MOTIVATION: In biomedical research transcriptomic, proteomic or metabolomic profiles of patient samples are often combined with genomic profiles from experiments in cell lines or animal models. Integrating experimental data with patient data is still a challenging task due to the lack of tailored statistical tools. RESULTS: Here we introduce guided clustering, a new data integration strategy that combines experimental and clinical high-throughput data. Guided clustering identifies sets of genes that stand out in experimental data while at the same time display coherent expression in clinical data. We report on two potential applications: The integration of clinical microarray data with (i) genome-wide chromatin immunoprecipitation assays and (ii) with cell perturbation assays. Unlike other analysis strategies, guided clustering does not analyze the two datasets sequentially but instead in a single joint analysis. In a simulation study and in several biological applications, guided clustering performs favorably when compared with sequential analysis approaches. AVAILABILITY: Guided clustering is available as a R-package from http://compdiag.uni-regensburg.de/software/guidedClustering.shtml. Documented R code of all our analysis is included in the Supplementary Materials. All newly generated data are available at the GEO database (GSE29700). CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: After the publication of JVirGel 1.0 in 2003 we got many requests and suggestions from the proteomics community to further improve the performance of the software and to add additional useful new features. RESULTS: The integration of the PrediSi algorithm for the prediction of signal peptides for the Sec-dependent protein export into JVirGel 2.0 allows the exclusion of most exported preproteins from calculated proteomic maps and provides the basis for the calculation of Sec-based secretomes. A tool for the identification of transmembrane helices carrying proteins (JCaMelix) and the prediction of the corresponding membrane proteome was added. Finally, in order to directly compare experimental and calculated proteome data, a function to overlay and evaluate predicted and experimental two-dimensional gels was included. AVAILABILITY: JVirGel 2.0 is freely available as precompiled package for the installation on Windows or Linux operating systems. Furthermore, there is a completely platform-independent Java version available for download. Additionally, we provide a Java Server Pages based version of JVirGel 2.0 which can be operated in nearly all web browsers. All versions are accessible at http://www.jvirgel.de