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A statistical framework for analyzing deep mutational scanning data

Open access
Rubin, Alan F., Gelman, Hannah, Lucas, Nathan, Bajjalieh, Sandra M., Papenfuss, Anthony T., Speed, Terence P., Fowler, Douglas M.·2017·Genome Biology
DOI 10.1186/s13059-017-1272-5PMC PMC5547491
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Abstract

Abstract Measuring the functional consequences of protein variants can reveal how a protein works or help unlock the meaning of an individual’s genome. Deep mutational scanning is a widely used method for multiplex measurement of the functional consequences of protein variants. A major limitation of this method has been the lack of a common analysis framework. We developed a statistical model for estimating variant scores that can be applied to many experimental designs. Our method generates an error estimate for each score that captures 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, particularly for removing noisy variants, detecting variants of small effect, and conducting hypothesis testing. We implemented our model in easy-to-use software, Enrich2, that can empower researchers analyzing deep mutational scanning data.

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Authors
Rubin, A. F., Gelman, H., Lucas, N., Bajjalieh, S. M., Papenfuss, A. T., Speed, T. P., & Fowler, D. M.
Affiliations
  • The University of Melbourne (AU)
  • University of Washington (US)
Corresponding author
Fowler, Douglas M.
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2017-08-07
Volume
18
Issue
1
Pages
150-150
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-017-1272-5
PMC
PMC5547491
OpenAlex
W2744336424
Cited by
262
References
3
Citations by year
2026: 42 · 2025: 39 · 2024: 58 · 2023: 31 · 2022: 22 · 2021: 20 · 2020: 18 · 2019: 13 · 2018: 15 · 2017: 3 · 2016: 1
FWCI
5.33
Type
journal article
Language
en
Update notices
correction (2018-02-07)
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institute of General Medical Sciences (1R01GM109110, 5R24GM115277, P41GM103533)
  • National Institute of Biomedical Imaging and Bioengineering (5R21EB020277)
  • National Health and Medical Research Council (1054618)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Keywords
deep mutational scanning, statistical models, error estimation, sampling error, protein variants, hypothesis testing, noise reduction, cell growth, protein binding

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