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Computational Inference Software for Tetrad Assembly from Randomly Arrayed Yeast Colonies

We describe an information-theory-based method and associated software for computationally identifying sister spores derived from the same meiotic tetrad. The method exploits specific DNA sequence features of tetrads that result from meiotic centromere and allele segregation patterns. Because the me...

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Autores principales: Sakhanenko, Nikita A., Cromie, Gareth A., Dudley, Aimée M., Galas, David J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Genetics Society of America 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6643883/
https://www.ncbi.nlm.nih.gov/pubmed/31109921
http://dx.doi.org/10.1534/g3.119.400166
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author Sakhanenko, Nikita A.
Cromie, Gareth A.
Dudley, Aimée M.
Galas, David J.
author_facet Sakhanenko, Nikita A.
Cromie, Gareth A.
Dudley, Aimée M.
Galas, David J.
author_sort Sakhanenko, Nikita A.
collection PubMed
description We describe an information-theory-based method and associated software for computationally identifying sister spores derived from the same meiotic tetrad. The method exploits specific DNA sequence features of tetrads that result from meiotic centromere and allele segregation patterns. Because the method uses only the genomic sequence, it alleviates the need for tetrad-specific barcodes or other genetic modifications to the strains. Using this method, strains derived from randomly arrayed spores can be efficiently grouped back into tetrads.
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spelling pubmed-66438832019-07-25 Computational Inference Software for Tetrad Assembly from Randomly Arrayed Yeast Colonies Sakhanenko, Nikita A. Cromie, Gareth A. Dudley, Aimée M. Galas, David J. G3 (Bethesda) Software and Data Resources We describe an information-theory-based method and associated software for computationally identifying sister spores derived from the same meiotic tetrad. The method exploits specific DNA sequence features of tetrads that result from meiotic centromere and allele segregation patterns. Because the method uses only the genomic sequence, it alleviates the need for tetrad-specific barcodes or other genetic modifications to the strains. Using this method, strains derived from randomly arrayed spores can be efficiently grouped back into tetrads. Genetics Society of America 2019-05-20 /pmc/articles/PMC6643883/ /pubmed/31109921 http://dx.doi.org/10.1534/g3.119.400166 Text en Copyright © 2019 Sakhanenko et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software and Data Resources
Sakhanenko, Nikita A.
Cromie, Gareth A.
Dudley, Aimée M.
Galas, David J.
Computational Inference Software for Tetrad Assembly from Randomly Arrayed Yeast Colonies
title Computational Inference Software for Tetrad Assembly from Randomly Arrayed Yeast Colonies
title_full Computational Inference Software for Tetrad Assembly from Randomly Arrayed Yeast Colonies
title_fullStr Computational Inference Software for Tetrad Assembly from Randomly Arrayed Yeast Colonies
title_full_unstemmed Computational Inference Software for Tetrad Assembly from Randomly Arrayed Yeast Colonies
title_short Computational Inference Software for Tetrad Assembly from Randomly Arrayed Yeast Colonies
title_sort computational inference software for tetrad assembly from randomly arrayed yeast colonies
topic Software and Data Resources
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6643883/
https://www.ncbi.nlm.nih.gov/pubmed/31109921
http://dx.doi.org/10.1534/g3.119.400166
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