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HypercubeME: two hundred million combinatorially complete datasets from a single experiment

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dc.contributor.author Esteban, Laura Avino
dc.contributor.author Lonishin, Lyubov R.
dc.contributor.author Bobrovskiy, Daniil
dc.contributor.author Leleytner, Gregory
dc.contributor.author Bogatyreva, Natalya S.
dc.contributor.author Kondrashov, Fyodor A., 1979-
dc.contributor.author Ivankov, Dmitry N.
dc.date.accessioned 2020-11-24T07:07:12Z
dc.date.available 2020-11-24T07:07:12Z
dc.date.issued 2020
dc.identifier.citation Esteban LA, Lonishin LR, Bobrovskiy D, Leleytner G, Bogatyreva NS, Kondrashov FA, Ivankov DN. HypercubeME: two hundred million combinatorially complete datasets from a single experiment. Bioinformatics. 2020; 36(6):1960-2. DOI: 10.1093/bioinformatics/btz841
dc.identifier.issn 1367-4803
dc.identifier.uri http://hdl.handle.net/10230/45879
dc.description.abstract Motivation: Epistasis, the context-dependence of the contribution of an amino acid substitution to fitness, is common in evolution. To detect epistasis, fitness must be measured for at least four genotypes: the reference genotype, two different single mutants and a double mutant with both of the single mutations. For higher-order epistasis of the order n, fitness has to be measured for all 2n genotypes of an n-dimensional hypercube in genotype space forming a "combinatorially complete dataset". So far, only a handful of such datasets have been produced by manual curation. Concurrently, random mutagenesis experiments have produced measurements of fitness and other phenotypes in a high-throughput manner, potentially containing a number of combinatorially complete datasets. Results: We present an effective recursive algorithm for finding all hypercube structures in random mutagenesis experimental data. To test the algorithm, we applied it to the data from a recent HIS3 protein dataset and found all 199,847,053 unique combinatorially complete genotype combinations of dimensionality ranging from two to twelve. The algorithm may be useful for researchers looking for higher-order epistasis in their high-throughput experimental data. Availability: https://github.com/ivankovlab/HypercubeME.git. Supplementary information: Supplementary data are available at Bioinformatics online.
dc.description.sponsorship This work was supported by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013, ERC grant agreement 335980_EinME) and Startup package to the Ivankov laboratory at Skolkovo Institute of Science and Technology. The work was started at the School of Molecular and Theoretical Biology 2017 supported by the Zimin Foundation. N.S.B. was supported by the Woman Scientists Support Grant in Centre for Genomic Regulation (CRG).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Oxford University Press
dc.relation.ispartof Bioinformatics. 2020; 36(6):1960-2
dc.rights © The Author(s) 2019. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/
dc.title HypercubeME: two hundred million combinatorially complete datasets from a single experiment
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1093/bioinformatics/btz841
dc.subject.keyword Genetics and population analysis
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/335980
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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