IMNGS is a platform that uniformly and systematically screens for, retrieves, processes, and analyses all available prokaryotic 16S rRNA gene amplicon datasets from public repositories and uses them to build sample-specific sequence databases and OTU-based profiles. The retrieved information can be used to address questions of relevance in microbial ecology, for example with respect to the occurrence of specific microorganisms in different ecosystems or to perform targeted diversity studies. IMNGS also offers a complete pipeline for de novo analysis of a user's own raw amplicon sequencing data generated using the Illumina technology. Due to the personalised nature of many functions and the high computational cost related to the performed tasks, only registered users can submit jobs (registration can be done via the IMNGS home page; regsiter there as a Guest to learn more about what it can do). The IMNGS web interface can then be used to: search and select datasets/samples (runs), create and launch jobs, and inspect the outputs of the jobs. Developed by: Ilias Lagkouvardos, Technical university of Munich (TUM) Used data resources: SRA repository amplicon studies, user query 16S sequence, user raw amplicon sequences Used in tools: USEARCH Technology or platform: Django, Python, PostgreSQL

Rhea: R scripts for the analysis of microbial profiles The importance of 16S rRNA gene amplicon profiles for understanding the influence of microbes in a variety of environments coupled with the steep reduction in sequencing costs led to a surge of microbial sequencing projects. Among available pipeline options for high-throughput 16S rRNA gene analysis, the R programming language and software environment for statistical computing stands out for its power and increased flexibility, and the possibility to adhere to most recent best practices and to adjust to individual project needs. The Rhea pipeline is a set of R scripts that encode a series of well-documented choices for the downstream analysis of Operational Taxonomic Units (OTUs) tables, including normalisation steps, alpha- and beta-diversity analysis, taxonomic composition, statistical comparisons, and calculation of correlations. Rhea is primarily a straightforward starting point for beginners, but can also be used as a framework for advanced users who can modify and expand the tool. Rhea is composed of 6 steps that can be run independently or as a set. Normalization Alpha-Diversity Beta-Diversity Taxonomic-Binning Serial-Group-Comparisons Correlations

This service aims to compute a list of taxa IDs detected from metabarcoding sequences of environmental DNA (eDNA) samples. This service contains eight substeps (1.1 to 1.8) implemented as a unique step. The eight substeps perform (1) sequencing error correction (using BayesHammer-SPAdes); (2) pairwise alignment, (3) pre-filtering, (4) dereplication, (5) attribute filtering, (6) clustering and OTU tab-producer (using OBITools); (7) taxonomic assignment (using blastn); (8) OTUs table generator. Several types of eDNA samples can be processed (i.e. water, feces, soil). It represents the Step 1 of the Metabarcoding Workflow within the Internal Joint Initiative.