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Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees

Gene function annotation is important for a variety of downstream analyses of genetic data. But experimental characterization of function remains costly and slow, making computational prediction an important endeavor. Phylogenetic approaches to prediction have been developed, but implementation of a...

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Autores principales: Vega Yon, George G., Thomas, Duncan C., Morrison, John, Mi, Huaiyu, Thomas, Paul D., Marjoram, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924801/
https://www.ncbi.nlm.nih.gov/pubmed/33600408
http://dx.doi.org/10.1371/journal.pcbi.1007948
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author Vega Yon, George G.
Thomas, Duncan C.
Morrison, John
Mi, Huaiyu
Thomas, Paul D.
Marjoram, Paul
author_facet Vega Yon, George G.
Thomas, Duncan C.
Morrison, John
Mi, Huaiyu
Thomas, Paul D.
Marjoram, Paul
author_sort Vega Yon, George G.
collection PubMed
description Gene function annotation is important for a variety of downstream analyses of genetic data. But experimental characterization of function remains costly and slow, making computational prediction an important endeavor. Phylogenetic approaches to prediction have been developed, but implementation of a practical Bayesian framework for parameter estimation remains an outstanding challenge. We have developed a computationally efficient model of evolution of gene annotations using phylogenies based on a Bayesian framework using Markov Chain Monte Carlo for parameter estimation. Unlike previous approaches, our method is able to estimate parameters over many different phylogenetic trees and functions. The resulting parameters agree with biological intuition, such as the increased probability of function change following gene duplication. The method performs well on leave-one-out cross-validation, and we further validated some of the predictions in the experimental scientific literature.
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spelling pubmed-79248012021-03-10 Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees Vega Yon, George G. Thomas, Duncan C. Morrison, John Mi, Huaiyu Thomas, Paul D. Marjoram, Paul PLoS Comput Biol Research Article Gene function annotation is important for a variety of downstream analyses of genetic data. But experimental characterization of function remains costly and slow, making computational prediction an important endeavor. Phylogenetic approaches to prediction have been developed, but implementation of a practical Bayesian framework for parameter estimation remains an outstanding challenge. We have developed a computationally efficient model of evolution of gene annotations using phylogenies based on a Bayesian framework using Markov Chain Monte Carlo for parameter estimation. Unlike previous approaches, our method is able to estimate parameters over many different phylogenetic trees and functions. The resulting parameters agree with biological intuition, such as the increased probability of function change following gene duplication. The method performs well on leave-one-out cross-validation, and we further validated some of the predictions in the experimental scientific literature. Public Library of Science 2021-02-18 /pmc/articles/PMC7924801/ /pubmed/33600408 http://dx.doi.org/10.1371/journal.pcbi.1007948 Text en © 2021 Vega Yon et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Vega Yon, George G.
Thomas, Duncan C.
Morrison, John
Mi, Huaiyu
Thomas, Paul D.
Marjoram, Paul
Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees
title Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees
title_full Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees
title_fullStr Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees
title_full_unstemmed Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees
title_short Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees
title_sort bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924801/
https://www.ncbi.nlm.nih.gov/pubmed/33600408
http://dx.doi.org/10.1371/journal.pcbi.1007948
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