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    Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing

    Access Status
    Open access via publisher
    Authors
    Morin, R.
    Bainbridge, M.
    Fejes, A.
    Hirst, M.
    Krzywinski, M.
    Pugh, T.
    McDonald, H.
    Varhol, Richard
    Jones, S.
    Marra, M.
    Date
    2008
    Type
    Journal Article
    
    Metadata
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    Citation
    Morin, R. and Bainbridge, M. and Fejes, A. and Hirst, M. and Krzywinski, M. and Pugh, T. and McDonald, H. et al. 2008. Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. BioTechniques. 45 (1): pp. 81-94.
    Source Title
    BioTechniques
    DOI
    10.2144/000112900
    ISSN
    0736-6205
    School
    Department of Health Policy and Management
    URI
    http://hdl.handle.net/20.500.11937/22968
    Collection
    • Curtin Research Publications
    Abstract

    Sequence-based methods for transcriptome characterization have typically relied on generation of either serial analysis of gene expression tags or expressed sequence tags. Although such approaches have the potential to enumerate transcripts by counting sequence tags derived from them, they typically do not robustly survey the majority of transcripts along their entire length. Here we show that massively parallel sequencing of randomly primed cDNAs, using a next-generation sequencing-by-synthesis technology, offers the potential to generate relative measures of mRNA and individual exon abundance while simultaneously profiling the prevalence of both annotated and novel exons and exon-splicing events. This technique identifies known single nucleotide polymorphisms (SNPs) as well as novel single-base variants. Analysis of these variants, and previously unannotated splicing events in the HeLa S3 cell line, reveals an overrepresentation of gene categories including those previously implicated in cancer.

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