BIOCOM-PIPE: a new user-friendly metabarcoding pipeline for the characterization of microbial diversity from 16S, 18S and 23S rRNA gene amplicons Summary: This Zenodo repository encompasses the demo input files and expected output files in the pipeline such that before directly applying it on large-scale datasets, a user can be assured that he/she has successfully implemented the pipeline. More precisely, we chose to use a recent dataset published (Sadet et al., 2018 – Applied Soil Ecology – DOI: 10.1016/j.apsoil.2018.02.006), with raw datasets areavailable in the EBI database system under project accession number PRJEB14258. We hope that these files will help users to efficiently test and checked the BIOCOM-PIPE pipeline. The deposited archive includes : - the Input.txt file with chosen parameters, - the project.csv file describing the composition of the library, - .fastq files from the EBI database system under project accession number PRJEB14258, - the expected result files and summary files after BIOCOM-PIPE analysis. Background: The ability to compare samples or studies easily using metabarcoding so as to better interpret microbial ecology results is an upcoming challenge. There exists a growing number of metabarcoding pipelines, each with its own benefits and limitations. However, very few have been developed to offer the opportunity to characterize various microbial communities (e.g., archaea, bacteria, fungi, photosynthetic microeukaryotes) with the same tool. Results: BIOCOM-PIPE is a flexible and independent suite of tools for processing data from high-throughput sequencing technologies, Roche 454 and Illumina platforms, and focused on the diversity of archaeal, bacterial, fungal, and photosynthetic microeukaryote amplicons. Various original methods were implemented in BIOCOM-PIPE to (i) remove chimeras based on read abundance, (ii) align sequences with structure-based alignments of RNA homologs using covariance models or a post-clustering tool (ReClustOR), and (iii) re-assign OTUs based on a reference OTU database. The comparison with two other pipelines (FROGS and mothur) highlighted that BIOCOM-PIPE was better at discriminating land use groups. Conclusions: The BIOCOM-PIPE pipeline makes it possible to analyze 16S/18S and 23S rRNA genes in the same package tool. This innovative approach defines a biological database from previously analyzed samples and performs post-clustering of reads with this reference database by using open-reference clustering. This makes it easier to compare projects from various sequencing runs. For advanced users, the pipeline was developed to allow for adding or modifying the components, the databases and the bioinformatics tools easily.