New data and workflow.

A new version of DairyFMBN has been added to Mendeley data (https://data.mendeley.com/datasets/3cwf729p34/5) and quarantine will expire at the end of July. This is connected to an astounding ( 😉 )workflow for inference of microbial association networks, which is available on GitHub (https://github.com/ep142/MAN_in_cheese). Both are related to a review on microbial association networks in cheese which is about to be published as a preprint…

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 paper

Here’s a new paper. Zotta, T., Ricciardi, A., Condelli, N., Parente, E., 2021. Metataxonomic and metagenomic approaches for the study of undefined strain starters for cheese manufacture. Critical Reviews in Food Science and Nutrition. doi:10.1080/10408398.2020.1870927Most of the figures have been generated by extracting data from DairyFMBN. 50 free e-reprints are available here: https://www.tandfonline.com/eprint/3C6T6Y8Y7X5MTGPYTUAV/full?target=10.1080/10408398.2020.1870927

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.

FoodMicrobionet and SILVA

Latest additions in FoodMicrobionet are done using SILVA 138 SSU. This version introduces several differences over previous version, especially in higher level taxonomy (2/3 taxonomic paths have been changed). I have compared taxonomic assignments with SILVA v132 with those of v138 using 5 recent studies targeting different 16S RNA gene regions (V1-V3, V3-V4, V4-V5) and found that:

  • ≥95% of the sequences in each study are identified at the genus or species level in the same way
  • overall, the matching identifications at the genus level range from 70 to 90%; differences are usually due to sequences of poor quality (which receive ambiguous identifications with either BLASTn or SEQMATCH)

Overall, while the best way to compare results of different studies is to re-analyse the data using the same pipeline and the same version of the taxonomic database, I still feel that comparing different studies at the genus level is a reasonable compromise. In addition, with FoodMicrobionet you always have the option of selecting studies which are as close as possible in terms of target, platform and pipeline. However, due to the changes in the higher level taxonomy, I have decided to make the higher level taxonomy (i.e. above the genus level) compatible with SILVA 138 SSU, even if this is sometimes in contrast with NCBI taxonomy or LPSN.

One last issue is with the new classification of the former genus Lactobacillus. The new classification has been incorporated in LPCN and NCBI taxonomy, but not in Florilege nor in SILVA, and searches with the old species names still work. Therefore I have decided to leave things as they are (and add a small hidden switch in the code of ShinyFMBN which allows you to convert old names into the new ones).

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.

New review

We have just published a new review:

Parente, E., Ricciardi, A., Zotta, T. 2020. The microbiota of dairy milk: a review. International Dairy Journal, in press, doi:10.1016/j.idairyj.2020.104714

The review is an example of how data in FoodMicrobionet can be exploited for quantitative metastudies.

You can access it for free using this link, which will expire on June 18th, 2020. The version of FoodMicrobionet used in the review in freely available on Mendeley data.

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.