Mirage 2.0: fast and memory-efficient reconstruction of gene-content evolution considering heterogeneous evolutionary patterns among gene families

SUMMARY: We present Mirage 2.0, which accurately estimates gene-content evolutionary history by considering heterogeneous evolutionary patterns among gene families. Notably, we introduce a deterministic pattern mixture model, which makes Mirage substantially faster and more memory-efficient to be ap...

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Detalles Bibliográficos
Autores principales: Fukunaga, Tsukasa, Iwasaki, Wataru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Applications Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364385/
https://www.ncbi.nlm.nih.gov/pubmed/35771653
http://dx.doi.org/10.1093/bioinformatics/btac433
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author Fukunaga, Tsukasa
Iwasaki, Wataru
author_facet Fukunaga, Tsukasa
Iwasaki, Wataru
author_sort Fukunaga, Tsukasa
collection PubMed
description SUMMARY: We present Mirage 2.0, which accurately estimates gene-content evolutionary history by considering heterogeneous evolutionary patterns among gene families. Notably, we introduce a deterministic pattern mixture model, which makes Mirage substantially faster and more memory-efficient to be applicable to large datasets with thousands of genomes. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at https://github.com/fukunagatsu/Mirage. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-93643852022-08-11 Mirage 2.0: fast and memory-efficient reconstruction of gene-content evolution considering heterogeneous evolutionary patterns among gene families Fukunaga, Tsukasa Iwasaki, Wataru Bioinformatics Applications Notes SUMMARY: We present Mirage 2.0, which accurately estimates gene-content evolutionary history by considering heterogeneous evolutionary patterns among gene families. Notably, we introduce a deterministic pattern mixture model, which makes Mirage substantially faster and more memory-efficient to be applicable to large datasets with thousands of genomes. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at https://github.com/fukunagatsu/Mirage. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-06-30 /pmc/articles/PMC9364385/ /pubmed/35771653 http://dx.doi.org/10.1093/bioinformatics/btac433 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Fukunaga, Tsukasa
Iwasaki, Wataru
Mirage 2.0: fast and memory-efficient reconstruction of gene-content evolution considering heterogeneous evolutionary patterns among gene families
title Mirage 2.0: fast and memory-efficient reconstruction of gene-content evolution considering heterogeneous evolutionary patterns among gene families
title_full Mirage 2.0: fast and memory-efficient reconstruction of gene-content evolution considering heterogeneous evolutionary patterns among gene families
title_fullStr Mirage 2.0: fast and memory-efficient reconstruction of gene-content evolution considering heterogeneous evolutionary patterns among gene families
title_full_unstemmed Mirage 2.0: fast and memory-efficient reconstruction of gene-content evolution considering heterogeneous evolutionary patterns among gene families
title_short Mirage 2.0: fast and memory-efficient reconstruction of gene-content evolution considering heterogeneous evolutionary patterns among gene families
title_sort mirage 2.0: fast and memory-efficient reconstruction of gene-content evolution considering heterogeneous evolutionary patterns among gene families
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364385/
https://www.ncbi.nlm.nih.gov/pubmed/35771653
http://dx.doi.org/10.1093/bioinformatics/btac433
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