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Benchmarking AI-based plasmid annotation tools for antibiotic resistance genes mining from metagenome of the Virilla River, Costa Rica

dc.creatorRojas Villalta, Dorian
dc.creatorCalderón Osorno, Melany
dc.creatorBarrantes Jiménez, Kenia
dc.creatorArias Andrés, María de Jesús
dc.creatorRojas Jiménez, Keilor Osvaldo
dc.date.accessioned2023-10-06T21:01:33Z
dc.date.available2023-10-06T21:01:33Z
dc.date.issued2023
dc.description.abstractBioinformatics and Artificial Intelligence (AI) stand as rapidly evolving tools that have facilitated the annotation of mobile genetic elements (MGEs), enabling the prediction of health risk factors in polluted environments, such as antibiotic resistance genes (ARGs). This study aims to assess the performance of four AI-based plasmid annotation tools (Plasflow, Platon, RFPlasmid, and PlasForest) by employing defined performance parameters for the identification of ARGs in the metagenome of one sediment obtained from the Virilla River, Costa Rica. We extracted complete DNA, sequenced it, assembled the metagenome, and then performed the plasmid prediction with each bioinformatic tool, and the ARGs annotation using the Resistance Gene Identifier web portal. Sensitivity, specificity, precision, negative predictive value, accuracy, and F1-score were calculated for each ARGs prediction result of the evaluated plasmidomes. Notably, Platon emerged as the highest performer among the assessed tools, exhibiting exceptional scores. Conversely, Plasflow seems to face difficulties distinguishing between chromosomal and plasmid sequences, while PlasForest has encountered limitations when handling small contigs. RFPlasmid displayed diminished specificity and was outperformed by its taxon-dependent work-flow. We recommend the adoption of Platon as the preferred bioinformatic tool for resistome investigations in the taxonindependent environmental metagenomic domain. Meanwhile, RFPlasmid presents a compelling choice for taxon-dependent prediction due to its exclusive incorporation of this approach. We expect that the results of this study serve as a guiding resource in selecting AI-based tools for accurately predicting the plasmidome and its associated genes.es_ES
dc.description.procedenceUCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de Biologíaes_ES
dc.description.procedenceUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Instituto de Investigaciones en Salud (INISA)es_ES
dc.identifier.citationhttps://www.biorxiv.org/content/10.1101/2023.08.24.554652v1es_ES
dc.identifier.doi10.1101/2023.08.24.554652
dc.identifier.urihttps://hdl.handle.net/10669/90101
dc.language.isoenges_ES
dc.rightsacceso abierto
dc.sourcebioRxives_ES
dc.subjectBENCHMARKINGes_ES
dc.subjectSCIENTIFIC EQUIPMENTes_ES
dc.subjectARTIFICIAL INTELLIGENCEes_ES
dc.subjectGENESes_ES
dc.subjectCOSTA RICAes_ES
dc.titleBenchmarking AI-based plasmid annotation tools for antibiotic resistance genes mining from metagenome of the Virilla River, Costa Ricaes_ES
dc.typeartículo preliminares_ES

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