The paper describing FoodMicrobionet v 3.1 is now available:
Parente, E., De Filippis, F., Ercolini, D., Ricciardi, A., Zotta, T., 2019. Advancing integration of data on food microbiome studies: FoodMicrobionet 3.1, a major upgrade of the FoodMicrobionet database. International Journal of Food Microbiology, 305 (September 2019): 108249 DOI: 10.1016/j.ijfoodmicro.2019.108249.
Check it out, there are several important novelties. Don’t forget to read the supplementary material and to access the datasets and scripts on Mendeley datasets.
I am completely new at using GitHub and I am not sure I want to transform FoodMicrobionet in a collaborative project, but I have decided to create a public repository with FoodMicrobionet app and data on GitHub (well, the published versions). You can find it here: https://github.com/ep142/FoodMicrobionet.
FoodMicrobionet now includes 104 studies and 5335 samples
minor changes to lineages for some taxa
corrected a few inconsistencies in the study table
added food codes to study description for version 3.1
added surname and email field for the corresponding author of studies
corrected a mismatch in row numbers and sample ids for the sample table (and corrected edges). This was probably due to deletion of a sample from a previous version and was causing errors in selection with the Shiny app after sample with id 1572.
FMBN includes an impressive variety of food types, with data on both foods and food environments:
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 products 252
Grains and grain-based products 149
Major isolated ingredients, additives, flavours, baking and processing aids 72
Meat and meat products 1086
Milk and dairy products 2516
Vegetables and vegetable products 537
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 spices 65
Seasoning, sauces and condiments 18
This version will be not made public (but parts of it will be released as supplementary material for review articles). We are however willing to discuss collaboration opportunities with other research groups.
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)
Now ShinyFMBN (v2.1.2) can produce directly a number of useful exploratory graphs, including prevalence and abundance plots, bar plots of taxa abundance and box/violin plots for taxa abundance. All the graphs can be personalised.
I have found a small (but nefarious) error in the samples table which affects selection of samples in the app if samples with id>1472 are included. This apparently escaped previous testing. You can find the fix here (in the meantime I will upload an update in Mendeley data).
There are more than 4000 samples in version 3.2 of FoodMicrobionet.
More will be added until the end of September. After that, development will be stopped for lack of funding and time and the new version is scheduled to be released on February 14th, 2020, together with a revised app and a few service scripts.
Food type (L1)
Animal and vegetable fats and oils and primary derivatives thereof
Eggs and egg products
Fish, seafood, amphibians, reptiles and invertebrates
Fruit and fruit products
Grains and grain-based products
Major isolated ingredients, additives, flavours, baking and processing aids
I am working on version 3.2 of FoodMicrobionet (which will include 70+ studies and 3500+ samples) and version 2 of the Shiny app. However, these versions will not be made public anytime soon and will only be shared with partners with whom I have research collaborations. If you are interested in collaborating in the development of FoodMicrobionet or in the submissions of research projects related to FoodMicrobionet contact me.
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 (https://www.microbiomeanalyst.ca). 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.