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    MIRAGAA-a methodology for finding coordinated effects of microRNA expression changes and genome aberrations in cancer

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    Author
    Gaire, RK; Bailey, J; Bearfoot, J; Campbell, IG; Stuckey, PJ; Haviv, I
    Date
    2010-01-15
    Source Title
    BIOINFORMATICS
    Publisher
    OXFORD UNIV PRESS
    University of Melbourne Author/s
    Bailey, James; Campbell, Ian; Stuckey, Peter; Haviv, Izhak; GAIRE, RAJ
    Affiliation
    Computer Science and Software Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Gaire, R. K., Bailey, J., Bearfoot, J., Campbell, I. G., Stuckey, P. J. & Haviv, I. (2010). MIRAGAA-a methodology for finding coordinated effects of microRNA expression changes and genome aberrations in cancer. BIOINFORMATICS, 26 (2), pp.161-167. https://doi.org/10.1093/bioinformatics/btp654.
    Access Status
    This item is currently not available from this repository
    URI
    http://hdl.handle.net/11343/31772
    DOI
    10.1093/bioinformatics/btp654
    Abstract
    MOTIVATION: Cancer evolves through microevolution where random lesions that provide the biggest advantage to cancer stand out in their frequent occurrence in multiple samples. At the same time, a gene function can be changed by aberration of the corresponding gene or modification of microRNA (miRNA) expression, which attenuates the gene. In a large number of cancer samples, these two mechanisms might be distributed in a coordinated and almost mutually exclusive manner. Understanding this coordination may assist in identifying changes which significantly produce the same functional impact on cancer phenotype, and further identify genes that are universally required for cancer. Present methodologies for finding aberrations usually analyze single datasets, which cannot identify such pairs of coordinating genes and miRNAs. RESULTS: We have developed MIRAGAA, a statistical approach, to assess the coordinated changes of genome copy numbers and miRNA expression. We have evaluated MIRAGAA on The Cancer Genome Atlas (TCGA) Glioblastoma Multiforme datasets. In these datasets, a number of genome regions coordinating with different miRNAs are identified. Although well known for their biological significance, these genes and miRNAs would be left undetected for being less significant if the two datasets were analyzed individually. AVAILABILITY AND IMPLEMENTATION: The source code, implemented in R and java, is available from our project web site at http://www.csse.unimelb.edu.au/~rgaire/MIRAGAA/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    Keywords
    Information Systems

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