A statistical framework for analyzing deep mutational scanning data
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...
Main Authors: | Alan F. Rubin, Hannah Gelman, Nathan Lucas, Sandra M. Bajjalieh, Anthony T. Papenfuss, Terence P. Speed, Douglas M. Fowler |
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Format: | Article |
Language: | English |
Published: |
BMC
2017-08-01
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Series: | Genome Biology |
Online Access: | http://link.springer.com/article/10.1186/s13059-017-1272-5 |
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