Integrating metagenetic datasets through microbial association networks to compare microbial communities from lacto-fermented vegetables Acknowledgements References ➢ The comparison of microbial ASV association networks is valuable for exploring bacterial community successions and guiding the exploration of potential microbial consortia responsible for different fermentation phases in lacto-fermented vegetables. ➢ This work validates the recurring pattern of the Enterobacterales presence at the beginning of fermentation, thus raising questions about their ecological role. ➢ However, the biological interpretation of this work is limited by its scale and the metabarcoding technology. ➢ Ultimately, this study highlights the potential of re-using data and the need for complementary functional studies (shotgun metagenomics, metatranscriptomics) with sampling over time to improve our understanding of vegetable fermentation. Conclusion Method The development of low-cost sequencing technologies has generated a massive amount of microbiome datasets during the last 20 years [5]. They represent an unprecedented opportunity to address new questions and better understand microbial ecosystems. Nevertheless, it also raises many challenges related to data reusability, comparison and integration. This work explores and compares public metabarcoding datasets related to lacto-fermented vegetables using an original meta-analysis approach. There is currently renewed interest in the bacterial communities of fermented vegetables [4], as plant-based fermented foods are at the heart of sustainability and health issues. Lacto-fermented vegetables are the product of a relatively well-described spontaneous lactic acid fermentation by lactic acid bacteria (LAB). However, community succession has received little attention in lacto-fermented vegetables. This study presents an integrative bioinformatics approach to explore and compare public amplicon datasets through a meta-analysis. The workflow includes searching and selecting public time-series datasets and constructing Amplicon Sequence Variants (ASV) association networks based on co-abundance metrics. Microbial communities detection is achieved by comparison and clustering of ASV networks. We applied the workflow to ten public datasets and demonstrated its value in monitoring precisely the fermentation with the identification of the bacterial communities succession and of putative core-consortia shared by different plant fermentation types.