Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen

Structural variation in genomes of the same species is frequent but what drives the rearrangements remains unclear. Machine-learning of rearrangement patterns among telomere-to-telomere assemblies can accurately identify regions of intrinsic DNA instability in a eukaryotic pathogen.

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Autores principales:	Thomas Badet, Simone Fouché, Fanny E. Hartmann, Marcello Zala, Daniel Croll
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/26805112d9a64392bfb163399db5f589
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spelling	oai:doaj.org-article:26805112d9a64392bfb163399db5f5892021-12-02T17:52:09ZMachine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen10.1038/s41467-021-23862-x2041-1723https://doaj.org/article/26805112d9a64392bfb163399db5f5892021-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23862-xhttps://doaj.org/toc/2041-1723Structural variation in genomes of the same species is frequent but what drives the rearrangements remains unclear. Machine-learning of rearrangement patterns among telomere-to-telomere assemblies can accurately identify regions of intrinsic DNA instability in a eukaryotic pathogen.Thomas BadetSimone FouchéFanny E. HartmannMarcello ZalaDaniel CrollNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Thomas Badet Simone Fouché Fanny E. Hartmann Marcello Zala Daniel Croll Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen
description	Structural variation in genomes of the same species is frequent but what drives the rearrangements remains unclear. Machine-learning of rearrangement patterns among telomere-to-telomere assemblies can accurately identify regions of intrinsic DNA instability in a eukaryotic pathogen.
format	article
author	Thomas Badet Simone Fouché Fanny E. Hartmann Marcello Zala Daniel Croll
author_facet	Thomas Badet Simone Fouché Fanny E. Hartmann Marcello Zala Daniel Croll
author_sort	Thomas Badet
title	Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen
title_short	Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen
title_full	Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen
title_fullStr	Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen
title_full_unstemmed	Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen
title_sort	machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/26805112d9a64392bfb163399db5f589
work_keys_str_mv	AT thomasbadet machinelearningpredictsgenomicdeterminantsofmeiosisdrivenstructuralvariationinaeukaryoticpathogen AT simonefouche machinelearningpredictsgenomicdeterminantsofmeiosisdrivenstructuralvariationinaeukaryoticpathogen AT fannyehartmann machinelearningpredictsgenomicdeterminantsofmeiosisdrivenstructuralvariationinaeukaryoticpathogen AT marcellozala machinelearningpredictsgenomicdeterminantsofmeiosisdrivenstructuralvariationinaeukaryoticpathogen AT danielcroll machinelearningpredictsgenomicdeterminantsofmeiosisdrivenstructuralvariationinaeukaryoticpathogen
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Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen

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