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SHAMAN is a shiny application for differential analysis of metagenomic data (16S, 18S, 23S, 28S, ITS and WGS) including bioinformatics treatment of raw reads for targeted metagenomics, statistical analysis and results visualization with a large variety of plots (barplot, boxplot, heatmap, …).
The bioinformatics treatment is based on Vsearch [Rognes 2016] which showed to be both accurate and fast [Wescott 2015] . The statistical analysis is based on DESeq2 R package [Anders and Huber 2010] which robustly identifies the differential abundant features as suggested in [McMurdie and Holmes 2014, Jonsson2016] . SHAMAN robustly identifies the differential abundant genera with the Generalized Linear Model implemented in DESeq2 [Love 2014] . SHAMAN is compatible with standard formats for metagenomic analysis (.csv, .tsv, .biom) and figures can be downloaded in several formats. A presentation about SHAMAN is available here and a poster here.
This website is free and open to all users and there is no login requirement. Hereafter is the global workflow of the SHAMAN application:
The main contributors to SHAMAN:
Stevenn Volant (Initiator, coding, testing, documentation, evaluation)
Amine Ghozlane (Coding, testing, documentation, evaluation, packaging)
Perrine Woringer (Coding, testing, documentation, feature suggestions)
Zakary Azmani (Coding, testing, documentation, feature suggestions)
Pierre Lechat (Coding, testing, feature suggestions)
Acknowledgements
Thanks to the following people for patches and other suggestions for improvements:
Valentin Marcon, Carine Rey, Hugo Varet, Emeline Perthame, Julien Tap, Anna Zhukova.
If you use SHAMAN for your project, please cite the following publication:
SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis Volant S, Lechat P, Woringer P, Azmani Z, Motreff L, Campagne P, Malabat C, Kennedy S, Ghozlane A; BMC Bioinformatics 2020 Aug 10;21(1):345.
Publication using SHAMAN:
Prediction of the intestinal resistome by a three-dimensional structure-based method. Ruppé E, Ghozlane A, Tap J, et al.;Nature microbiology 2018
Combined bacterial and fungal intestinal microbiota analyses: Impact of storage conditions and DNA extraction protocols Angebault C, Ghozlane A, Volant S, Botterel F, d’Enfert C, Bougnoux ME, PloS one 2018
Clinical Efficacy and Microbiome Changes Following Fecal Microbiota Transplantation in Children With Recurrent Clostridium Difficile Infection Li X, Gao X, Hu H, Xiao Y, Li D, Yu G, Yu D, Zhang T, Wang Y, Frontiers in Microbiology
Diverse laboratory colonies of Aedes aegypti harbor the same adult midgut bacterial microbiome Dickson LB, Ghozlane A, Volant S, Bouchier C, Ma L, Vega-Rúa A, Dusfour I, Jiolle D, Paupy C, Mayanja MN, Kohl A, Lutwama JJ, Duong V, Lambrechts L
Characteristics of Fecal Microbiota in Pediatric Crohn’s Disease and Their Dynamic Changes During Infliximab Therapy. Wang Y, Gao X, Ghozlane A, Hu H, Li X, Xiao Y, Li D, Yu G, Zhang T; Journal of Crohn's & colitis 2017
Carryover effects of larval exposure to different environmental bacteria drive adult trait variation in a mosquito vector. Dickson LB, Jiolle D, Minard G, Moltini-Conclois I, Volant S, Ghozlane A, Bouchier C, Ayala D, Paupy C, Moro CV, Lambrechts L; Science Advances 2017
A bacteriocin from epidemic Listeria strains alters the host intestinal microbiota to favor infection. Quereda JJ, Dussurget O, Nahori MA, Ghozlane A, Volant S, Dillies MA, Regnault B, Kennedy S, Mondot S, Villoing B, Cossart P, Pizarro-Cerda J.; PNAS 2016
Reporting bugs, ask for help
If you have any comments, questions or suggestions, or need help to use SHAMAN, please contact us at shaman@pasteur.fr and please provide us with enough information that we can recreate the problem. Useful things to include are:
- Input data (or examples, a small test case sufficient to recreate the problem)
- Information about which system your are using: web version, docker or R installation
What's new in SHAMAN
You can now see the composition of the communities detected in the Network.
December 6th 2023 - Database update
Greengenes2 and unite are up-to-date. New host genomes (mostly mosquitoes) are available.
November 23th 2023 - New data visualization and Global View features
UMAP has been added. We have also updated the Barplot to show other abundances for visualization of the 12 most abundant. For diversity, we have added a visualization of richness with comparison tests.
October 15th 2023 - Updates of PCA and PCoA
These updates bring new data visualizations. We have added some useful features for cluster visualization.
May 9th 2023 - Raw fastq submission is re-activated !
Shaman submission process is up again. We updated in the mean time the unite database to version 9.0.
April 12th 2022 - Improvement of the docker and web interface raw read submission
Raw read submission is now improved for gzip fastq files. We now recommend to send gzip compressed fastq especially for docker instance.
March 11th 2022 - Server maintenance on march 22th
Raw reads submission will be temporarly desactivated from March 22th. For now we keep improving our workflows, submissions from the docker machine should now be much more improved !
February 29th 2022 - Workflow updates
We updated the workflow with Alientrimmer 2 to improve speed and resilience. Let us know if you meet some trouble!
February 29th 2022 - Upgrades on 29 feb !
Raw reads submission will be temporarly desactivated 29 February to upgrade the bioinformatic workflow.
January 13th 2022 - Server maintenance is done
Raw reads submission is back.
January 7th 2022 - Server maintenance from 10-12 January 2021
Raw reads submission will be temporarly desactivated from January 10 until 12 due to a cluster maintenance.
May 5th 2021 - Server maintenance is done
Raw reads submission is back. We updated vsearch to 2.17, RDP classifier to 2.13, Bowtie to 2.3.5.1 and Biom to 2.1.10. Let us know if you meet any issue.
April 28th 2021 - Server maintenance from 3-6 Mai 2021
Raw reads submission will be temporarly desactivated from Mai 3 until 6 due to a cluster maintenance.
Feburary 19th 2020 - New feature
Shaman now support Epi2me data output. Let us know if you meet any issue.
August 19th 2020 - SHAMAN paper is out
We are really happy to announce that the paper describing in depth shaman and comparing this application to other available is out in BMC Bioinformatics. Moreover we keep improving the stability and functions of the application and we hope that we will be able to maintain this application for a long time.
August 14th 2019 - Major update
We performed a global improvement of SHAMAN. The application is now migrated to R 3.6.1. We implemented several visualization for differential analysis and network of abundance. We hope you will enjoy this new version.
April 11th 2019 - Debugging
We fixed few bugs in export system and scatterplot visualisation system.
March 28th 2019 - Packaging
SHAMAN is now available as packrat package. Take a look at download section.
April 17th 2018 - Bioinformatics
The bioinformatic treatment offers a larger access to parameters. We also worked a lot on the documentation.
September 4th 2017 - Bioinformatics
The bioinformatic treatment of fastq reads is now available in SHAMAN. For now, SHAMAN allows to compute OTU, build an OTU table and annotate them with the last version of the available database. This application is for 16S/18S/23S/28S/ITS sequencing.
July 18th 2017 - Normalization and visualisation
A new method for normalization called total counts was added. More options have been added to the abundance tree.
May 30th 2017 - Bug fixes
Some visualization bug with the abundance tree and phylogenetic tree are now fixed. The export of the relative abundance and normalised abundance are now given in the right level. This update prepares the field for the next major release of shaman for June.
March 30th 2017 - Krona, Phylogeny and bug fixes
Krona and phylogenetic tree plots are now available in visualisation. Several new distance are available in PCOA. The import float count matrices is now ok. We have finaly debugged the export of the relative abundance/normalized matrices.
Dec 9th 2016 - Phylogenetic tree and stress plot
You can now upload a phylogenetic tree to calculate the unifrac distance (only available at the OTU level). The stress plot has been added to evaluate the goodness of fit of the NMDS.
Nov 22th 2016 - New visualization and bug fix
We have implemented a new visualization called tree abundance. Some bugs have been fixed (thanks Carine Rey from ENS).
Oct 12th 2016 - Filtering step and bugs fix
You can now apply a filter on the features according to their abundance and the number of samples. Bugs on confidence intervals for the alpha diversity have been fixed.
Sep 21th 2016 - SHAMAN on docker
The install of SHAMAN is now available with docker. The R install is also updated and passed in release candidate state.
Sep 14th 2016 - Download and install SHAMAN
You can install SHAMAN (beta).
Sep 9th 2016 - PCA/PCOA
You can select the axes for the PCOA and PCA plots.
Aug 1st 2016 - Biom format
SHAMAN can now support all the Biom format versions.
Jun 24th 2016 - Comparisons plots
The venn diagram and the heatmap of foldchange have been added to compare the results of 2 or more contrasts.
Jun 17th 2016 - Diversity plots
Enhancement of the visualisation of the diverties. The shanon and inv. shanon have been added.
First check out our user guide:
You can test SHAMAN with the dataset from [Tap et al. 2015] , which is available here .
The zip archive contains 4 different files :
- the otu count matrix : Alimintest_otu_table.csv,
- the otu annotation table : Alimintest_otu_annotation.csv,
- the experimental design : Alimintest_target.csv,
- the contrast table : Alimintest_contrasts.csv.
Two groups of person follow two strict diet periods that involve the intake of 40g following 10g of fiber per day, or 10g of fiber after a 40g fiber intake period:
The 16S rRNA (V3 - V4 regions) from fece samples was sequenced at time stamp : 2, 3, 4 and 5.
The analysis will consider the different impact of the different fiber intake and the comparison to patient metabolic data.
The first step consists to load the count table and the annotation table as follow :
- Select 'Upload your data'
- Load the count table :
- Load the annotation table :
- When successfully loaded, the tables are accessible as bellow :
The second step consists to load the experimental design and the contrast table as follow :
- Select 'Run differential analysis'
- Load the target file :
- Identify the taxonomy level where the analysis will be performed :
- Identify the interactions :
- Run the analysis :
- When successfully loaded, the tables are accessible as bellow :
- Finally, load the contrast file :
- Contrasts can be visualized as follow :
'Diagnostic plots' section provides several visualizations to control the analysis
- Total mapped read count
- Nul barplot count
- Maj taxonomy count
- Density of counts
- Size factors vs total number of reads
- PCA
- PCOA
- Clustering
'Tables' section provides the results of the differential analysis.
For one given contrast, we have:
- The id of the given taxonomical level
- The base mean is the mean normalized count for the given annotation of all samples.
- The fold-change is a mesure describing how much the abundance varies from one condition to an other. For instance, if the abundance is 100 in condition 1 and 200 for condition 2, the fold-change equals 100/200=0.5.
- The log2 fold-change is the log2 value of the fold-change.
- The p-value adjusted (padj) is the pvalue obtained by the Wald test and adjusted by Benjamini & Hochberg procedure (BH) or Benjamini & Yekutieli procedure (see linear model options).
'Diagnostic plots' section provides several visualization to control the analysis
- Barplot
- Heatmap
- Boxplot
- Diversity
- Rarefaction
You can test SHAMAN with two other datasets :
- Bacteriocin impact on mice microbiota from
[Quereda et al. 2016]
:
here
The project is divided into three group : WT, Delta : Bacteriocin KO, Delta-complemented: Bacteriocin KO + complementation.
Three time points are considered :
- T0 : merged counts from -48H and -24H
- T2 : 6H after listeria infection
- T3 : 24H after listeria infection
Analysis with SHAMAN:
Set variables: condition, time, mice
Set interactions:
1. condition:mice
2. condition:time
- MOCK communities from [The NIH HMP Working Group, 2009] : here
Mock communities are composed of 21 species mixed in even or staggered proportions :
Analysis with SHAMAN:
Set variables : Community
no interaction
Docker install is the easiest way to use SHAMAN locally.
- On ubuntu (Linux), install docker (link here) , download/run shaman:
Then connect to http://localhost/ or http://0.0.0.0/ with your favorite web navigator.
Failed: port is already allocated ?
Connect to http://localhost:3838/ http://0.0.0.0:3838/.
- Install docker on Windows and Mac:
Download and install docker from https://www.docker.com/
- Search for aghozlane/SHAMAN and run:
- In advanced settings, configure host port, 5438 with 5438/tcp and 80 with 80/tcp and run:
- Connect to http://localhost/ or http://0.0.0.0/ with your favorite web navigator.
SHAMAN is available for R=3.6.1. Packrat framework allows an easy installation of all the dependencies. Of note, raw data submission is not possible with this version.
wget https://zenodo.org/record/7418309/files/shaman_package_202204.tar.gz
mkdir packrat/
Now you can run R:
devtools::install_github("rstudio/packrat")
packrat::unbundle("shaman_package_202204.tar.gz", "packrat/")
packrat::init("packrat/shaman")
system("Rscript -e 'shiny::runGitHub(\"pierreLec/KronaRShy\",port=5438)'",wait=FALSE)
shiny::runGitHub('aghozlane/shaman')
SHAMAN is available for R=3.6.1. Gfortran is required on mac (https://cran.r-project.org/bin/macosx/tools/) and Rtools on windows (https://cran.r-project.org/bin/windows/Rtools/index.html). Of note, raw data submission is not possible with this version. The installation, download and execution can all be performed with a small R script:
if(!require('shiny')){
install.packages('shiny')
}
system("Rscript -e 'shiny::runGitHub(\"pierreLec/KronaRShy\",port=5438)'",wait=FALSE)
# Install dependencies,
# download last version from github,
# and run SHAMAN in one command:
shiny::runGitHub('aghozlane/shaman')
This script can also be dowloaded here and executed as following :
Of note, contribution to SHAMAN code are always welcome and can be performed with the github deposit.
Conda installation of SHAMAN is available for linux and mac. It can be performed as follow:
conda install -c aghozlane shaman
# Now run shaman
shaman_conda.R
About
Read processing
OTU processing
OTU annotation
Directory containing the FastQ files
Match the paired files (only for paired-end sequencing)
Enter the key
Select your file format
Load the count table
Load the taxonomy file
Load the BIOM file
Load the Epi2me file
Load phylogenetic tree (optional)
Experimental design
Options
Size factors
Eigen values
Eigen values
Contribution
Stress plot
Distance values
Matrix coordinates
Select your contrast
Reorder data
Plot options
Choose colours
Down
Not significant
Up
Export
