Bacterial interactions in cheese

Using 34 studies in the last version of DairyFMBN I have inferred microbial association networks (at the genus level) using NetCoMi, with four different methods (SparCC, CCREPE, SPRING, SpiecEasi). I then calculated how often an association was detected (by any method in any study) and which was the average stability (i.e. the mean of the number of times a given association was detected by the 4 methods within a study). here are the results for the top 25 copresence and mutual exclusion associations.

New version (3.2.6)

I have made major changes to the taxonomic table of the database, which is now compatible with SILVA taxonomy. Although SILVA taxonomy does not match the taxonomy in the List of Prokaryotic names with Standing in Nomenclature the change was necessary because new studies in FoodMicrobionet are processed using SILVA v138 and this was causing inconsistencies in the higher level taxonomy of taxa (i.e. the same genus might potentially have a different lineage depending on when it was added to the database) and this, in turn, would prevent correct aggregation at levels higher than genus. In case you want to know, I did compare taxonomic assignments done with SILVA v132 and v138 for five studies using different 16S RNA gene regions as a target. The same sequence was assigned to a different “taxon” in as many as 70% of the cases. However, when doing comparisons at the genus level, >96% of the sequences were assigned to the same genus using either version of the database. Mismatches were mostly due to sequences (actually Amplicon Sequence Variants) which, when tested with Seqmatch had consistently a Sab<0.80 with the best match. Again, the best way to compare studies is to reprocess data based on the same target using exactly the same pipeline, but this is time consuming. Doing comparisons at the genus level is still a reasonable alternative: Article A comparison of bioinformatic approaches for 16S rRNA gene p…
The update version of the last public version of FoodMicrobionet should be shortly available here.

As usual, we are open to collaborations and if you are interested in obtaining data from FoodMicrobionet 3.2.6 contact me.

R 4.0.0 and Bioconductor 3.11

I have tested ShinyFMBN nor DairyFMBN with R.4.0.0 and with Bioconductor 3.11, and, as far as I can see, they both work. My advice is to check that if you have R 4.0 you should upgrade Bioconductor (the installation script does not check if the Bioconductor version installed in the system is the latest). Older version of R (3.6.3) Bioconductor (3.10) are still available for download. I will do any patching needed for compatibility as soon as I have time.

Larger, and larger

FoodMicrobionet v3.4.5 has now 120 studies and 5974 samples.

  • Animal and vegetable fats and oils and primary derivatives thereof 53
  • Composite dishes 115
  • Eggs and egg products 21
  • Fish, seafood, amphibians, reptiles and invertebrates 362
  • Fruit and fruit products292Grains and grain-based products 44
  • Major isolated ingredients, additives, flavours, baking and processing aids 196
  • Meat and meat products 1199
  • Milk and dairy products 2908
  • Vegetables and vegetable products 604
  • Sugar and similar, confectionery and water-based sweet desserts 42
  • Alcoholic beverages 30
  • Starchy roots or tubers and products thereof, sugar plants 17
  • Legumes, nuts, oilseeds and spices65Seasoning, sauces and condiments 26

This version will not be released, except in the framework of scientific agreements and project cooperations.

ShinyFMBN 2.1.2 is available for download

The development version of ShinyFMBN 2.1.2 (I had time only for limited testing) is available for download. The app includes access to records of FoodMicrobionet 3.1 and a new version of the manual is available. If you have time to test it please do and let me know:

  • if you find a bug provide enough information to reproduce the error
  • if your testing is successful (i.e. everything seems to work smoothly) please let me know which machine, operating system, browser and R version you have used for testing (see examples on page 11 of the manual)


This is a new script for converting *_agg.RDS or *_physeq.Rdata files created using the ShinyFMBN app (v1.1 or later) in OTU, taxonomy and sample metadata tables suitable for use with the Marker Data Profiling analysis pipeline of Microbiome Analyst ( Click here to download. Please beware: I am too old to be willing to write foolproof scripts and try to predict the vagaries of lazy users. If you are a fool (and/or don’t read/follow the instructions provided as comments in the script) the script will not work. Period.


Dr. Sandrine Guillou has kindly invited me to give a talk at IAFP’s European Symposium on Food Safety in Nantes. Il will be speaking in symposium S4 (Network Analysis to Better Decipher Functions and Dynamics of Food Microbial Ecosystems) and the title of my talk is: “Shining Light on Networks in Food Microbiomes: FoodMicrobionet and the ShinyFMBN App“. You can download the presentation here. While we wait for the review of the manuscript I have submitted, you can cite FMBN 3.1 using this talk: Parente, E., Ricciardi, A., Zotta, T. 2019. Shining Light on Networks in Food Microbiomes: FoodMicrobionet and the ShinyFMBN App. Proceedings of IAFP’s European Symposium on Food Safety, Nantes, April 24-26, 2019.