: This motivated us to develop MIRACLE, a comprehensive and user-friendly web application bridging the gap between spotting and array analysis by conveniently keeping track of sample information. Data processing includes correction of staining bias, estimation of protein concentration from response curves, normalization for total protein amount per sample and statistical evaluation. Established analysis methods have been integrated with MIRACLE, offering experimental scientists an end-to-end solution for sample management and for carrying out data analysis. In addition, experienced users have the possibility to export data to R for more complex analyses. MIRACLE thus has the potential to further spread utilization of RPPAs as an emerging technology for high-throughput protein analysis. AVAILABILITY: Project URL: http://www.nanocan.org/miracle/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The microarray-based gene expression time-course experiments represent an important research design in biomedical studies. Current methods for analyzing microarray time-course data ignore individual differences in treatment responses or time-course patterns and have difficulties in handling missing values. We introduce the latent growth curve models popular in use in longitudinal epidemiology studies to analyze microarray time-course data with incomplete observations. The models have been applied to human in vivo irritated epidermis data with missing observations to investigate time-dependent global transcriptional responses to a chemical irritant. Our strategic data analysis identified significant time-course genes and selected the best fitting model for each of the genes on the array without pre-defining any parametric form. Various time-course patterns are then recognized from the significant genes using clustering methods including hierarchical clustering and self-organising map. Very high correlation between baseline expression and time-course response has been detected for many of the significant genes. The latent linear growth curve model can be applied to microarray-based gene expression time-course data with incomplete observations to find genes exhibiting various time-course responses. This method also estimates covariance between baseline gene activity and time-course response, providing useful additional information ignored in current gene expression analysis.