Journal article

A statistical framework for analyzing deep mutational scanning data

Alan F Rubin, Hannah Gelman, Nathan Lucas, Sandra M Bajjalieh, Anthony T Papenfuss, Terence P Speed, Douglas M Fowler

Genome Biology | BMC | Published : 2017

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 mutation..

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Grants

Awarded by National Institute of General Medical Sciences


Awarded by National Institute of Biomedical Imaging and Bioengineering


Awarded by National Health and Medical Research Council of Australia


Awarded by NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING


Awarded by NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES


Funding Acknowledgements

This work was supported by the National Institute of General Medical Sciences (1R01GM109110 and 5R24GM115277 to DMF and P41GM103533 to Stanley Fields); the National Institute of Biomedical Imaging and Bioengineering (5R21EB020277 to SMB and DMF); the Washington Research Foundation (Washington Research Foundation Innovation Postdoctoral Fellowship to HG); and the National Health and Medical Research Council of Australia (Program Grant 1054618 to ATP and TPS).