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Bayesian prediction of microbial oxygen requirement

Background: Prediction of the optimal habitat conditions for a given bacterium, based on genome sequence alone would be of value for scientific as well as industrial purposes. One example of such a habitat adaptation is the requirement for oxygen. In spite of good genome data availability, there hav...

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Autores principales: Jensen, Dan B., Ussery, David W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000Research 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4743139/
https://www.ncbi.nlm.nih.gov/pubmed/26913185
http://dx.doi.org/10.12688/f1000research.2-184.v1
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author Jensen, Dan B.
Ussery, David W.
author_facet Jensen, Dan B.
Ussery, David W.
author_sort Jensen, Dan B.
collection PubMed
description Background: Prediction of the optimal habitat conditions for a given bacterium, based on genome sequence alone would be of value for scientific as well as industrial purposes. One example of such a habitat adaptation is the requirement for oxygen. In spite of good genome data availability, there have been only a few prediction attempts of bacterial oxygen requirements, using genome sequences. Here, we describe a method for distinguishing aerobic, anaerobic and facultative anaerobic bacteria, based on genome sequence-derived input, using naive Bayesian inference. In contrast, other studies found in literature only demonstrate the ability to distinguish two classes at a time. Results: The results shown in the present study are as good as or better than comparable methods previously described in the scientific literature, with an arguably simpler method, when results are directly compared. This method further compares the performance of a single-step naive Bayesian prediction of the three included classifications, compared to a simple Bayesian network with two steps. A two-step network, distinguishing first respiring from non-respiring organisms, followed by the distinction of aerobe and facultative anaerobe organisms within the respiring group, is found to perform best. Conclusions: A simple naive Bayesian network based on the presence or absence of specific protein domains within a genome is an effective and easy way to predict bacterial habitat preferences, such as oxygen requirement.
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spelling pubmed-47431392016-02-23 Bayesian prediction of microbial oxygen requirement Jensen, Dan B. Ussery, David W. F1000Res Method Article Background: Prediction of the optimal habitat conditions for a given bacterium, based on genome sequence alone would be of value for scientific as well as industrial purposes. One example of such a habitat adaptation is the requirement for oxygen. In spite of good genome data availability, there have been only a few prediction attempts of bacterial oxygen requirements, using genome sequences. Here, we describe a method for distinguishing aerobic, anaerobic and facultative anaerobic bacteria, based on genome sequence-derived input, using naive Bayesian inference. In contrast, other studies found in literature only demonstrate the ability to distinguish two classes at a time. Results: The results shown in the present study are as good as or better than comparable methods previously described in the scientific literature, with an arguably simpler method, when results are directly compared. This method further compares the performance of a single-step naive Bayesian prediction of the three included classifications, compared to a simple Bayesian network with two steps. A two-step network, distinguishing first respiring from non-respiring organisms, followed by the distinction of aerobe and facultative anaerobe organisms within the respiring group, is found to perform best. Conclusions: A simple naive Bayesian network based on the presence or absence of specific protein domains within a genome is an effective and easy way to predict bacterial habitat preferences, such as oxygen requirement. F1000Research 2013-09-13 /pmc/articles/PMC4743139/ /pubmed/26913185 http://dx.doi.org/10.12688/f1000research.2-184.v1 Text en Copyright: © 2013 Jensen DB and Ussery DW http://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method Article
Jensen, Dan B.
Ussery, David W.
Bayesian prediction of microbial oxygen requirement
title Bayesian prediction of microbial oxygen requirement
title_full Bayesian prediction of microbial oxygen requirement
title_fullStr Bayesian prediction of microbial oxygen requirement
title_full_unstemmed Bayesian prediction of microbial oxygen requirement
title_short Bayesian prediction of microbial oxygen requirement
title_sort bayesian prediction of microbial oxygen requirement
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4743139/
https://www.ncbi.nlm.nih.gov/pubmed/26913185
http://dx.doi.org/10.12688/f1000research.2-184.v1
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