Introduction: The recent breakthrough of meta-omics offers new perspectives to the field of anaerobic digestion (AD). It promises the deep understanding of microbial communities acting as catalysts and thereby the possibility to develop more cost-effective processes, through the design of operational biomarkers or through ecological engineering approaches. However, the variety of AD processes and operating conditions worldwide is very high. It is therefore difficult to extend the conclusions gained on one specific system. It highlights the need for data collection and organization at a large scale, to perform meta-analyses and to be able to identify robust trends. We present here two new software tools developed to favour data mining of meta-omics data from environmental biotechnologies. Methodology: DeepOmics is an n-tier web application: the user interface is a single page application built with the Angular framework. It accesses the data using a RESTful API. Data are stored in a PostgreSQL relational database as well as in an indexation and search engine (Elasticsearch, open-source version). Easy16S is an interactive R shiny interface based on two main R packages, shinydashboard and phyloseq. Easy16S is currently deployed on the INRAE-MIGALE server (https://migale.inrae.fr/). Results: DeepOmics offers the possibility to upload, request and export 16S metabarcoding data from several environmental biotechnologies together with many relevant associated metadata, especially regarding operating conditions and process design. It also enables the graphical monitoring of analytical data. Data collection is organized by research project: each project coordinator manages the rights associated to its project data (e.g. private, public). Many modules have been developed and are being tested by INRAE users and external partners. Our objective is to open a pilot version of DeepOmics to a wider community in the near future. Easy16S is a user-friendly web server tool intended for practitioners eager to explore their data and create figures rapidly and interactively. It is specifically focused on the mapping of covariates of interest. Easy16S accepts as entry the classical file formats associated to 16S metabarcoding. The R code lines corresponding to each plot can be copy-pasted from the webpage. Updated on a regular basis, Easy16S is available online (https://genome.jouy.inra.fr/shiny/easy16S/). To present these tools, we will rely on a practical example of AD inhibition data (Poirier and Chapleur, 2018). Conclusions: These complementary user-friendly tools should foster data mining of meta-omics data from AD processes, helping to take full advantage of next generation sequencing data and to turn knowledge into operational outputs.