Dipartimento di Scienze, Università degli Studi della Basilicata, 85100 Potenza, Italy.
Department of Agricultural Sciences, Division of Microbiology, University of Naples "Federico II", 80055 Portici, Italy; Task Force on Microbiome Studies, University of Naples "Federico II", Naples, Italy.
Int J Food Microbiol. 2019 Sep 16;305:108249. doi: 10.1016/j.ijfoodmicro.2019.108249. Epub 2019 Jun 11.
We present a new version of FoodMicrobionet, a database for the exploration of food bacterial communities. The database, available as an app built with the Shiny package of R, includes data from 44 studies and 2234 samples (food or food environment), covering dairy, meat, fruit and vegetables, cereal based and ready-to-eat foods. The interactive interface allows exploration of data, access to external resources (on line versions of the studies, sequence data on SRA, taxonomic databases), filtering samples on the basis of a number of criteria, aggregation of samples and bacterial taxa and export of data in a variety of formats. FoodMicrobionet is the largest collection of data on food bacterial communities and, due to the structure of sample metadata, largely derived from the European Food Safety Agency FoodEx2 classification, makes comparison and re-analysis of data from published and unpublished studies easy. Data exported from FoodMicrobionet can be readily used for graphical and statistical meta-analyses using open-source software (Gephi, Cytoscape, CoNet, and R packages and apps, such as phyloseq and Shiny-Phyloseq) thus providing scientists, risk assessors and industry with a wealth of information on the structure of food biomes.
我们展示了 FoodMicrobionet 的新版本，这是一个用于探索食物细菌群落的数据库。该数据库作为一个使用 R 语言的 Shiny 包构建的应用程序提供，包括来自 44 项研究和 2234 个样本（食物或食物环境）的数据，涵盖了奶制品、肉类、水果和蔬菜、谷物和即食食品。交互式界面允许用户探索数据、访问外部资源（研究的在线版本、SRA 上的序列数据、分类数据库）、根据多个标准对样本进行筛选、对样本和细菌分类群进行聚合以及以多种格式导出数据。FoodMicrobionet 是最大的食物细菌群落数据集，由于样本元数据的结构，它主要来自欧洲食品安全局的 FoodEx2 分类，使得对已发表和未发表研究的数据进行比较和重新分析变得容易。从 FoodMicrobionet 导出的数据可以方便地用于使用开源软件（Gephi、Cytoscape、CoNet 和 R 包和应用程序，如 phyloseq 和 Shiny-Phyloseq）进行图形和统计元分析，从而为科学家、风险评估人员和行业提供有关食物生物群落结构的丰富信息。
