A Statistical Framework for Analyzing Deep Mutational Scanning Data

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2017 Aug 9

PMID 28784151

Citations 116

Authors

Alan F Rubin

Hannah Gelman

Nathan Lucas

Sandra M Bajjalieh

Anthony T Papenfuss

Terence P Speed

Douglas M Fowler

Affiliations

Soon will be listed here.

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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