A statistical framework for analyzing deep mutational scanning data
- Author(s)
- Rubin, AF; Gelman, H; Lucas, N; Bajjalieh, SM; Papenfuss, AT; Speed, TP; Fowler, DM;
- 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
- Research Division(s)
- Bioinformatics
- PubMed ID
- 28784151
- Publisher's Version
- https://doi.org/10.1186/s13059-017-1272-5
- Open Access at Publisher's Site
https://genomebiology-biomedcentral-com/articles/10.1186/s13059-017-1272-5- NHMRC Grants
- NHMRC/1054618,
- Terms of Use/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