Practical application of a Bayesian network approach to poultry epigenetics and stress
Abstract Background Relationships among genetic or epigenetic features can be explored by learning probabilistic networks and unravelling the dependencies among a set of given genetic/epigenetic features. Bayesian networks (BNs) consist of nodes that represent the variables and arcs that represent t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-07-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-04800-0 |
| _version_ | 1811328394263003136 |
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| author | Emiliano A. Videla Rodriguez Fábio Pértille Carlos Guerrero-Bosagna John B. O. Mitchell Per Jensen V. Anne Smith |
| author_facet | Emiliano A. Videla Rodriguez Fábio Pértille Carlos Guerrero-Bosagna John B. O. Mitchell Per Jensen V. Anne Smith |
| author_sort | Emiliano A. Videla Rodriguez |
| collection | DOAJ |
| description | Abstract Background Relationships among genetic or epigenetic features can be explored by learning probabilistic networks and unravelling the dependencies among a set of given genetic/epigenetic features. Bayesian networks (BNs) consist of nodes that represent the variables and arcs that represent the probabilistic relationships between the variables. However, practical guidance on how to make choices among the wide array of possibilities in Bayesian network analysis is limited. Our study aimed to apply a BN approach, while clearly laying out our analysis choices as an example for future researchers, in order to provide further insights into the relationships among epigenetic features and a stressful condition in chickens (Gallus gallus). Results Chickens raised under control conditions (n = 22) and chickens exposed to a social isolation protocol (n = 24) were used to identify differentially methylated regions (DMRs). A total of 60 DMRs were selected by a threshold, after bioinformatic pre-processing and analysis. The treatment was included as a binary variable (control = 0; stress = 1). Thereafter, a BN approach was applied: initially, a pre-filtering test was used for identifying pairs of features that must not be included in the process of learning the structure of the network; then, the average probability values for each arc of being part of the network were calculated; and finally, the arcs that were part of the consensus network were selected. The structure of the BN consisted of 47 out of 61 features (60 DMRs and the stressful condition), displaying 43 functional relationships. The stress condition was connected to two DMRs, one of them playing a role in tight and adhesive intracellular junctions in organs such as ovary, intestine, and brain. Conclusions We clearly explain our steps in making each analysis choice, from discrete BN models to final generation of a consensus network from multiple model averaging searches. The epigenetic BN unravelled functional relationships among the DMRs, as well as epigenetic features in close association with the stressful condition the chickens were exposed to. The DMRs interacting with the stress condition could be further explored in future studies as possible biomarkers of stress in poultry species. |
| first_indexed | 2024-04-13T15:24:18Z |
| format | Article |
| id | doaj.art-3feaca9ff4f44e56b6553a755136b795 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T15:24:18Z |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-3feaca9ff4f44e56b6553a755136b7952022-12-22T02:41:33ZengBMCBMC Bioinformatics1471-21052022-07-0123111610.1186/s12859-022-04800-0Practical application of a Bayesian network approach to poultry epigenetics and stressEmiliano A. Videla Rodriguez0Fábio Pértille1Carlos Guerrero-Bosagna2John B. O. Mitchell3Per Jensen4V. Anne Smith5School of Biology, University of St AndrewsEnvironmental Toxicology Program, Institute of Organismal Biology, Uppsala UniversityEnvironmental Toxicology Program, Institute of Organismal Biology, Uppsala UniversityEaStCHEM School of Chemistry, University of St AndrewsAVIAN Behavioural Genomics and Physiology Group, Department of Physics, Chemistry and Biology, Linköping UniversitySchool of Biology, University of St AndrewsAbstract Background Relationships among genetic or epigenetic features can be explored by learning probabilistic networks and unravelling the dependencies among a set of given genetic/epigenetic features. Bayesian networks (BNs) consist of nodes that represent the variables and arcs that represent the probabilistic relationships between the variables. However, practical guidance on how to make choices among the wide array of possibilities in Bayesian network analysis is limited. Our study aimed to apply a BN approach, while clearly laying out our analysis choices as an example for future researchers, in order to provide further insights into the relationships among epigenetic features and a stressful condition in chickens (Gallus gallus). Results Chickens raised under control conditions (n = 22) and chickens exposed to a social isolation protocol (n = 24) were used to identify differentially methylated regions (DMRs). A total of 60 DMRs were selected by a threshold, after bioinformatic pre-processing and analysis. The treatment was included as a binary variable (control = 0; stress = 1). Thereafter, a BN approach was applied: initially, a pre-filtering test was used for identifying pairs of features that must not be included in the process of learning the structure of the network; then, the average probability values for each arc of being part of the network were calculated; and finally, the arcs that were part of the consensus network were selected. The structure of the BN consisted of 47 out of 61 features (60 DMRs and the stressful condition), displaying 43 functional relationships. The stress condition was connected to two DMRs, one of them playing a role in tight and adhesive intracellular junctions in organs such as ovary, intestine, and brain. Conclusions We clearly explain our steps in making each analysis choice, from discrete BN models to final generation of a consensus network from multiple model averaging searches. The epigenetic BN unravelled functional relationships among the DMRs, as well as epigenetic features in close association with the stressful condition the chickens were exposed to. The DMRs interacting with the stress condition could be further explored in future studies as possible biomarkers of stress in poultry species.https://doi.org/10.1186/s12859-022-04800-0Bayesian networksDifferential methylationEpigeneticsPoultryStress |
| spellingShingle | Emiliano A. Videla Rodriguez Fábio Pértille Carlos Guerrero-Bosagna John B. O. Mitchell Per Jensen V. Anne Smith Practical application of a Bayesian network approach to poultry epigenetics and stress BMC Bioinformatics Bayesian networks Differential methylation Epigenetics Poultry Stress |
| title | Practical application of a Bayesian network approach to poultry epigenetics and stress |
| title_full | Practical application of a Bayesian network approach to poultry epigenetics and stress |
| title_fullStr | Practical application of a Bayesian network approach to poultry epigenetics and stress |
| title_full_unstemmed | Practical application of a Bayesian network approach to poultry epigenetics and stress |
| title_short | Practical application of a Bayesian network approach to poultry epigenetics and stress |
| title_sort | practical application of a bayesian network approach to poultry epigenetics and stress |
| topic | Bayesian networks Differential methylation Epigenetics Poultry Stress |
| url | https://doi.org/10.1186/s12859-022-04800-0 |
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