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  • 1
    Keywords: SPECTRA ; CELLS ; BLOOD ; CELL ; Germany ; human ; THERAPY ; POPULATION ; SITE ; SITES ; GENE ; GENES ; GENOME ; PROTEIN ; transcription ; METABOLISM ; MICE ; TRANSDUCTION ; TRANSPLANTATION ; SEQUENCE ; VECTOR ; HUMAN GENOME ; REGION ; REGIONS ; STEM-CELLS ; HEMATOPOIETIC-CELLS ; mutagenesis ; insertional mutagenesis ; cord blood ; INTEGRATION ; PROGRAM ; RE ; LENTIVIRAL VECTOR ; ligation-mediated PCR ; SEVERE COMBINED IMMUNODEFICIENCY ; technique ; function ; hematopoietic stem cell ; REPOPULATING CELLS ; RELEVANCE ; progenitor cell ; HUMAN-GENOME ; CORD ; TRANSCRIPTION START REGIONS ; BLOOD-GROUP ; cord blood progenitors cells ; I-HYPERSENSITIVE SITES ; lentiviral vector transduction ; RETROVIRAL GENE MARKING ; scid repopulating cell
    Abstract: Background Recent observations of insertional mutagenesis in preclinical and clinical settings emphasize the relevance of investigating comprehensively the spectrum of integration sites targeted by specific vectors. Methods We followed the engraftment of lentivirally transduced human cord blood (CB) progenitor cells after transplantation into.NOD/SCID mice using a self-inactivating HIV-1-derived vector expressing the enhanced green fluorescent protein (EGFP). Results The mean of transduction of CD34(+) CB cells was 41%, as deduced from the percentage of EGFP(+) cells before transplantation. At 3 weeks post-transplantation, the average of EGFP+ cells in the human cell population was 65 +/- 8%, and increased to 75 +/- 10% at 12 weeks post-transplantation. In order to determine the proviral integration sites in human NOD/SCID repopulating cells (SRCs) we used the ligation-mediated polymerase chain I reaction (LM-PCR) technique. Sixty-eight percent of the integrations were found to be located in RefSeq genes, most of them in intron regions. Twenty percent of these integrations occurred within a distance of 10 kb from the transcription start site; a percentage that is significantly lower compared to that observed in cells transduced by gammaretroviral vectors. Sixty-two percent of integrations occurred in genes with a biological function in cell metabolism, and four integrations were located in genes with a role in turnorigenesis. Conclusions These investigations indicate that integration of lentiviral vectors in human repopulating cells capable of engrafting NOD/SCID mice preferentially occur in coding regions of the human genome. Nevertheless, the clustering of integrations at the transcriptional start is not as high as that observed for gammaretroviral vectors. Copyright (c) 2006 John Wiley & Sons, Ltd
    Type of Publication: Journal article published
    PubMed ID: 16960916
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  • 2
    Keywords: EXPRESSION ; proliferation ; CELL-PROLIFERATION ; Germany ; INFORMATION ; screening ; GENE ; GENE-EXPRESSION ; GENES ; GENOME ; PROTEIN ; PROTEINS ; gene expression ; ASSAY ; DATABASE ; bioinformatics ; INTERFACE ; PROJECT ; INTEGRATION ; FEATURES ; RE ; cell proliferation ; FULL-LENGTH HUMAN ; HUMAN CDNAS ; ASSAYS ; genomic ; NORTHERN
    Abstract: LIFEdb (http://www.LIFEdb.de) integrates data from large-scale functional genomics assays and manual cDNA annotation with bioinformatics gene expression and protein analysis. New features of LIFEdb include (i) an updated user interface with enhanced query capabilities, (ii) a configurable output table and the option to download search results in XML, (iii) the integration of data from cell-based screening assays addressing the influence of protein-overexpression on cell proliferation and (iv) the display of the relative expression ('Electronic Northern') of the genes under investigation using curated gene expression ontology information. LIFEdb enables researchers to systematically select and characterize genes and proteins of interest, and presents data and information via its user-friendly web-based interface
    Type of Publication: Journal article published
    PubMed ID: 16381901
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  • 3
    Keywords: CELLS ; EXPRESSION ; Germany ; MODEL ; INFORMATION ; SYSTEM ; DISEASE ; GENE ; GENES ; GENOME ; HYBRIDIZATION ; microarray ; TISSUE ; DISCOVERY ; TARGET ; IDENTIFICATION ; PATTERNS ; DNA microarray ; DESIGN ; DATABASE ; TARGETS ; DIFFERENTIAL EXPRESSION ; EXPRESSED SEQUENCE TAGS ; biomarker ; THERAPEUTIC TARGETS ; EST ; KNOWLEDGEBASE ; TIGR GENE INDEXES ; Tissue ontology ; Tissue slims ; Tissue synonym library ; Tissue type ; Tissue-distribution pattern ; Tissue-distribution profiles
    Abstract: Tissue-distribution profiles are crucial for understanding the characteristics of cells and tissues in terms of their differential expression of genes. Most of the currently available resources for tissue-distribution profiles are either specialized for a few particular organisms, tissue types and disease stages or do not consider the "tissue ontology" levels for the calculation of the tissue-distribution profiles. Therefore, we have developed "TissueDistributionDBs", a repository of tissue-distribution profiles based on the expressed sequence tags (ESTs) data extracted from the UniGene database by employing "Tissue Ontology" available at BRENDA. To overcome the occurrence of the natural language variations in the EST's source tissue-type terms, we have generated a "tissue synonym library" and standardized these tissue-type terms by cross-referencing to the controlled vocabulary for tissue-type terms available at BRENDA "Tissue Ontology". Furthermore, we have provided a quantitative expression for genes among the tissue types at various anatomical levels by constructing "tissue slims". Concurrently, the expression among tissue types is used for tissue-distribution calculations. The resulting output profiles can be queried by the Sequence Retrieval System (SRS) and are currently available for 20 different model organisms. We benchmarked our database system against the Swissprot database using a set of 40 different tissue types. This database system is useful for the understanding of the tissue-specific expression patterns of genes, which have implications for the identification of possible new therapeutic drug targets, in gene discovery, and in the design and analysis of micro-arrays. TissueDistributionDBs can be accessed via the World Wide Web (www) at "http://genius.embnet.dkfz-heidelberg.de/menu/tissue_db/"
    Type of Publication: Journal article published
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  • 4
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    BMC Bioinformatics 4 (), Art. Nr.: 39- 
    Keywords: COMBINATION ; Germany ; human ; CDNA ; CLONING ; GENE ; DNA ; FLOW ; BIOLOGY ; SEQUENCE ; SEQUENCES ; FORM ; IDENTIFICATION ; DATABASE ; HUMAN GENOME ; bioinformatics ; US ; exon-intron structure ; INTERFACE ; PREDICTION ; W2H ; WEB
    Abstract: Background: In the last years several high-throughput cDNA sequencing projects have been funded worldwide with the aim of identifying and characterizing the structure of complete novel human transcripts. However some of these cDNAs are error prone due to frameshifts and stop codon errors caused by low sequence quality, or to cloning of truncated inserts, among other reasons. Therefore, accurate CDS prediction from these sequences first require the identification of potentially problematic cDNAs in order to speed up the posterior annotation process.Results: cDNA2Genome is an application for the automatic high-throughput mapping and characterization of cDNAs. It utilizes current annotation data and the most up to date databases, especially in the case of ESTs and mRNAs in conjunction with a vast number of approaches to gene prediction in order to perform a comprehensive assessment of the cDNA exon-intron structure. The final result of cDNA2Genome is an XML file containing all relevant information obtained in the process. This XML output can easily be used for further analysis such us program pipelines, or the integration of results into databases. The web interface to cDNA2Genome also presents this data in HTML, where the annotation is additionally shown in a graphical form. cDNA2Genome has been implemented under the W3H task framework which allows the combination of bioinformatics tools in tailor-made analysis task flows as well as the sequential or parallel computation of many sequences for large-scale analysis.Conclusions: cDNA2Genome represents a new versatile and easily extensible approach to the automated mapping and annotation of human cDNAs. The underlying approach allows sequential or parallel computation of sequences for high-throughput analysis of cDNAs
    Type of Publication: Journal article published
    PubMed ID: 12964951
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  • 5
    Keywords: Germany ; CLASSIFICATION ; INFORMATION ; TOOL ; SITE ; CLONING ; GENOME ; PROTEIN ; PROTEINS ; TIME ; SEQUENCE ; SIGNAL ; VARIANTS ; ASSAY ; DATABASE ; LOCALIZATION ; PREDICTION ; SELECTION ; REJECTION ; SEQUENCE-ANALYSIS ; HUMAN GENES ; FUNCTIONAL GENOMICS ; CDNAS ; FEATURES ; PROGRAM ; RE ; VARIANT ; assembly ; databases ; ANNOTATION ; CPG ISLANDS ; FULL-LENGTH HUMAN ; ASSAYS ; HIGH-THROUGHPUT ; TESTS ; GENOMIC DNA ; genomic ; SIGNALS ; E ; SET ; transcriptome ; POLYADENYLATION
    Abstract: Background: The German cDNA Consortium has been cloning full length cDNAs and continued with their exploitation in protein localization experiments and cellular assays. However, the efficient use of large cDNA resources requires the development of strategies that are capable of a speedy selection of truly useful cDNAs from biological and experimental noise. To this end we have developed a new high-throughput analysis tool, CAFTAN, which simplifies these efforts and thus fills the gap between large-scale cDNA collections and their systematic annotation and application in functional genomics. Results: CAFTAN is built around the mapping of cDNAs to the genome assembly, and the subsequent analysis of their genomic context. It uses sequence features like the presence and type of PolyA signals, inner and flanking repeats, the GC-content, splice site types, etc. All these features are evaluated in individual tests and classify cDNAs according to their sequence quality and likelihood to have been generated from fully processed mRNAs. Additionally, CAFTAN compares the coordinates of mapped cDNAs with the genomic coordinates of reference sets from public available resources ( e. g., VEGA, ENSEMBL). This provides detailed information about overlapping exons and the structural classification of cDNAs with respect to the reference set of splice variants. The evaluation of CAFTAN showed that is able to correctly classify more than 85% of 5950 selected "known protein-coding" VEGA cDNAs as high quality multi-or single-exon. It identified as good 80.6% of the single exon cDNAs and 85% of the multiple exon cDNAs. The program is written in Perl and in a modular way, allowing the adoption of this strategy to other tasks like EST-annotation, or to extend it by adding new classification rules and new organism databases as they become available. We think that it is a very useful program for the annotation and research of unfinished genomes. Conclusion: CAFTAN is a high-throughput sequence analysis tool, which performs a fast and reliable quality prediction of cDNAs. Several thousands of cDNAs can be analyzed in a short time, giving the curator/scientist a first quick overview about the quality and the already existing annotation of a set of cDNAs. It supports the rejection of low quality cDNAs and helps in the selection of likely novel splice variants, and/or completely novel transcripts for new experiments
    Type of Publication: Journal article published
    PubMed ID: 17064411
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  • 6
    Keywords: EXPRESSION ; Germany ; human ; MODEL ; CLASSIFICATION ; GENE ; GENE-EXPRESSION ; GENES ; GENOME ; PROTEIN ; ACCURACY ; TIME ; COMPLEX ; COMPLEXES ; DNA ; TISSUES ; BIOLOGY ; SEQUENCE ; SUSCEPTIBILITY ; PATTERNS ; gene expression ; PROMOTERS ; DNA methylation ; HUMAN GENOME ; bioinformatics ; PREDICTION ; INSIGHTS ; METHYLATION ; MAMMALIAN DEVELOPMENT ; DNA-SEQUENCES ; FEATURES ; CLUSTER-ANALYSIS ; CPG ISLANDS ; VERTEBRATE GENOMES ; biotechnology ; epigenetic ; epigenetic regulation ; WELL ; STRATEGY ; ISLANDS ; FUNCTIONAL-ROLE ; REGIONS TDMS
    Abstract: Background: The computational prediction of DNA methylation has become an important topic in the recent years due to its role in the epigenetic control of normal and cancer-related processes. While previous prediction approaches focused merely on differences between methylated and unmethylated DNA sequences, recent experimental results have shown the presence of much more complex patterns of methylation across tissues and time in the human genome. These patterns are only partially described by a binary model of DNA methylation. In this work we propose a novel approach, based on profile analysis of tissue-specific methylation that uncovers significant differences in the sequences of CpG islands (CGIs) that predispose them to a tissue-specific methylation pattern. Results: We defined CGI methylation profiles that separate not only between constitutively methylated and unmethylated CGIs, but also identify CGIs showing a differential degree of methylation across tissues and cell-types or a lack of methylation exclusively in sperm. These profiles are clearly distinguished by a number of CGI attributes including their evolutionary conservation, their significance, as well as the evolutionary evidence of prior methylation. Additionally, we assess profile functionality with respect to the different compartments of protein coding genes and their possible use in the prediction of DNA methylation. Conclusion: Our approach provides new insights into the biological features that determine if a CGI has a functional role in the epigenetic control of gene expression and the features associated with CGI methylation susceptibility. Moreover, we show that the ability to predict CGI methylation is based primarily on the quality of the biological information used and the relationships uncovered between different sources of knowledge. The strategy presented here is able to predict, besides the constitutively methylated and unmethylated classes, two more tissue specific methylation classes conserving the accuracy provided by leading binary methylation classification methods
    Type of Publication: Journal article published
    PubMed ID: 19383127
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  • 7
    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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  • 8
    Keywords: Germany ; SYSTEM ; TOOL ; GENE ; GENOME SEQUENCE ; BIOLOGY ; SEQUENCE ; NUMBER ; DATABASE ; bioinformatics ; INTERFACE ; PREDICTION ; W2H ; SEQUENCE-ANALYSIS ; support vector machines ; SUBSET ; FRAMEWORK ; RE ; function ; PROGRAMS ; ANNOTATION SYSTEM
    Abstract: Background: Vast progress in sequencing projects has called for annotation on a large scale. A Number of methods have been developed to address this challenging task. These methods, however, either apply to specific subsets, or their predictions are not formalised, or they do not provide precise confidence values for their predictions. Description: We recently established a learning system for automated annotation, trained with a broad variety of different organisms to predict the standardised annotation terms from Gene Ontology ( GO). Now, this method has been made available to the public via our web-service GOPET ( Gene Ontology term Prediction and Evaluation Tool). It supplies annotation for sequences of any organism. For each predicted term an appropriate confidence value is provided. The basic method had been developed for predicting molecular function GO-terms. It is now expanded to predict biological process terms. This web service is available via http:// genius. embnet.dkfz-heidelberg. de/menu/biounit/open-husar Conclusion: Our web service gives experimental researchers as well as the bioinformatics community a valuable sequence annotation device. Additionally, GOPET also provides less significant annotation data which may serve as an extended discovery platform for the user
    Type of Publication: Journal article published
    PubMed ID: 16549020
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  • 9
    Keywords: CANCER ; COMBINATION ; Germany ; INFORMATION ; SITE ; SITES ; GENES ; PROTEIN ; TIME ; DOMAIN ; BIOLOGY ; MOLECULAR-BIOLOGY ; ACID ; ELEMENT ; IDENTIFICATION ; ELEMENTS ; DATABASE ; bioinformatics ; INTERFACE ; SECONDARY STRUCTURE ; PROJECT ; molecular biology ; molecular ; RE ; FAMILIES ; SOFTWARE ; ANNOTATION ; SUPPLEMENT ; HOMOLOGY ; analysis ; methods ; cancer research ; ACCESS ; ENGLAND ; in combination ; ENSEMBL ; FUNCTIONAL-ANALYSIS ; PIPELINES ; SECONDARY STRUCTURE PREDICTION
    Abstract: The wealth of transcript information that has been made publicly available in recent years has led to large pools of individual web sites offering access to bioinformatics software. However, finding out which services exist, what they can or cannot do, how to use them and how to feed results from one service to the next one in the right format can be very time and resource consuming, especially for non-experts. Automating this task, we present a suite of protein annotation pipelines (tasks) developed at the German Cancer Research Centre (DKFZ) oriented to protein annotation by homology (ProtSweep), by domain analysis (DomainSweep), and by secondary structure elements (2Dsweep). The aim of these tasks is to perform an exhaustive structural and functional analysis employing a wide variety of methods in combination with the most updated public databases. The three servers are available for academic users at the HUSAR open server http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar/
    Type of Publication: Journal article published
    PubMed ID: 17526514
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  • 10
    Keywords: GENE ; GENE-EXPRESSION ; EXPRESSION ; SURVIVAL ; CLASSIFICATION ; gene expression profiling ; FLUORESCENCE ; expression profiling ; MULTIPLE-MYELOMA ; ONCOLOGY ; multiple myeloma ; Jun ; gene expression ; IN-SITU ; fluorescence in situ hybridisation ; PROGNOSTIC-FACTOR ; MOLECULAR CLASSIFICATION ; FREE SURVIVAL
    Type of Publication: Meeting abstract published
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