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  • Computational Methods, Transcriptome Mapping - Monitoring Gene Expression  (1)
  • DNA Contamination  (1)
  • 1
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    Cancer: Authenticate new xenograft models (2016)
    Nature Publishing Group (NPG)
    Details
    Publication Date: 2016-04-30
    Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Nardone, Roland M -- MacLeod, Roderick A F -- Capes-Davis, Amanda -- England -- Nature. 2016 Apr 21;532(7599):313.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/27127813" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Cell Line, Tumor ; DNA Contamination ; Databases, Factual ; *Disease Models, Animal ; Guidelines as Topic ; Heterografts/*standards ; Humans ; National Cancer Institute (U.S.) ; Neoplasms/*pathology ; Quality Control ; Reproducibility of Results ; United States ; Xenograft Model Antitumor Assays/*standards
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Published by Nature Publishing Group (NPG)
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    Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line (2016)
    Oxford University Press
    Details
    Publication Date: 2016-10-08
    Description: Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.
    Keywords: Computational Methods, Transcriptome Mapping - Monitoring Gene Expression
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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