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Microbiome dataset analysis from a shrimp pond in Ninh Thuan, Vietnam using shotgun metagenomics

Vietnam is one of the top shrimp producing and exporting countries in the world [1]. However, viral and bacterial epidemic diseases cause severe damages to shrimp farming, resulting in millions of US dollars losses annually [2]. Furthermore, inappropriate use of antibiotics in shrimp rearing lead to...

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Detalles Bibliográficos
Autores principales: Le, Lan-Anh, Nguyen-Hoang, Thien-Phuc, Huynh, Van-Phuc, Nguyen, Tan-Huy, Nguyen, Thuy-Vy, Ho-Huynh, Thuy-Duong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7264489/
https://www.ncbi.nlm.nih.gov/pubmed/32509936
http://dx.doi.org/10.1016/j.dib.2020.105731
Descripción
Sumario:Vietnam is one of the top shrimp producing and exporting countries in the world [1]. However, viral and bacterial epidemic diseases cause severe damages to shrimp farming, resulting in millions of US dollars losses annually [2]. Furthermore, inappropriate use of antibiotics in shrimp rearing lead to increased emergence of drug resistant pathogens [3]. Current practices for water quality control, mostly based on chemical and physical parameters; neglected biological criteria necessary for maintaining pond health. Ninh Thuan is a region situated in the South Central Coast of Vietnam. Due to its geographic location, a large part of this region is dedicated to shrimp (Litopenaeus vannamei) post-larvae production and rearing. This article presents a microbiome dataset from two water samples collected in a shrimp rearing pond in Ninh Thuan. We used Oxford Nanopore Technologies (ONT) for metagenomic sequencing of the samples to characterize microbial communities and antibiotic resistance profiles. The metagenome dataset generated will provide an understanding and comparison framework of the microbial diversity and functionality among shrimp ponds with potential application in health management and shrimp rearing industry.