web cellHTS2: A web-application for the analysis of high-throughput screening data

<p>Abstract</p> <p>Background</p> <p>The analysis of high-throughput screening data sets is an expanding field in bioinformatics. High-throughput screens by RNAi generate large primary data sets which need to be analyzed and annotated to identify relevant phenotypic hits. Large-scale RNAi screens are frequently used to identify novel factors that influence a broad range of cellular processes, including signaling pathway activity, cell proliferation, and host cell infection. Here, we present a web-based application utility for the end-to-end analysis of large cell-based screening experiments by cellHTS2.</p> <p>Results</p> <p>The software guides the user through the configuration steps that are required for the analysis of single or multi-channel experiments. The web-application provides options for various standardization and normalization methods, annotation of data sets and a comprehensive HTML report of the screening data analysis, including a ranked hit list. Sessions can be saved and restored for later re-analysis. The web frontend for the cellHTS2 R/Bioconductor package interacts with it through an R-server implementation that enables highly parallel analysis of screening data sets. web cellHTS2 further provides a file import and configuration module for common file formats.</p> <p>Conclusions</p> <p>The implemented web-application facilitates the analysis of high-throughput data sets and provides a user-friendly interface. web cellHTS2 is accessible online at <url>http://web-cellHTS2.dkfz.de</url>. A standalone version as a virtual appliance and source code for platforms supporting Java 1.5.0 can be downloaded from the web cellHTS2 page. web cellHTS2 is freely distributed under GPL.</p

GenomeRNAi: a database for cell-based RNAi phenotypes

RNA interference (RNAi) has emerged as a powerful tool to generate loss-of-function phenotypes in a variety of organisms. Combined with the sequence information of almost completely annotated genomes, RNAi technologies have opened new avenues to conduct systematic genetic screens for every annotated gene in the genome. As increasing large datasets of RNAi-induced phenotypes become available, an important challenge remains the systematic integration and annotation of functional information. Genome-wide RNAi screens have been performed both in Caenorhabditis elegans and Drosophila for a variety of phenotypes and several RNAi libraries have become available to assess phenotypes for almost every gene in the genome. These screens were performed using different types of assays from visible phenotypes to focused transcriptional readouts and provide a rich data source for functional annotation across different species. The GenomeRNAi database provides access to published RNAi phenotypes obtained from cell-based screens and maps them to their genomic locus, including possible non-specific regions. The database also gives access to sequence information of RNAi probes used in various screens. It can be searched by phenotype, by gene, by RNAi probe or by sequence and is accessible a

GenomeRNAi: a database for cell-based RNAi phenotypes. 2009 update

The GenomeRNAi database (http://www.genomernai.org/) contains phenotypes from published cell-based RNA interference (RNAi) screens in Drosophila and Homo sapiens. The database connects observed phenotypes with annotations of targeted genes and information about the RNAi reagent used for the perturbation experiment. The availability of phenotypes from Drosophila and human screens also allows for phenotype searches across species. Besides reporting quantitative data from genome-scale screens, the new release of GenomeRNAi also enables reporting of data from microscopy experiments and curated phenotypes from published screens. In addition, the database provides an updated resource of RNAi reagents and their predicted quality that are available for the Drosophila and the human genome. The new version also facilitates the integration with other genomic data sets and contains expression profiling (RNA-Seq) data for several cell lines commonly used in RNAi experiments

sj-docx-1-tpx-10.1177_01926233221132747 – Supplemental material for Toxicologic Pathology Forum*: A Roadmap for Building State-of-the-Art Digital Image Data Resources for Toxicologic Pathology in the Pharmaceutical Industry

Supplemental material, sj-docx-1-tpx-10.1177_01926233221132747 for Toxicologic Pathology Forum*: A Roadmap for Building State-of-the-Art Digital Image Data Resources for Toxicologic Pathology in the Pharmaceutical Industry by Xing-Yue Ge, Juergen Funk, Tom Albrecht, Merima Birkhimer, Moritz Gilsdorf, Matthew Hayes, Fangyao Hu, Pierre Maliver, Mark McCreary, Trung Nguyen, Fernando Romero-Palomo, Shanon Seger, Reina N. Fuji, Vanessa Schumacher and Ruth Sullivan in Toxicologic Pathology</p