From 19c98b7e4aa16f8431333a3a29ab949e7cda7217 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Javier=20Gonz=C3=A1lez-Delgado?= <jgonzalezd@laas.fr>
Date: Fri, 26 Apr 2024 16:24:59 +0000
Subject: [PATCH] Update README.md

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 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -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.
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