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  • Computational Methods, Genomics, Transcriptome Mapping - Monitoring Gene Expression  (1)
  • Life and Medical Sciences  (1)
  • 1
    Electronic Resource
    Electronic Resource
    Evidence of an EGF/TGF-α - independent pathway for estrogen-regulated cell proliferation (1991)
    New York, N.Y. : Wiley-Blackwell
    Journal of Cellular Biochemistry 46 (1991), S. 125-133 
    Details
    ISSN: 0730-2312
    Keywords: breast cancer cell line ; CAMA-1 ; Intron Differential RNA/PCR ; gene expression ; EGF receptor ; Life and Medical Sciences ; Cell & Developmental Biology
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Biology , Chemistry and Pharmacology , Medicine
    Notes: To elucidate the relationship between epidermal growth factor (EGF)/transforming growth factor (TGF-α) and estradiol-17β (E) in cell proliferation, we examined their effects on the breast cancer cell line, CAMA-1. While E was able to consistently induce cell proliferation under a variety of experimental conditions, EGF/TGF-α was without effect. Despite the presence of the receptor (EGFR) gene, mature EGFR protein and mRNA were not detected by radioreceptor assay, 35S Met-labelling, and the Intron Differential RNA/PCR method under conditions in which cells remain responsive to E. Furthermore, TGF-α is not an autocrine factor in CAMA-1 cells. We demonstrated unequivocally that EGF/TGF-α interaction with EGFR is not an obligatory event in mediating estrogen-stimulated cell proliferation.
    Additional Material: 8 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/jcb.240460206
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    Paper (German National Licenses)
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    TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis (2016)
    Oxford University Press
    Details
    Publication Date: 2016-07-28
    Description: When analyzing single-cell RNA-seq data, constructing a pseudo-temporal path to order cells based on the gradual transition of their transcriptomes is a useful way to study gene expression dynamics in a heterogeneous cell population. Currently, a limited number of computational tools are available for this task, and quantitative methods for comparing different tools are lacking. Tools for Single Cell Analysis (TSCAN) is a software tool developed to better support in silico pseudo- T ime reconstruction in S ingle- C ell RNA-seq AN alysis. TSCAN uses a cluster-based minimum spanning tree (MST) approach to order cells. Cells are first grouped into clusters and an MST is then constructed to connect cluster centers. Pseudo-time is obtained by projecting each cell onto the tree, and the ordered sequence of cells can be used to study dynamic changes of gene expression along the pseudo-time. Clustering cells before MST construction reduces the complexity of the tree space. This often leads to improved cell ordering. It also allows users to conveniently adjust the ordering based on prior knowledge. TSCAN has a graphical user interface (GUI) to support data visualization and user interaction. Furthermore, quantitative measures are developed to objectively evaluate and compare different pseudo-time reconstruction methods. TSCAN is available at https://github.com/zji90/TSCAN and as a Bioconductor package.
    Keywords: Computational Methods, Genomics, Transcriptome Mapping - Monitoring Gene Expression
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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