@@ -73,8 +73,8 @@ By setting the clustering precision at the default level (i.e. minimum cluster s
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Each cluster can be complementary described by the average DSSP propensities of its conformations. The corresponding plots are available [here](https://gitlab.laas.fr/moma/methods/analysis/WARIO/-/tree/main/wario/demo).
Each cluster can be complementary described by the average DSSP propensities of its conformations. The corresponding plots for P113 are available [here](https://gitlab.laas.fr/moma/methods/analysis/WARIO/-/tree/main/wario/demo).
## Large ensembles
The current implementation of WARIO can be run in a normal desktop computer for ensembles with $nL\lessapprox 3\cdot 10^7$. Limitations are mainly due to memory constraints. We are currently working on a more efficient implementation for large ensembles, that can be run in remote servers for larger ensembles. Until it is released, we can [provide guidance](mailto:javier.gonzalezdelgado@mcgill.ca) on how to more efficiently run the current code in limiting settings $nL\approx 3-4\cdot 10^7$.
The current implementation of WARIO can be run in a normal desktop computer for ensembles with $nL\lessapprox 3\cdot 10^7$. Limitations are mainly due to memory constraints. We are currently working on a more efficient implementation for large ensembles, that can be run in remote servers for larger ensembles. Until it is released, we can [provide guidance](mailto:javier.gonzalezdelgado@mcgill.ca) on how to run the current code in limiting settings $nL\approx 3-4\cdot 10^7$ more efficiently.
