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Commit ed3b29ba authored by Javier González-Delgado's avatar Javier González-Delgado
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Update README.md

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...@@ -38,7 +38,7 @@ wario-notebooks ...@@ -38,7 +38,7 @@ wario-notebooks
``` ```
opens the ready-to-use jupyter notebook. opens the ready-to-use jupyter notebook.
The installation procedure works correctly with recent versions of Linux (Ubuntu 20.04 and 22.04) and MacOS (Sonoma 14.4.1) operating systems. Typical install time on a normal desktop computer is around 5 minutes. If you encounter any trouble to install WARIO, please file an [issue](https://gitlab.laas.fr/moma/methods/analysis/WARIO/-/issues) or [contact us](mailto:javier.gonzalez-delgado@math.univ-toulouse.fr). The installation procedure works correctly with recent versions of Linux (Ubuntu 20.04 and 22.04) and MacOS (Sonoma 14.4.1) operating systems. Typical install time on a normal desktop computer is around 5 minutes. If you encounter any trouble to install WARIO, please file an [issue](https://gitlab.laas.fr/moma/methods/analysis/WARIO/-/issues) or [contact us](mailto:javier.gonzalezdelgado@mcgill.ca).
## DEMO (data example) ## DEMO (data example)
...@@ -75,3 +75,6 @@ By setting the clustering precision at the default level (i.e. minimum cluster s ...@@ -75,3 +75,6 @@ By setting the clustering precision at the default level (i.e. minimum cluster s
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 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 $nL\approx 3-4\cdot 10^7$.
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