Nowadays, complex communities of microorganisms can be studied in depth with metagenomics and metatranscriptomics driven by evolution of sequencing techniques. Indeed, these meta’omics techniques offer insight concerning structure and functions of the studied communities. Notwithstanding, raw microbiota sequencing data are difficult to analyze due to their size and the numerous available tools [1,2] for analytical workflow. Indeed, sequences analysis requests successive bioinformatics tasks (e.g. quality control, sequence sorting, taxonomic analysis, functional analysis, statistical analysis). Choosing the best tools with correct parameter values and combining tools together in an analysis chain is a complex and error-prone process. Therefore, there is an urgent need for modular, accessible and sharable user-friendly tools. Several solutions are proposed in the form of bioinformatic pipelines to exploit metagenomic data. Tools such as QIIME [3], Mothur [4], MEGAN [5], IMG/M [6,7] or CloVR-metagenomics [8] are useful tools, but they do not provide a complete analytical workflow or are limited to amplicon sequences. Other interesting solutions such as SmashCommunity [9], RAMMCAP [10], MetAMOS [11] propose complete analysis pipelines. However, these command-line tools without proper graphical interface are not user-friendly solutions for researchers with low programming or informatics expertise. For their part, web-based services (MG-RAST [12,13], EBI metagenomics [14]) analyze microbiota data with automated pipelines working as a black box that also prevents the mastering and monitoring of the data process. Reproducibility can be questioned by this lack of transparency and flexibility. Alternative approaches to improve usability, transparency and modularity can be found in open-source workflow systems (Taverna [15], Wings [16], Galaxy [17,18] or Kepler [19]). They can be used to build a complete modular, accessible, sharable and user-friendly framework to analyze sequence data using chosen tools and parameters. To overcome these limitations, we have developed ASaiM, an open-source opinionated Galaxy-based framework. It is accessible via a web-based interface to ease microbiota sequence data analyses for biologists. With a custom collection of tools, workflows and databases, the framework is dedicated to microbiota sequence analyses (particularly extraction of taxonomic and metabolic information from raw sequences). By its intrinsic modularity, ASaiM allows adjustment of workflows, tools parameters and used databases for general or specific analyses and provides therefore a powerful framework to easily and rapidly analyze microbiota data in a reproducible and transparent environment.