The LIFEdb database in 2006

Division Molecular Genome Analysis, German Cancer Research Center, Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany.
Nucleic Acids Research (Impact Factor: 9.11). 02/2006; 34(Database issue):D415-8. DOI: 10.1093/nar/gkj139
Source: PubMed


LIFEdb ( 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.

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Available from: Winston Alexander Hide, Oct 06, 2015
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    • "There were two such cases at the beginning of 2013 when it became necessary to change the reference associated with a large set of manually created Cellular Component annotations provided by the Human Protein Atlas and LifeDB projects [25,26]. Previously these annotations were referenced by publications describing the experimental methods used in pilot studies for obtaining the annotations. "
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    ABSTRACT: The Gene Ontology Consortium (GOC) is a major bioinformatics project that provides structured controlled vocabularies to classify gene product function and location. GOC members create annotations to gene products using the Gene Ontology (GO) vocabularies, thus providing an extensive, publicly available resource. The GO and its annotations to gene products are now an integral part of functional analysis, and statistical tests using GO data are becoming routine for researchers to include when publishing functional information. While many helpful articles about the GOC are available, there are certain updates to the ontology and annotation sets that sometimes go unobserved. Here we describe some of the ways in which GO can change that should be carefully considered by all users of GO as they may have a significant impact on the resulting gene product annotations, and therefore the functional description of the gene product, or the interpretation of analyses performed on GO datasets. GO annotations for gene products change for many reasons, and while these changes generally improve the accuracy of the representation of the underlying biology, they do not necessarily imply that previous annotations were incorrect. We additionally describe the quality assurance mechanisms we employ to improve the accuracy of annotations, which necessarily changes the composition of the annotation sets we provide. We use the Universal Protein Resource (UniProt) for illustrative purposes of how the GO Consortium, as a whole, manages these changes.
    03/2014; 3(1):4. DOI:10.1186/2047-217X-3-4
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    • "The first genome-wide assessment of protein localization was carried out in the yeast Saccharomyces cerevisiae, using a green fluorescent protein (GFP)-tagging approach [2]. Similar systematic approaches to reveal the localization of the human proteome have also been described [3,4], however this task remains to be completed. Apart from the sheer complexity of mammalian genomes and their extensive transcriptional products, systematic analysis of protein localization in higher eukaryotes is also hampered by a lack of software tools to aid in automated determination of localization. "
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    ABSTRACT: Accurate quantitative co-localization is a key parameter in the context of understanding the spatial co-ordination of molecules and therefore their function in cells. Existing co-localization algorithms consider either the presence of co-occurring pixels or correlations of intensity in regions of interest. Depending on the image source, and the algorithm selected, the co-localization coefficients determined can be highly variable, and often inaccurate. Furthermore, this choice of whether co-occurrence or correlation is the best approach for quantifying co-localization remains controversial. We have developed a novel algorithm to quantify co-localization that improves on and addresses the major shortcomings of existing co-localization measures. This algorithm uses a non-parametric ranking of pixel intensities in each channel, and the difference in ranks of co-localizing pixel positions in the two channels is used to weight the coefficient. This weighting is applied to co-occurring pixels thereby efficiently combining both co-occurrence and correlation. Tests with synthetic data sets show that the algorithm is sensitive to both co-occurrence and correlation at varying levels of intensity. Analysis of biological data sets demonstrate that this new algorithm offers high sensitivity, and that it is capable of detecting subtle changes in co-localization, exemplified by studies on a well characterized cargo protein that moves through the secretory pathway of cells. This algorithm provides a novel way to efficiently combine co-occurrence and correlation components in biological images, thereby generating an accurate measure of co-localization. This approach of rank weighting of intensities also eliminates the need for manual thresholding of the image, which is often a cause of error in co-localization quantification. We envisage that this tool will facilitate the quantitative analysis of a wide range of biological data sets, including high resolution confocal images, live cell time-lapse recordings, and high-throughput screening data sets.
    BMC Bioinformatics 10/2011; 12(1):407. DOI:10.1186/1471-2105-12-407 · 2.58 Impact Factor
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    • "Vacuole membrane protein 1 has been predicted to be a seven transmembrane protein (Hirokawa et al., 1998; Figure 2a) and the overexpressed human protein was reported to localize to the ER and Golgi apparatus (Starkuviene et al., 2004; Mehrle et al., 2006). The overexpressed rat protein was shown to induce intracellular vacuole formation with Vmp1 integrated into the membrane of these vacuoles (Dusetti et al., 2002). "
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    ABSTRACT: Vacuole membrane protein 1 (Vmp1) is described as a cancer-relevant cell cycle modulator, but the function of this protein and its mode of action in tumor progression are still unknown. In this study, we show that the VMP1 mRNA level is significantly reduced in kidney cancer metastases as compared to primary tumors. Further, VMP1 expression is also decreased in the invasive breast cancer cell lines HCC1954 and MDA-MB-231 as compared to the non-invasive cell lines MCF-12A, T-47D and MCF-7. We show for the first time that Vmp1 is a plasma membrane protein and an essential component of initial cell-cell contacts and tight junction formation. It interacts with the tight junction protein Zonula Occludens-1 and colocalizes in spots between neighboring HEK293 cells. Downregulation of VMP1 by RNAi results in loss of cell adherence, and increases the invasion capacity of the non-invasive kidney cancer cell line Caki-2. In conclusion, our findings establish Vmp1 to be a novel cell-cell adhesion protein and that its expression level determines the invasion and metastatic potential of cancer cells.
    Oncogene 03/2008; 27(9):1320-6. DOI:10.1038/sj.onc.1210743 · 8.46 Impact Factor
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