readme update

README.md

 # snakemake_rapidrun_swarm
 
-OTU clustering with SWARM on RAPIDRUN data encapsulated in SNAKEMAKE
+OTU clustering based on [TARA Fred's metabarcoding pipeline](https://github.com/frederic-mahe/swarm/wiki/Fred%27s-metabarcoding-pipeline) applied on RAPIDRUN data managed with [SNAKEMAKE](https://snakemake.readthedocs.io/en/stable/)
 
+# Installation
 
-# Prerequisites
+## Prerequisites
 
+* linux system
+* [python3](https://www.python.org/)
+* [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) or [pip3](https://pip.pypa.io/en/stable/)
 
-* python3
-
-* snakemake
-
-* singularity
-
-python3 dependencies
+## 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
 ```
-pip3 install pandas
-pip3 install biopython
+conda install -c conda-forge mamba
 ```
 
-python3 dependencies to run `snakemake`:
-
+Then, you can install Snakemake, pandas, biopython and dependencies with
 ```
-pip3 install datrie
-pip3 install ConfigArgParse
-pip3 install appdirs
-pip3 install gitdb2
+mamba create -n snakemake_rapidrun -c conda-forge -c bioconda snakemake biopython pandas
 ```
 
-# Configuration / input files
-
-You have to set 2 files:
-
-* [01_infos/all_samples.tsv](01_infos/all_samples.tsv)
-* [01_infos/config.yaml](01_infos/config_test.yaml)
-
-
-# Run the workflow
-
+from the conda-forge and bioconda channels. This will install all required software into an isolated software environment, that has to be activated with
 ```
-CORES=32
-CONFIGFILE="01_infos/config_test.yaml"
-bash main.sh $CORES $CONFIGFILE
+conda activate snakemake_rapidrun
 ```
 
 
-# Run from scratch
-
+# Get started
 
-## clone repositories
+* open a shell
+* clone the project and switch to the main folder, it's your working directory
 
 ```
-git clone git@gitlab.mbb.univ-montp2.fr:edna/snakemake_rapidrun_swarm.git
+git clone https://gitlab.mbb.univ-montp2.fr/edna/snakemake_rapidrun_swarm
 cd snakemake_rapidrun_swarm
 ```
 
-## write demultiplexing table
-
-From
-- `01_infos/config_test.yaml` 
-	* `fichiers:` `rapidrun`
-	* `fichiers:` `dat`
-	* `blacklist:`
-
-* :warning: the colon "marker" into `fichiers:` `rapidrun` must be the same name as marker's keys of `fichiers:` `dat` into `01_infos/config_test.yaml` 
-
-* This will generate a file `01_infos/all_demultiplex.csv` with each line arguments for each command to run in parallel.
-
-* `blacklist:` `projet:` contains a list of projects you don't want to proceed 
-
-
-* `blacklist:` `run:` contains a list of runs you don't want to proceed 
-
-
-
-```
-snakemake --configfile 01_infos/config_test.yaml -s readwrite_rapidrun_demultiplexing.py
-```
-
-
-## merge fastq
-
-From
-- `01_infos/config_test.yaml` 
-	* `fichiers:` `rapidrun`
-	* `blacklist:`
-
-* Deduce the `{run}` fastq paired-end to merge
-
-* `blacklist:` `projet:` contains a list of projects you don't want to proceed 
-
-
-* `blacklist:` `run:` contains a list of runs you don't want to proceed 
-
-
-
-```
-cd 02_assembly
-snakemake --configfile $CONFIGFILE -s Snakefile -j $CORES --use-singularity --singularity-args "--bind /media/superdisk:/media/superdisk" --latency-wait 20
-cd ..
-```
-
-## demultiplexing
-
-From
-- `01_infos/config_test.yaml` 
-	* `fichiers:` `rapidrun`
-
-* will generate demultiplexed .fasta for each `{projet}`/`{marker}`/`{sample}` into `03_demultiplexing`
-
+* Activate the conda environment to access the required dependencies
 
 ```
-cd 03_demultiplexing
-snakemake --configfile $CONFIGFILE -s Snakefile -j $CORES --use-singularity --singularity-args "--bind /media/superdisk:/media/superdisk" --latency-wait 20
-cd ..
+conda activate snakemake_rapidrun
 ```
 
-## cat qualities
-
-From
-- `01_infos/config_test.yaml` 
-	* `fichiers:` `rapidrun`
+You are ready to run the analysis !
 
-* will concatenate and format .qual files by `{projet}`/`{marker}`
-
-```
-cd 04_cat_quality
-snakemake --configfile $CONFIGFILE -s Snakefile -j $CORES --use-singularity --singularity-args "--bind /media/superdisk:/media/superdisk" --latency-wait 20
-cd ..
-```
 
-## clustering
+## Download data
 
-From
-- `01_infos/config_test.yaml` 
-	* `fichiers:` `rapidrun`
-	* `clustering:` `swarm:` `cores`
 
-* will cluster sequences by Molecular Operational Taxonomic Unit (MOTU)
+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:
 
 ```
-cd 05_clustering
-snakemake --configfile $CONFIGFILE -s Snakefile -j $CORES --use-singularity --singularity-args "--bind /media/superdisk:/media/superdisk" --latency-wait 20
-cd ..
-
+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/
 ```
 
+# Run the workflow
 
-
-## assignment
-
-From
-- `01_infos/config_test.yaml` 
-	* `fichiers:` `rapidrun`
-    * `assignment:` `ecotag:` `minIdentity`
-
-* will assign each MOTU to a species
+Simply type the following command to process data (estimated time: 20 minutes)
 
 ```
-cd 06_assignment
-snakemake --configfile $CONFIGFILE -s Snakefile -j $CORES --use-singularity --singularity-args "--bind /media/superdisk:/media/superdisk" --latency-wait 20
-cd ..
-
+bash main.sh
 ```
 
+# To go further
 
+Please check the [wiki](https://gitlab.mbb.univ-montp2.fr/edna/snakemake_rapidrun_swarm/-/wikis/home).