Bayesian prediction of microbial oxygen requirement [v1; ref status: indexed, http://f1000r.es/1m6]
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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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2013-09-01
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| Series: | F1000Research |
| Subjects: | |
| Online Access: | http://f1000research.com/articles/2-184/v1 |
| _version_ | 1818516914733842432 |
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| author | Dan B. Jensen David W. Ussery |
| author_facet | Dan B. Jensen David W. Ussery |
| author_sort | Dan B. Jensen |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-11T00:48:51Z |
| format | Article |
| id | doaj.art-182092e6861f442e92bf0ff6214745c7 |
| institution | Directory Open Access Journal |
| issn | 2046-1402 |
| language | English |
| last_indexed | 2024-12-11T00:48:51Z |
| publishDate | 2013-09-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj.art-182092e6861f442e92bf0ff6214745c72022-12-22T01:26:41ZengF1000 Research LtdF1000Research2046-14022013-09-01210.12688/f1000research.2-184.v12094Bayesian prediction of microbial oxygen requirement [v1; ref status: indexed, http://f1000r.es/1m6]Dan B. Jensen0David W. Ussery1Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, DenmarkComparative Genomics Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USABackground: 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.http://f1000research.com/articles/2-184/v1BioinformaticsMicrobial Evolution & GenomicsMicrobial Physiology & Metabolism |
| spellingShingle | Dan B. Jensen David W. Ussery Bayesian prediction of microbial oxygen requirement [v1; ref status: indexed, http://f1000r.es/1m6] F1000Research Bioinformatics Microbial Evolution & Genomics Microbial Physiology & Metabolism |
| title | Bayesian prediction of microbial oxygen requirement [v1; ref status: indexed, http://f1000r.es/1m6] |
| title_full | Bayesian prediction of microbial oxygen requirement [v1; ref status: indexed, http://f1000r.es/1m6] |
| title_fullStr | Bayesian prediction of microbial oxygen requirement [v1; ref status: indexed, http://f1000r.es/1m6] |
| title_full_unstemmed | Bayesian prediction of microbial oxygen requirement [v1; ref status: indexed, http://f1000r.es/1m6] |
| title_short | Bayesian prediction of microbial oxygen requirement [v1; ref status: indexed, http://f1000r.es/1m6] |
| title_sort | bayesian prediction of microbial oxygen requirement v1 ref status indexed http f1000r es 1m6 |
| topic | Bioinformatics Microbial Evolution & Genomics Microbial Physiology & Metabolism |
| url | http://f1000research.com/articles/2-184/v1 |
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