Scuola di Scienze Agrarie, Forestali, Alimentari ed Ambientali, Università degli Studi della Basilicata, Potenza, Italy.
Scuola di Scienze Agrarie, Forestali, Alimentari ed Ambientali, Università degli Studi della Basilicata, Potenza, Italy.
Int J Food Microbiol. 2022 Jul 2;372:109696. doi: 10.1016/j.ijfoodmicro.2022.109696. Epub 2022 May 2.
With the availability of high-throughput sequencing techniques our knowledge of the structure and dynamics of food microbial communities has made a quantum leap. However, this knowledge is dispersed in a large number of papers and hard data are only partly available through powerful on-line databases and tools such as QIITA, MGnify and the Integrated Microbial Next Generation Sequencing platform, whose annotation is not optimized for foods. Here, we present the 4th iteration of FoodMicrobionet, a database of the composition of bacterial microbial communities of foods and food environments. With 180 studies and 10,151 samples belonging to 8 major food groups FoodMicrobionet 4.1.2 is arguably the largest and best annotated database on food bacterial communities. This version includes 1684 environmental samples and 8467 food samples, belonging to 16 L1 categories and 196 L6 categories of the EFSA FoodEx2 classification and is approximately 4 times larger than previous version (3.1, https://doi.org/10.1016/j.ijfoodmicro.2019.108249). As a representative case study among the many potential applications of FoodMicrobionet, we confirm that taxonomic assignment at the genus level can be performed confidently for the majority of amplicon sequence variants using the most commonly used 16S RNA gene target regions (V1-V3, V3-V4, V4), with best results with higher quality sequences and longer fragment lengths, but that care should be exercised in confirming the assignment at species level. Both FoodMicrobionet and related data and software conform to FAIR (findable, accessible, interoperable, reusable/reproducible) criteria for scientific data and software and are freely available on public repositories (GitHub, Mendeley data). Even if FoodMicrobionet does not have the sophistication of QIITA, IMNGS and MGnify, we feel that this iteration, due to its size and diversity, provides a valuable asset for both the scientific community and industrial and regulatory stakeholders.
随着高通量测序技术的出现，我们对食品微生物群落的结构和动态的了解有了质的飞跃。然而，这些知识分散在大量的论文中，只有通过强大的在线数据库和工具（如 QIITA、MGnify 和 Integrated Microbial Next Generation Sequencing 平台）部分获得硬数据，这些数据库和工具的注释并没有针对食品进行优化。在这里，我们展示了 FoodMicrobionet 的第 4 个版本，这是一个食品和食品环境中细菌微生物群落组成的数据库。FoodMicrobionet 4.1.2 包含 180 项研究和 10151 个样本，属于 8 个主要食品组，是目前最大和最好注释的食品细菌群落数据库。该版本包括 1684 个环境样本和 8467 个食品样本，属于 EFSA FoodEx2 分类的 16 个 L1 类别和 196 个 L6 类别，大约是上一版本（3.1，https://doi.org/10.1016/j.ijfoodmicro.2019.108249）的 4 倍。作为 FoodMicrobionet 众多潜在应用中的一个代表性案例研究，我们证实，使用最常用的 16S RNA 基因靶区域（V1-V3、V3-V4、V4），在属水平上对大多数扩增子序列变体进行分类分配是可以置信的，结果最好的是具有更高质量序列和更长片段长度的序列，但在确认种水平的分配时应谨慎。FoodMicrobionet 及其相关数据和软件符合科学数据和软件的 FAIR（可发现、可访问、可互操作、可重复使用/可重现）标准，并可在公共存储库（GitHub、Mendeley Data）上免费获得。即使 FoodMicrobionet 没有 QIITA、IMNGS 和 MGnify 那么复杂，我们也认为由于其规模和多样性，该版本为科学界以及工业和监管利益相关者提供了有价值的资产。
