diff --git a/README.md b/README.md
index 708b96765df981e9f8983a80d44ae02120f6edf0..7a3aede9108f6660722fe88fa5a013fbba1bbd5a 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,8 @@
 
 ## What is RNAflow ?
 
-RNAflow is a snakemake pipeline dedicated to standard transcriptomic analysis.
+RNAflow is a snakemake pipeline dedicated to standard transcriptomic analysis [@Hub of Bioinformatics & Biostatistics](https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/).
+
 Be careful, RNAflow was principally conceived to run on the HPC of Institut Pasteur, and not on other HPC.
 
 
@@ -49,19 +50,25 @@ If you simply want to use this workflow, download and extract the latest release
 
 *  Step 2: Configure workflow
 
-Configure the workflow according to your needs via editing the config.yaml and multiqc_config.yaml files in the config/ directory.
+Configure the workflow according to your needs via editing the [config.yaml](https://gitlab.pasteur.fr/hub/rnaflow/-/blob/master/config/config.yaml) and [multiqc_config.yaml](https://gitlab.pasteur.fr/hub/rnaflow/-/blob/master/config/multiqc_config.yaml) files in the `config/` directory.
+
+
+*  Step 3: Load your conda environment
+
+See [runme.sh](https://gitlab.pasteur.fr/hub/rnaflow/-/blob/master/runme.sh) to create the complete conda env.
 
+`conda activate snakemake`
 
-*  Step 3: Execute workflow
+*  Step 4: Execute workflow
 
 Test your configuration by performing a dry-run via
 
 `snakemake -n `
 
 
-run it in a HPC using environment modules via
+run it in a Maestro using environment modules via
 
-`snakemake -s Snakefile --use-envmodules --cluster-config config/cluster_config.json --cluster "sbatch --mem={cluster.ram} --cpus-per-task={threads} " -j 200 --nolock`
+`sbatch -q hubbioit -p hubbioit snakemake --cluster-config config/cluster_config.json --use-envmodules --cluster "sbatch --mem={cluster.ram} --cpus-per-task={threads} -q hubbioit -p hubbioit" -j 300 --nolock`
 
 
 Visualize how the rules are connected via