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    A statistical framework for analyzing deep mutational scanning data

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    Author
    Rubin, AF; Gelman, H; Lucas, N; Bajjalieh, SM; Papenfuss, AT; Speed, TP; Fowler, DM
    Date
    2017-08-07
    Source Title
    Genome Biology
    Publisher
    BMC
    University of Melbourne Author/s
    Papenfuss, Anthony; Speed, Terence; Rubin, Alan
    Affiliation
    Medical Biology (W.E.H.I.)
    School of Mathematics and Statistics
    Metadata
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    Document Type
    Journal Article
    Citations
    Rubin, A. F., Gelman, H., Lucas, N., Bajjalieh, S. M., Papenfuss, A. T., Speed, T. P. & Fowler, D. M. (2017). A statistical framework for analyzing deep mutational scanning data. GENOME BIOLOGY, 18 (1), https://doi.org/10.1186/s13059-017-1272-5.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/256620
    DOI
    10.1186/s13059-017-1272-5
    Abstract
    Deep mutational scanning is a widely used method for multiplex measurement of functional consequences of protein variants. We developed a new deep mutational scanning statistical model that generates error estimates for each measurement, capturing both sampling error and consistency between replicates. We apply our model to one novel and five published datasets comprising 243,732 variants and demonstrate its superiority in removing noisy variants and conducting hypothesis testing. Simulations show our model applies to scans based on cell growth or binding and handles common experimental errors. We implemented our model in Enrich2, software that can empower researchers analyzing deep mutational scanning data.

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