Minimum Epistasis Interpolation for Sequence-Function Relationships

Zhou, J., McCandlish, D. M. (April 2020) Minimum Epistasis Interpolation for Sequence-Function Relationships. Nature Communication, 11 (1782). pp. 1-11. ISSN 2041-1723 (Public Dataset)

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Abstract

Massively parallel phenotyping assays have provided unprecedented insight into how multiple mutations combine to determine biological function. While such assays can measure phenotypes for thousands to millions of genotypes in a single experiment, in practice these measurements are not exhaustive, so that there is a need for techniques to impute values for genotypes whose phenotypes have not been directly assayed. Here, we present an imputation method based on inferring the least epistatic possible sequence-function relationship compatible with the data. In particular, we infer the reconstruction where mutational effects change as little as possible across adjacent genetic backgrounds. The resulting models can capture complex higher-order genetic interactions near the data, but approach additivity where data is sparse or absent. We apply the method to high-throughput transcription factor binding assays and use it to explore a fitness landscape for protein G.

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > analysis and processing
bioinformatics
bioinformatics > genomics and proteomics
organism description > bacteria
bioinformatics > computational biology
bioinformatics > genomics and proteomics > annotation > phenotyping
CSHL Authors:
Communities: CSHL labs > McCandlish lab
Cold Spring Harbor Laboratory of Quantitative Biology
CSHL Cancer Center Program > Cancer Genetics and Genomics Program
Depositing User: Adrian Gomez
Date: 14 April 2020
Date Deposited: 16 Apr 2020 18:13
Last Modified: 01 Feb 2024 19:51
PMCID: PMC7156698
Related URLs:
Dataset ID:
  • https://doi.org/10.7554/eLife.16965.024
  • http://cisbp.ccbr.utoronto.ca
URI: https://repository.cshl.edu/id/eprint/39236

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