home.md

@@ -3,3 +3,121 @@
 You can browse the different steps using the sidebar on the right.
 
 
+# Installation
+
+## Prerequisites
+
+* linux system
+* [python3](https://www.python.org/)
+* [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/)
+
+## Installation via Conda
+
+The default conda solver is a bit slow and sometimes has issues with selecting the latest package releases. Therefore, we recommend to install Mamba as a drop-in replacement via
+```
+conda install -c conda-forge mamba
+```
+
+Then, you can install Snakemake, pandas, biopython and dependencies with
+```
+mamba create -n snakemake_rapidrun -c conda-forge -c bioconda snakemake biopython pandas
+```
+
+from the conda-forge and bioconda channels. This will install all required software into an isolated software environment, that has to be activated with
+```
+conda activate snakemake_rapidrun
+```
+
+
+# Get started
+
+* open a shell
+* clone the project and switch to the main folder, it's your working directory
+
+```
+git clone https://gitlab.mbb.univ-montp2.fr/edna/snakemake_rapidrun_swarm
+cd snakemake_rapidrun_swarm
+```
+
+* Activate the conda environment to access the required dependencies
+
+```
+conda activate snakemake_rapidrun
+```
+
+You are ready to run the analysis !
+
+
+## Download data
+
+
+The complete data set can be downloaded and stored into [resources/tutorial](https://gitlab.mbb.univ-montp2.fr/edna/snakemake_rapidrun_obitools/-/tree/master/resources/tutorial) folder with the following command:
+
+```
+wget -c https://gitlab.mbb.univ-montp2.fr/edna/tutorial_metabarcoding_data/-/raw/master/tutorial_rapidrun_data.tar.gz -O - | tar -xz -C ./resources/tutorial/
+```
+* Data is downloaded at [resources/tutorial](https://gitlab.mbb.univ-montp2.fr/edna/snakemake_rapidrun_swarm/-/tree/master/resources/tutorial)
+* This is a tiny subset of a real metabarcoding analysis in rapidrun format
+
+
+# Run the workflow
+
+Simply type the following command to process data (estimated time: 25 minutes)
+
+```
+bash main.sh config/config_tutorial.yaml 8
+```
+
+
+* This will generate OTU occ:urences tables into [results/06_assignment/04_table](https://gitlab.mbb.univ-montp2.fr/edna/snakemake_rapidrun_swarm/-/tree/master/results/06_assignment/04_table)
+* The first argument [config/config_tutorial.yaml](https://gitlab.mbb.univ-montp2.fr/edna/snakemake_rapidrun_swarm/-/blob/master/config/config_tutorial.yaml) contains mandatory parameters information
+* The second argument **8** is the number of CPU cores you want to allow the system uses to run the whole workflow
+
+# To go further
+
+Please check the [wiki](https://gitlab.mbb.univ-montp2.fr/edna/snakemake_rapidrun_swarm/-/wikis/home).
+
+
+# Cluster MBB
+
+
+
+
+
+clone the workflow
+
+```
+git clone https://gitlab.mbb.univ-montp2.fr/edna/snakemake_rapidrun_swarm.git
+cd snakemake_rapidrun_obitools
+```
+
+Download data test (resources/test)
+
+```
+scp -r resources/test/ peguerin@162.38.181.66:~/snakemake_rapidrun_swarm/resources
+```
+
+Download singularity image (sedna.simg)
+
+```
+scp -r sedna.simg peguerin@162.38.181.66:~/snakemake_rapidrun_swarm/
+```
+
+load singularity module
+
+```
+module load singularity/3.5.3 
+```
+
+run the workflow local
+
+```
+singularity exec sedna.simg snakemake --use-conda --configfile config/config_test_rapidrun.yaml -j 4
+```
+
+run the workflow sge
+
+
+```
+singularity exec sedna.simg snakemake --use-conda --cluster '/opt/gridengine/bin/linux-x64/qsub -q cemeb20.q -N testsmk -j y -pe robin 16' --configfile config/config_test_rapidrun.yaml -j 16
+```