Update README.md

README.md

-# CONCHA: Contact-based characterization of conformational ensembles of highly flexible proteins
+# WARIO: Weighted fAmilies of contact maps to chaRacterize conformational ensembles of (highly-)flexIble prOteins
 
-Welcome to CONCHA, an ensemble characterization tool. CONCHA represents an ensemble as a weighted family of contact maps. Contact is redefined by a continuous function taking values in $[0,1]$ that incorporates the relative orientation of the interacting residues as well as the sequence information. Then, the featured data is embedded into a 10-dimensional [UMAP](https://umap-learn.readthedocs.io/en/latest/index.html) space and clustered using the [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/#) algorithm. Finally, the average values of the contact function across each cluster conformation are represented as cluster-specific contact maps. The maps are assigned with a weight given by the cluster occupancy.
+Welcome to WARIO, an ensemble characterization tool. WARIO represents an ensemble as a weighted family of contact maps. Contact is redefined by a continuous function taking values in $[0,1]$ that incorporates the relative orientation of the interacting residues as well as the sequence information. Then, the featured data is embedded into a 10-dimensional [UMAP](https://umap-learn.readthedocs.io/en/latest/index.html) space and clustered using the [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/#) algorithm. Finally, the average values of the contact function across each cluster conformation are represented as cluster-specific contact maps. The maps are assigned with a weight given by the cluster occupancy.
 
-#### Running CONCHA
+#### Running WARIO
 
-To run CONCHA to characterize an ensemble, the user can directly execute the [contact_clustering](https://github.com/gonzalez-delgado/CONCHA/blob/main/concha/contact_clustering.ipynb) notebook, which contains the detailed pipeline and allows a step-by-step implementation of the tool.
+To run WARIO to characterize an ensemble, the user can directly execute the [contact_clustering](https://github.com/gonzalez-delgado/WARIO/blob/main/WARIO/contact_clustering.ipynb) notebook, which contains the detailed pipeline and allows a step-by-step implementation of the tool.
 
-## Installing CONCHA
+## Installing WARIO
 
-CONCHA and its required dependencies can be automatically installed if Python >=3.8 is available. We recommend to perform the installation inside a [Python virtual environment](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/). It can be created as follows
+WARIO and its required dependencies can be automatically installed if Python >=3.8 is available. We recommend to perform the installation inside a [Python virtual environment](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/). It can be created as follows
 ```
 python3 -m venv pythonEnv
 source pythonEnv/bin/activate
 ```
-Then, CONCHA is installed with
+Then, WARIO is installed with
 ```
-pip install git+https://gitlab.laas.fr/moma/CONCHA.git
+pip install git+https://gitlab.laas.fr/moma/WARIO.git
 ```
 Once the installation is completed, the command
 ```
-concha-notebooks
+wario-notebooks
 ```
 opens the ready-to-use jupyter notebook.
 
-The installation procedure works correctly with recent versions of Linux and MacOS operating systems. If you encounter any trouble to install CONCHA, please file an [issue](https://gitlab.laas.fr/moma/WASCO/-/issues) or [contact us](mailto:javier.gonzalez-delgado@math.univ-toulouse.fr).
+The installation procedure works correctly with recent versions of Linux and MacOS operating systems. If you encounter any trouble to install WARIO, please file an [issue](https://gitlab.laas.fr/moma/WARIO/-/issues) or [contact us](mailto:javier.gonzalez-delgado@math.univ-toulouse.fr).