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FoodMicrobionet v4: a large, integrated, open and transparent database for food bacterial communities

View ORCID ProfileEugenio Parente, View ORCID ProfileTeresa Zotta, View ORCID ProfileAnnamaria Ricciardi
doi: https://doi.org/10.1101/2022.01.19.476946
Eugenio Parente
Scuola di Scienze Agrarie, Forestali, Alimentari ed Ambientali, Università degli Studi della Basilicata, Potenza, Italy
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  • For correspondence: eugenio.parente@unibas.it
Teresa Zotta
Scuola di Scienze Agrarie, Forestali, Alimentari ed Ambientali, Università degli Studi della Basilicata, Potenza, Italy
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Annamaria Ricciardi
Scuola di Scienze Agrarie, Forestali, Alimentari ed Ambientali, Università degli Studi della Basilicata, Potenza, Italy
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Abstract

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 1,684 environmental samples and 8,467 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).

Using data in 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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Two proofs of concept providing evidence on the potential success of taxonomic assignment of different target regions of the 16S RNA gene were added. A few changes required by reviewers of the version submitted to IJFM were also added.

  • https://github.com/ep142/FoodMicrobionet

  • https://data.mendeley.com/datasets/8fwwjpm79y/5

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 25, 2022.
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FoodMicrobionet v4: a large, integrated, open and transparent database for food bacterial communities
Eugenio Parente, Teresa Zotta, Annamaria Ricciardi
bioRxiv 2022.01.19.476946; doi: https://doi.org/10.1101/2022.01.19.476946
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FoodMicrobionet v4: a large, integrated, open and transparent database for food bacterial communities
Eugenio Parente, Teresa Zotta, Annamaria Ricciardi
bioRxiv 2022.01.19.476946; doi: https://doi.org/10.1101/2022.01.19.476946

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