SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

Abstract

Background Comparing the composition of microbial communities among groups of interest (e.g., patients vs healthy individuals) is a central aspect in microbiome research. It typically involves sequencing, data processing, statistical analysis and graphical display. Such an analysis is normally obtained by using a set of different applications that require specific expertise for installation, data processing and in some cases, programming skills. Results Here, we present SHAMAN, an interactive web application we developed in order to facilitate the use of (i) a bioinformatic workflow for metataxonomic analysis, (ii) a reliable statistical modelling and (iii) to provide the largest panel of interactive visualizations among the applications that are currently available. SHAMAN is specifically designed for non-expert users. A strong benefit is to use an integrated version of the different analytic steps underlying a proper metagenomic analysis. The application is freely accessible at http://shaman.pasteur.fr/ , and may also work as a standalone application with a Docker container (aghozlane/shaman), conda and R. The source code is written in R and is available at https://github.com/aghozlane/shaman . Using two different datasets (a mock community sequencing and a published 16S rRNA metagenomic data), we illustrate the strengths of SHAMAN in quickly performing a complete metataxonomic analysis. Conclusions With SHAMAN, we aim at providing the scientific community with a platform that simplifies reproducible quantitative analysis of metagenomic data.

Distinct systemic and mucosal immune responses during acute SARS-CoV-2 infection

“…We performed unbiased bacterial 16S rRNA sequencing to better characterize the commensal communities and potential pathobiont carriage in the nasopharynx of controls and patients with COVID-19 ( n = 42). V3–V4 region amplicons were sequenced and analyzed using SHAMAN 27 allowing for identification of 464 operational taxonomic units (OTUs). Genus-level analysis demonstrated significant ( P < 0.05) perturbations comparing healthy controls to patients with COVID-19 (Fig.…”

“…After removing reads containing incorrect primer or barcode sequences and sequences with more than one ambiguous base, we recovered a total of 8,075,384 reads (192,271 reads on average) from 42 samples. The bioinformatics analysis was performed into OTUs 27 or into ASVs 28 . Briefly, amplicons were clustered into OTUs with VSEARCH (v1.4) or ASV and aligned against the SILVA database.…”

Analysis of Microbiota and Mycobiota in Fungal Ball Rhinosinusitis: Specific Interaction between Aspergillus fumigatus and Haemophilus influenza?

“…Reads with ≥98.7 and ≥94.5% homology and 0.0 e-value were considered for identification at species and genus level, respectively [31]. Data analysis was further performed by SHAMAN (http://shaman.pasteur.fr, accessed on 7 July 2021) [32]. Rarefaction curves were computed to evaluate the quality of the taxonomic diversity assessment.…”

Characterization of biliary microbiota dysbiosis in extrahepatic cholangiocarcinoma

“…Values were considered statistically significant when p <0.05. PCoA, figures, and differential microbiota analysis were performed using the Shiny Application for Metagenomic Analysis (Shaman), from Institut Pasteur de Paris ( http://shaman.pasteur.fr/ ) [ 30 ]. For the differential abundance analysis, we used the BIOM Table and the “target” File that associates each sample with its explanatory variables (see suppl data: S1 File and S3 Table ).…”

“…We also included the interaction between diagnosis and associated diseases in the model. We used defaults parameters, such as the “weighted non-null normalization,” which was introduced by Volant et al [ 30 ] and accounts more accurately for matrix sparsity. SHAMAN/DESeq2 yields baseMean and FoldChange (and log2FoldChange) and an adjusted p-value.…”