BIOMAT Database Logo BIOMAT Database Logo

A statistical framework for analyzing deep mutational scanning data. 2017

Alan F Rubin, and Hannah Gelman, and Nathan Lucas, and Sandra M Bajjalieh, and Anthony T Papenfuss, and Terence P Speed, and Douglas M Fowler
Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.

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.

UI MeSH Term Description Entries

Related Publications

Alan F Rubin, and Hannah Gelman, and Nathan Lucas, and Sandra M Bajjalieh, and Anthony T Papenfuss, and Terence P Speed, and Douglas M Fowler
Statistical approaches for analyzing mutational spectra: some recommendations for categorical data.
January 1994, Genetics,
Alan F Rubin, and Hannah Gelman, and Nathan Lucas, and Sandra M Bajjalieh, and Anthony T Papenfuss, and Terence P Speed, and Douglas M Fowler
DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies.
August 2020, Genome biology,
Alan F Rubin, and Hannah Gelman, and Nathan Lucas, and Sandra M Bajjalieh, and Anthony T Papenfuss, and Terence P Speed, and Douglas M Fowler
A Statistical Guide to the Design of Deep Mutational Scanning Experiments.
September 2016, Genetics,
Alan F Rubin, and Hannah Gelman, and Nathan Lucas, and Sandra M Bajjalieh, and Anthony T Papenfuss, and Terence P Speed, and Douglas M Fowler
dms-view: Interactive visualization tool for deep mutational scanning data.
January 2020, Journal of open source software,
Alan F Rubin, and Hannah Gelman, and Nathan Lucas, and Sandra M Bajjalieh, and Anthony T Papenfuss, and Terence P Speed, and Douglas M Fowler
Software for the analysis and visualization of deep mutational scanning data.
May 2015, BMC bioinformatics,
Alan F Rubin, and Hannah Gelman, and Nathan Lucas, and Sandra M Bajjalieh, and Anthony T Papenfuss, and Terence P Speed, and Douglas M Fowler
popDMS infers mutation effects from deep mutational scanning data.
January 2024, bioRxiv : the preprint server for biology,
Alan F Rubin, and Hannah Gelman, and Nathan Lucas, and Sandra M Bajjalieh, and Anthony T Papenfuss, and Terence P Speed, and Douglas M Fowler
Deep mutational scanning for therapeutic antibody engineering.
February 2022, Trends in pharmacological sciences,
Alan F Rubin, and Hannah Gelman, and Nathan Lucas, and Sandra M Bajjalieh, and Anthony T Papenfuss, and Terence P Speed, and Douglas M Fowler
Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage.
May 2024, Genome biology,
Alan F Rubin, and Hannah Gelman, and Nathan Lucas, and Sandra M Bajjalieh, and Anthony T Papenfuss, and Terence P Speed, and Douglas M Fowler
Topological deep learning based deep mutational scanning.
September 2023, Computers in biology and medicine,
Alan F Rubin, and Hannah Gelman, and Nathan Lucas, and Sandra M Bajjalieh, and Anthony T Papenfuss, and Terence P Speed, and Douglas M Fowler
A statistical approach for analyzing clonogenic survival data.
May 1996, Radiation research,
Copied contents to your clipboard!