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  • 1
    Keywords: GENE ; GENES ; DESIGN ; Drosophila ; DATABASE ; INTERFERENCE ; RNA INTERFERENCE ; ALIGNMENT ; SCREENS ; DROSOPHILA CELLS
    Abstract: 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.
    Type of Publication: Journal article published
    PubMed ID: 19910367
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  • 2
    Keywords: proliferation ; CELL ; CELL-PROLIFERATION ; Germany ; PATHWAY ; screening ; PROTEIN ; INFECTION ; BIOLOGY ; SIGNALING PATHWAY ; INTERFACE ; signaling ; INTERFERENCE ; secretion ; ANNOTATION ; methods ; high throughput ; STATISTICAL-METHODS ; SCREENS ; HIT SELECTION ; RNAI SCREENS
    Abstract: Background: 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. Results: 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. Conclusions: The implemented web-application facilitates the analysis of high-throughput data sets and provides a user-friendly interface. web cellHTS2 is accessible online at http://web-cellHTS2.dkfz.de. 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
    Type of Publication: Journal article published
    PubMed ID: 20385013
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  • 3
    Keywords: COMBINATION ; Germany ; ALGORITHM ; TOOL ; GENE ; MOTIFS ; TRANSCRIPTION FACTOR ; BINDING ; SEQUENCES ; DISCOVERY ; IDENTIFICATION ; PROMOTER ; DATABASE ; REGION ; INTERFACE ; PREDICTION ; W2H ; FRAMEWORK ; ANNOTATION ; Motif discovery
    Abstract: There are many tools available for the prediction of potential promoter regions and the transcription factor binding sites (TFBS) harboured by them. Unfortunately, these tools cannot really avoid the prediction of vast amounts of false positives, the greatest problem in promoter analysis. The combination of different methods and algorithms has shown an improvement in prediction accuracy for similar biological problems such as gene prediction. The web-tool presented here uses this approach to perform an exhaustive integrative analysis, identification and annotation of potential promoter regions. The combination of methods employed includes searches in different experimental promoter databases to identify promoter regions and their orthologs, use of TFBS databases and search tools, and a phylogenetic footprinting strategy, combining multiple alignment of genomic sequences together with motif discovery tools that were tested previously in order to get the best method combination. The pipeline is available for academic users at the HUSAR open server "http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar/" . It integrates all of this information and identifies among the huge number of TFBS predictions those, which are more likely to be potentially functional
    Type of Publication: Journal article published
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  • 4
    Keywords: INTERFERENCE ; genomics ; RESOURCES
    Abstract: 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.
    Type of Publication: Journal article published
    PubMed ID: 23193271
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  • 5
  • 6
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    German Medical Science GMS Publishing House; Düsseldorf
    In:  124. Kongress der Deutschen Gesellschaft für Chirurgie; 20070501-20070504; München; DOC07dgch7794 /20071001/
    Publication Date: 2007-10-02
    Keywords: ddc: 610
    Language: German
    Type: conferenceObject
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  • 7
    Keywords: EFFECTOR ; CPG-ISLANDS ; CONSTRUCTS ; RNAi
    Abstract: Use of transcription activator-like effector nucleases (TALENs) is a promising new technique in the field of targeted genome engineering, editing and reverse genetics. Its applications span from introducing knockout mutations to endogenous tagging of proteins and targeted excision repair. Owing to this wide range of possible applications, there is a need for fast and user-friendly TALEN design tools. We developed E-TALEN ( ext-link-type="uri" xlink:href="http://www.e-talen.org" xmlns:xlink="http://www.w3.org/1999/xlink"〉http://www.e-talen.org), a web-based tool to design TALENs for experiments of varying scale. E-TALEN enables the design of TALENs against a single target or a large number of target genes. We significantly extended previously published design concepts to consider genomic context and different applications. E-TALEN guides the user through an end-to-end design process of de novo TALEN pairs, which are specific to a certain sequence or genomic locus. Furthermore, E-TALEN offers a functionality to predict targeting and specificity for existing TALENs. Owing to the computational complexity of many of the steps in the design of TALENs, particular emphasis has been put on the implementation of fast yet accurate algorithms. We implemented a user-friendly interface, from the input parameters to the presentation of results. An additional feature of E-TALEN is the in-built sequence and annotation database available for many organisms, including human, mouse, zebrafish, Drosophila and Arabidopsis, which can be extended in the future.
    Type of Publication: Journal article published
    PubMed ID: 24003033
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