Journal article

Enrich2: a statistical framework for analyzing deep mutational scanning data

Alan Rubin, Nathan Lucas, Sandra Bajjalieh, Anthony Papenfuss, Terence Speed, Douglas Fowler

Published : 2016

Abstract

Abstract Measuring the functional consequences of protein variants can reveal how a protein works or help unlock the meaning of an individual’s genome. Deep mutational scanning is a widely used method for multiplex measurement of the functional consequences of protein variants. A major limitation of this method has been the lack of a common analysis framework. We developed a statistical model for estimating variant scores that can be applied to many experimental designs. Our method generates an error estimate for each score that captures both sampling error and consistency between replicates. We apply our model to one novel and five published datasets comprising 243,732 variants and demonstr..

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