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dc.contributor.authorRubin, AF
dc.contributor.authorGelman, H
dc.contributor.authorLucas, N
dc.contributor.authorBajjalieh, SM
dc.contributor.authorPapenfuss, AT
dc.contributor.authorSpeed, TP
dc.contributor.authorFowler, DM
dc.date.accessioned2020-12-21T01:41:51Z
dc.date.available2020-12-21T01:41:51Z
dc.date.issued2017-08-07
dc.identifierpii: 10.1186/s13059-017-1272-5
dc.identifier.citationRubin, 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.
dc.identifier.issn1474-760X
dc.identifier.urihttp://hdl.handle.net/11343/256620
dc.description.abstractDeep 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.
dc.languageEnglish
dc.publisherBMC
dc.titleA statistical framework for analyzing deep mutational scanning data
dc.typeJournal Article
dc.identifier.doi10.1186/s13059-017-1272-5
melbourne.affiliation.departmentMedical Biology (W.E.H.I.)
melbourne.affiliation.departmentSchool of Mathematics and Statistics
melbourne.source.titleGenome Biology
melbourne.source.volume18
melbourne.source.issue1
dc.rights.licenseCC BY
melbourne.elementsid1226756
melbourne.contributor.authorPapenfuss, Anthony
melbourne.contributor.authorSpeed, Terence
melbourne.contributor.authorRubin, Alan
dc.identifier.eissn1474-760X
melbourne.accessrightsOpen Access


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