A statistical framework for analyzing deep mutational scanning data
Publication Year 2017-08-07, Volume 18, Issue #1, Page 150
Journal Title
Genome Biol
Publication Type
Journal Article
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.
BioMed Central
WEHI Research Division(s)
PubMed ID
NHMRC Grants
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
An error has occurred. This application may no longer respond until reloaded. Reload 🗙