Dataset Information

16S rRNA gene sequencing data of the human skin microbiome before and after swimming in the ocean.

ABSTRACT: These data represent the abundance, diversity and predicted function gene profiles of the microbial communities present on human skin before and after swimming in the ocean. The skin microbiome has been shown to provide protection against infection from pathogenic bacteria. It is well-known that exposure to ocean water can cause skin infection, but little is known about how exposure can alter the bacterial communities on the skin. Skin microbiome samples were collected from human participants before and after swimming in the ocean. These data were used to analyze the changes in abundance and diversity of microbial communities on the skin and the changes in the functional profiles of the bacteria, specifically focusing on genes involved in antibiotic resistance and bacterial virulence.

SUBMITTER: Nielsen MC

PROVIDER: S-EPMC8215175 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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16S rRNA gene sequencing data of the human skin microbiome before and after swimming in the ocean.

Nielsen Marisa C MC Jiang Sunny C SC

Data in brief 20210609

These data represent the abundance, diversity and predicted function gene profiles of the microbial communities present on human skin before and after swimming in the ocean. The skin microbiome has been shown to provide protection against infection from pathogenic bacteria. It is well-known that exposure to ocean water can cause skin infection, but little is known about how exposure can alter the bacterial communities on the skin. Skin microbiome samples were collected from human participants be ...[more]

PMID: 34189199

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Project description:BackgroundIn the last few years, 16S rRNA gene sequencing (16S rDNA-seq) has seen a surprisingly rapid increase in election rate as a methodology to perform microbial community studies. Despite the considerable popularity of this technique, an exiguous number of specific tools are currently available for proper 16S rDNA-seq count data preprocessing and simulation. Indeed, the great majority of tools have been developed adapting methodologies previously used for bulk RNA-seq data, with poor assessment of their applicability in the metagenomics field. For such tools and the few ones specifically developed for 16S rDNA-seq data, performance assessment is challenging, mainly due to the complex nature of the data and the lack of realistic simulation models. In fact, to the best of our knowledge, no software thought for data simulation are available to directly obtain synthetic 16S rDNA-seq count tables that properly model heavy sparsity and compositionality typical of these data.ResultsIn this paper we present metaSPARSim, a sparse count matrix simulator intended for usage in development of 16S rDNA-seq metagenomic data processing pipelines. metaSPARSim implements a new generative process that models the sequencing process with a Multivariate Hypergeometric distribution in order to realistically simulate 16S rDNA-seq count table, resembling real experimental data compositionality and sparsity. It provides ready-to-use count matrices and comes with the possibility to reproduce different pre-coded scenarios and to estimate simulation parameters from real experimental data. The tool is made available at http://sysbiobig.dei.unipd.it/?q=Software#metaSPARSimand https://gitlab.com/sysbiobig/metasparsim.ConclusionmetaSPARSim is able to generate count matrices resembling real 16S rDNA-seq data. The availability of count data simulators is extremely valuable both for methods developers, for which a ground truth for tools validation is needed, and for users who want to assess state of the art analysis tools for choosing the most accurate one. Thus, we believe that metaSPARSim is a valuable tool for researchers involved in developing, testing and using robust and reliable data analysis methods in the context of 16S rRNA gene sequencing.

Project description:BackgroundSalmonella enterica is a common cause of foodborne gastroenteritis in the United States and is associated with outbreaks in fresh produce such as cilantro. Salmonella culture-based detection methods are complex and time consuming, and improvments to increase detection sensitivity will benefit consumers. In this study, we used 16S rRNA sequencing to determine the microbiome of cilantro. We also investigated changes to the microbial community prior to and after a 24-hour nonselective pre-enrichment culture step commonly used by laboratory analysts to resuscitate microorganisms in foods suspected of contamination with pathogens. Cilantro samples were processed for Salmonella detection according to the method in the United States Food and Drug Administration Bacteriological Analytical Manual. Genomic DNA was extracted from culture supernatants prior to and after a 24-hour nonselective pre-enrichment step and 454 pyrosequencing was performed on 16S rRNA amplicon libraries. A database of Enterobacteriaceae 16S rRNA sequences was created, and used to screen the libraries for Salmonella, as some samples were known to be culture positive. Additionally, culture positive cilantro samples were examined for the presence of Salmonella using shotgun metagenomics on the Illumina MiSeq.ResultsTime zero uncultured samples had an abundance of Proteobacteria while the 24-hour enriched samples were composed mostly of Gram-positive Firmicutes. Shotgun metagenomic sequencing of Salmonella culture positive cilantro samples revealed variable degrees of Salmonella contamination among the sequenced samples.ConclusionsOur cilantro study demonstrates the use of high-throughput sequencing to reveal the microbiome of cilantro, and how the microbiome changes during the culture-based protocols employed by food safety laboratories to detect foodborne pathogens. Finding that culturing the cilantro shifts the microbiome to a predominance of Firmicutes suggests that changing our culture-based methods will improve detection sensitivity for foodborne enteric pathogens.

Dataset Information

16S rRNA gene sequencing data of the human skin microbiome before and after swimming in the ocean.

Publications

16S rRNA gene sequencing data of the human skin microbiome before and after swimming in the ocean.

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