Merge branch 'helene.rimbert-master-patch-73205' into 'master'

Update README.md

See merge request !2

README.md

@@ -10,17 +10,28 @@ $ git clone git@forgemia.inra.fr:umr-gdec/magatt.git
 
 ## Dependancies
 
-### Snakemake
-* Version 5.5.2
+### Build magatt environment with conda
 
-### Python3
+We recommend to build the environment using conda (developped with miniconda 3, conda 4.9.2 ) with the file [environment.yml](environment.yml):  
+
+```console
+$ conda env create -f=environment.yml -n magatt
+```
+
+Once created, you can  activate the environment with:
+
+```console
+$ conda activate magatt
+```
+
+All the dependancies are listed below.  
+
+* Snakemake : 5.5.2
 * Python: 3.5
 * Biopython: 1.68
 * numpy: 1.15
 * pandas: 0.23
 * pysam: 0.15
-  
-### Genomic Tools
 * Bedtools: 2.27
 * Blat: 36
 * Exonerate (fastavalidcds): 2.4.0
@@ -30,9 +41,9 @@ $ git clone git@forgemia.inra.fr:umr-gdec/magatt.git
 * NCBI-blast (BLAST+): 2.6
 * Samtools: 1.9
 
-## Prepare the pipeline
+## Prepare and run the pipeline
 
-### Creating the configuration file
+### Creating the configuration file: inputs and other parameters
 
 The configuration file [config.yaml](config.yaml) will contain all the input files required for the pipeline and some other parameters.
 
@@ -170,6 +181,11 @@ $ snakemake -j 32 --cluster sbatch
 
 This will allow to have at most 32 subproccess run through the SLURM scheduler with `sbatch`.
 
+You can use a custom [cluster.json](cluster.json) JSON file do setup the parameters of SBATCH for each rules, and use it with with:    
+```console
+$ snakemake -j 32 -u cluster.json --cluster "sbatch -J {cluster.jobName} -c {cluster.c} --mem {cluster.mem} -e {cluster.error} -o {cluster.output} -p debug" --verbose"
+```
+
 You can generate the diagram of all the processes and dependancies of you analysis:
 ```bash
 $ snakemake --dag |dot -T png > dag.png