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. 2020 Aug 10;21(1):345.
doi: 10.1186/s12859-020-03666-4.

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

Stevenn Volant  1 , Pierre Lechat  1 , Perrine Woringer  1 , Laurence Motreff  2 , Pascal Campagne  1 , Christophe Malabat  1 , Sean Kennedy  2 , Amine Ghozlane  3   4
Affiliations

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

Stevenn Volant et al. BMC Bioinformatics. .

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.

Keywords: Differential analysis; Metagenomics; Visualization; Web application.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
SHAMAN workflow. SHAMAN can start from raw reads or from processed data. In this last case, it needs at least three tables, the count matrix, the annotation table and the metadata which can either be provided into three different files or by using the BIOM format. The user can then select the variables of interest and add some batch effects. Then, contrast vectors can easily be defined and interactive visualizations are available
Fig. 2
Fig. 2
Taxonomic composition of the Zymo mock community as analyzed in SHAMAN. Samples were processed in the lab under two different type of treatment: varying amount of DNA (0.5 and 1 ng) and varying the number of PCR cycles (25 and 30 cycles). a Barplot of proportion of taxa in the different conditions. b Heatmap of log2 fold changes obtained in the different contrasts. ** indicates 0.001 <p-value <0.01; *, 0.01 <p-value <0.05
Fig. 3
Fig. 3
Afribiota study of small intestine fluids and feces from stunt children compared to non stunt. a PCoA plot the Bray-Curtis dissimilarity index of the samples. Duodenal samples are colored in blue, light blue for Gastric and orange for Feces. PERMANOVA test based on the sample type yielded a p-value of 0.001. b Alpha diversity analysis of non-stunt (NN), moderately stunted (MCM) and severely stunted (MCS). Overlapping confidence interval indicates that the diversity are not different between NN, MCM and MCS in duodenal, gastric and feces samples. c Volcano plot of differentially abundant genera in the feces of stunt children compared to non-stunt. We plot the log2 fold change against the -log 10 adjusted p-value. Microbial taxa in red correspond to an increase of abundance and in blue to a decrease abundance. Labeled dots correspond to taxa from oropharyngeal core microbiota. d Log 2 abundance of differential abundant taxa from oropharyngeal core microbiota in stunt and non-stunt children feces
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