SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach

Gut microbiomes of fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these interactions. Here, we introduc...

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Autores: Soriano, Beatriz, Hafez, Ahmed, Naya-Català, Fernando, Moroni, Federico, Moldovan, Roxana Andreea, Toxqui-Rodríguez, S., Piazzon de Haro, María Carla, Arnau, Vicente, Llorens, Carlos, Pérez-Sánchez, Jaume
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/334435
Acceso en línea:http://hdl.handle.net/10261/334435
Access Level:acceso abierto
Palabra clave:Bayesian networks
Metagenomics
Machine learning
Farmed fish
Gilthead sea bream
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spelling SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian ApproachSoriano, BeatrizHafez, AhmedNaya-Català, FernandoMoroni, FedericoMoldovan, Roxana AndreeaToxqui-Rodríguez, S.Piazzon de Haro, María CarlaArnau, VicenteLlorens, CarlosPérez-Sánchez, JaumeBayesian networksMetagenomicsMachine learningFarmed fishGilthead sea breamGut microbiomes of fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these interactions. Here, we introduce SAMBA (Structure-Learning of Aquaculture Microbiomes using a Bayesian Approach), a computational tool that uses a unified Bayesian network approach to model the network structure of fish gut microbiomes and their interactions with biotic and abiotic variables associated with typical aquaculture systems. SAMBA accepts input data on microbial abundance from 16S rRNA amplicons as well as continuous and categorical information from distinct farming conditions. From this, SAMBA can create and train a network model scenario that can be used to (i) infer information of how specific farming conditions influence the diversity of the gut microbiome or pan-microbiome, and (ii) predict how the diversity and functional profile of that microbiome would change under other variable conditions. SAMBA also allows the user to visualize, manage, edit, and export the acyclic graph of the modelled network. Our study presents examples and test results of Bayesian network scenarios created by SAMBA using data from a microbial synthetic community, and the pan-microbiome of gilthead sea bream (Sparus aurata) in different feeding trials. It is worth noting that the usage of SAMBA is not limited to aquaculture systems as it can be used for modelling microbiome–host network relationships of any vertebrate organism, including humans, in any system and/or ecosystem.This work was supported by the Spanish MCIN project Bream-AquaINTECH (RTI2018–094128-B-I00, AEI/FEDER, UE) to JP-S. This study also forms part of the ThinkInAzul programme and was supported by MCINN with funding from European Union NextGenerationEU (PRTR-C17.I1) and by Generalitat Valenciana (THINKINAZUL/2021/024) to JP-S. BS was supported by a pre-doctoral research fellowship from Industrial Doctorate of MINECO (DI-17-09134). FN-C was supported by a research contract from the EU H2020 Research Innovation Program under grant agreement no. 818367 (AquaIMPACT). FM was funded by a research contract from the EU H2020 Research Innovation Program under grant agreement no. 871108 (AQUAEXCEL3.0). MCP was funded by a Ramón y Cajal Postdoctoral Research Fellowship (RYC2018-024049-I co-funded by AEI, European Social Fund (ESF) and ACOND/2022 Generalitat Valenciana).Peer reviewedMultidisciplinary Digital Publishing InstituteMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)European CommissionGeneralitat ValencianaConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2023202320232023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/334435reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094128-B-I00info:eu-repo/grantAgreement/EC/H2020/818367info:eu-repo/grantAgreement/EC/H2020/871108info:eu-repo/grantAgreement/AEI//RYC2018-024049-Ihttps://doi.org/10.3390/genes14081650Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3344352026-05-22T06:33:51Z
dc.title.none.fl_str_mv SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach
title SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach
spellingShingle SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach
Soriano, Beatriz
Bayesian networks
Metagenomics
Machine learning
Farmed fish
Gilthead sea bream
title_short SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach
title_full SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach
title_fullStr SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach
title_full_unstemmed SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach
title_sort SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach
dc.creator.none.fl_str_mv Soriano, Beatriz
Hafez, Ahmed
Naya-Català, Fernando
Moroni, Federico
Moldovan, Roxana Andreea
Toxqui-Rodríguez, S.
Piazzon de Haro, María Carla
Arnau, Vicente
Llorens, Carlos
Pérez-Sánchez, Jaume
author Soriano, Beatriz
author_facet Soriano, Beatriz
Hafez, Ahmed
Naya-Català, Fernando
Moroni, Federico
Moldovan, Roxana Andreea
Toxqui-Rodríguez, S.
Piazzon de Haro, María Carla
Arnau, Vicente
Llorens, Carlos
Pérez-Sánchez, Jaume
author_role author
author2 Hafez, Ahmed
Naya-Català, Fernando
Moroni, Federico
Moldovan, Roxana Andreea
Toxqui-Rodríguez, S.
Piazzon de Haro, María Carla
Arnau, Vicente
Llorens, Carlos
Pérez-Sánchez, Jaume
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
European Commission
Generalitat Valenciana
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Bayesian networks
Metagenomics
Machine learning
Farmed fish
Gilthead sea bream
topic Bayesian networks
Metagenomics
Machine learning
Farmed fish
Gilthead sea bream
description Gut microbiomes of fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these interactions. Here, we introduce SAMBA (Structure-Learning of Aquaculture Microbiomes using a Bayesian Approach), a computational tool that uses a unified Bayesian network approach to model the network structure of fish gut microbiomes and their interactions with biotic and abiotic variables associated with typical aquaculture systems. SAMBA accepts input data on microbial abundance from 16S rRNA amplicons as well as continuous and categorical information from distinct farming conditions. From this, SAMBA can create and train a network model scenario that can be used to (i) infer information of how specific farming conditions influence the diversity of the gut microbiome or pan-microbiome, and (ii) predict how the diversity and functional profile of that microbiome would change under other variable conditions. SAMBA also allows the user to visualize, manage, edit, and export the acyclic graph of the modelled network. Our study presents examples and test results of Bayesian network scenarios created by SAMBA using data from a microbial synthetic community, and the pan-microbiome of gilthead sea bream (Sparus aurata) in different feeding trials. It is worth noting that the usage of SAMBA is not limited to aquaculture systems as it can be used for modelling microbiome–host network relationships of any vertebrate organism, including humans, in any system and/or ecosystem.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/334435
url http://hdl.handle.net/10261/334435
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094128-B-I00
info:eu-repo/grantAgreement/EC/H2020/818367
info:eu-repo/grantAgreement/EC/H2020/871108
info:eu-repo/grantAgreement/AEI//RYC2018-024049-I
https://doi.org/10.3390/genes14081650
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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