Automated Reports for Monitoring and Improving Data Quality in a Translational Research Network.

Standardised, automated quality reports were generated at three pilot locations of the decentralized translational research network DKTK with separated local data warehouses (LDW), for assessing syntactic conformity against common data element definitions deposited in a central metadata repository (MDR). Deviations in the LDW were categorised, and locally corrected. Comparisons of reports from two time points confirm a major improvement in data quality in terms of syntactic conformity, an essential prerequisite for network-wide data integration.

A Method to Use Metadata in Legacy Web Applications: The Samply.MDR.Injector.

Whenever medical data is integrated from multiple sources, it is regarded good practice to separate data from information about its meaning, such as designations, definitions or permissible values (in short: metadata). However, the ways in which applications work with metadata are imperfect: Many applications do not support fetching metadata from externalized sources such as metadata repositories. In order to display human-readable metadata in any application, we propose not to change the application, but to provide a library that makes a change to the user interface. The goal of this work is to provide a way to "inject" the meaning of metadata keys into the web-based frontend of an application to make it "metadata aware".

GenomeRNAi: a database for cell-based and in vivo RNAi phenotypes, 2013 update.

RNA interference (RNAi) represents a powerful method to systematically study loss-of-function phenotypes on a large scale with a wide variety of biological assays, constituting a rich source for the assignment of gene function. The GenomeRNAi database (http://www.genomernai.org) makes available RNAi phenotype data extracted from the literature for human and Drosophila. It also provides RNAi reagent information, along with an assessment as to their efficiency and specificity. This manuscript describes an update of the database previously featured in the NAR Database Issue. The new version has undergone a complete re-design of the user interface, providing an intuitive, flexible framework for additional functionalities. Screen information and gene-reagent-phenotype associations are now available for download. The integration with other resources has been improved by allowing in-links via GenomeRNAi screen IDs, or external gene or reagent identifiers. A distributed annotation system (DAS) server enables the visualization of the phenotypes and reagents in the context of a genome browser. We have added a page listing 'frequent hitters', i.e. genes that show a phenotype in many screens, which might guide on-going RNAi studies. Structured annotation guidelines have been established to facilitate consistent curation, and a submission template for direct submission by data producers is available for download.

R spider: a network-based analysis of gene lists by combining signaling and metabolic pathways from Reactome and KEGG databases.

R spider is a web-based tool for the analysis of a gene list using the systematic knowledge of core pathways and reactions in human biology accumulated in the Reactome and KEGG databases. R spider implements a network-based statistical framework, which provides a global understanding of gene relations in the supplied gene list, and fully exploits the Reactome and KEGG knowledge bases. R spider provides a user-friendly dialog-driven web interface for several model organisms and supports most available gene identifiers. R spider is freely available at http://mips.helmholtz-muenchen.de/proj/rspider.

Patterns of somatic mutation in human cancer genomes.

Cancers arise owing to mutations in a subset of genes that confer growth advantage. The availability of the human genome sequence led us to propose that systematic resequencing of cancer genomes for mutations would lead to the discovery of many additional cancer genes. Here we report more than 1,000 somatic mutations found in 274 megabases (Mb) of DNA corresponding to the coding exons of 518 protein kinase genes in 210 diverse human cancers. There was substantial variation in the number and pattern of mutations in individual cancers reflecting different exposures, DNA repair defects and cellular origins. Most somatic mutations are likely to be 'passengers' that do not contribute to oncogenesis. However, there was evidence for 'driver' mutations contributing to the development of the cancers studied in approximately 120 genes. Systematic sequencing of cancer genomes therefore reveals the evolutionary diversity of cancers and implicates a larger repertoire of cancer genes than previously anticipated.

Short postoperative survival for glioblastoma patients with a dysfunctional Rb1 pathway in combination with no wild-type PTEN.

PURPOSE: Glioblastoma (GB, WHO grade IV) is the most common primary brain tumor in adults. Survival is typically <1 year but varies between a few months and a couple of years. The aim of the study was to find novel genetic prognostic factors in a well-defined GB series. EXPERIMENTAL DESIGN: The survival data on 129 GBs were correlated with the results of a detailed analysis of 9 genes. These included 3 genes coding for proteins in the p53 pathway (i.e., TP53, p14(ARF), and MDM2), 4 in the Rb1 pathway (i.e., CDKN2A, CDKN2B, RB1, and CDK4), as well as PTEN and epidermal growth factor receptor. RESULTS: We found that abnormalities in any of the four genes (CDKN2A, CDKN2B, RB1, and CDK4) coding for components of the Rb1 pathway were associated with shorter survival (P = 0.002). In combination with loss of wild-type PTEN, the association was even stronger (P < 0.001), the median survival being 166 days as compared with the group without these abnormalities where the median postoperative survival was 437 days. The survival difference remained statistically significant in Cox' regression analysis adjusting for age (P = 0.012). CONCLUSIONS: The findings indicate that knowledge of the molecular genetic abnormalities in GBs provides important data in assessing individual patients. As additional advances in our understanding of the molecular genetics and cell biology of gliomas are made, in addition to providing prognostic information, such data may also provide targets for innovative therapy in the individual case.