A statistical framework for analyzing deep mutational scanning data
Details
Publication Year 2017-08-07, Volume 18, Issue #1, Page 150
Journal Title
Genome Biol
Publication Type
Journal Article
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.
Publisher
BioMed Central
WEHI Research Division(s)
Bioinformatics
PubMed ID
28784151
NHMRC Grants
NHMRC/1054618
Rights Notice
Refer to copyright notice on published article.


Creation Date: 2017-08-30 02:22:20
Last Modified: 2017-09-04 01:36:00
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