rANOMALY 
rANOMALY is an R Package integrating AmplicoN wOrkflow for Microbial community AnaLYsis. Here the F1000 reference paper and the poster presenting this workflow.
A shiny app ExploreMetabar is available to explore phyloseq object generated with rANOMALY or other tools, and allow users to perform statistical analysis in an user friendly interface (no command line).
Installation
You can install rANOMALY from this repository with following commands (tested on R 4.4.2):
Linux (highly recommended)
In bash terminal:
sudo apt-get install -y git libcurl4-openssl-dev libssl-dev libxml2-dev libgmp3-dev libmpfr-dev cmake zlib1g-dev libglpk40 libglpk-dev liblzma-dev libbz2-dev libfontconfig1-dev libfribidi-dev libharfbuzz-dev libfreetype6-dev libpng-dev libtiff5-dev libjpeg-dev pandoc-
cutadapt: rANOMALY allow the use of CUTADAPT to search and trim primers from raw sequences. You can find the simple installation step here.
Then, R console:
install.packages("devtools", "BiocManager")
options(repos = BiocManager::repositories()); devtools::install_git("https://forgemia.inra.fr/umrf/ranomaly")Windows
Require:
Then, R console:
install.packages("devtools", "BiocManager")
options(repos = BiocManager::repositories()); devtools::install_git("https://forgemia.inra.fr/umrf/ranomaly")Documentation and Tutorials
Visit the rANOMALY page
Ressources
IDTAXA formatted references databases for 16S and ITS are available here.
Citation
Sebastien Theil and Etienne Rifa. « RANOMALY: AmplicoN WOrkflow for Microbial Community AnaLYsis ». F1000Research 10 (07/01/2021): 7. https://doi.org/10.12688/f1000research.27268.1.
If you use rANOMALY, please cite following tools:
Callahan, Benjamin J., Paul J. McMurdie, Michael J. Rosen, Andrew W. Han, Amy Jo A. Johnson, et Susan P. Holmes. « DADA2: High-Resolution Sample Inference from Illumina Amplicon Data ». Nature Methods 13, nᵒ 7 (juillet 2016): 581‑83. https://doi.org/10.1038/nmeth.3869.
McMurdie, Paul J., et Susan Holmes. « Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data ». PLOS ONE 8, nᵒ 4 (22 avril 2013): e61217. https://doi.org/10.1371/journal.pone.0061217.
Murali, Adithya, Aniruddha Bhargava, et Erik S. Wright. « IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences ». Microbiome 6, nᵒ 1 (9 août 2018): 140. https://doi.org/10.1186/s40168-018-0521-5.
Licence
ETALAB
GPL 3.0
Copyright
2024 INRAE