Genome-wide prediction of DNA mutation effect on nucleosome positions for yeast synthetic genomics
- 1Sorbonne Universite, CNRS, Laboratoire de Physique Théorique de la Matière Condensée, LPTMC, Paris F-75252, France;
- 2LISSI, Université Paris-Est Créteil, 94000 Créteil, France;
- 3Muséum National d'Histoire Naturelle, Structure et Instabilité des Génomes, UMR7196, Paris 75231, France;
- 4Institut Universitaire de France, Paris 75005, France
Abstract
Genetically modified genomes are often used today in many areas of fundamental and applied research. In many studies, coding or noncoding regions are modified in order to change protein sequences or gene expression levels. Modifying one or several nucleotides in a genome can also lead to unexpected changes in the epigenetic regulation of genes. When designing a synthetic genome with many mutations, it would thus be very informative to be able to predict the effect of these mutations on chromatin. We develop here a deep learning approach that quantifies the effect of every possible single mutation on nucleosome positions on the full Saccharomyces cerevisiae genome. This type of annotation track can be used when designing a modified S. cerevisiae genome. We further highlight how this track can provide new insights on the sequence-dependent mechanisms that drive nucleosomes’ positions in vivo.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.264416.120.
- Received April 15, 2020.
- Accepted December 11, 2020.
This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
