diff --git a/README.md b/README.md
index 2391ca409e532a925ab6110c318af062c5bf2b8d..cfe401dd1de51deeaf4c9decd62fa3bb95a53fdb 100644
--- a/README.md
+++ b/README.md
@@ -47,19 +47,22 @@ Load and preprocess the datasets to extract the numeric data, with gene names as
 
 4. Reducing Noise Using Thresholds
     Apply a threshold to reduce weak correlations and focus on the stronger relationships.
-    ``` threshold_value <- 0.5
+    ``` 
+    threshold_value <- 0.5
         reduced_network_control <- applyThreshold(network_control, threshold_value)
         reduced_network_disease <- applyThreshold(network_disease, threshold_value) 
 
 5. Generating Edge Lists
     Generate edge lists to identify interactions between proteins, RNA, and phosphoproteins.
-    ``` edge_list_control <- generateEdgeList(reduced_network_control, gene_names)
-    ``` edge_list_disease <- generateEdgeList(reduced_network_disease, gene_names) 
+    ``` 
+        edge_list_control <- generateEdgeList(reduced_network_control, gene_names)
+        edge_list_disease <- generateEdgeList(reduced_network_disease, gene_names) 
 
 6. Using LIONESS for Sample-Specific Networks
 LIONESS is used to generate sample-specific networks for the data.
-    ``` sample_specific_control <- lioness(data_control[, control], cor)
-    ``` sample_specific_disease <- lioness(data_disease[, disease], cor)
+    ``` 
+        sample_specific_control <- lioness(data_control[, control], cor)
+        sample_specific_disease <- lioness(data_disease[, disease], cor)
 
 After generating the  correlation matrices for each sample using LIONESS, the next step is to integrate the DrDimont pipeline for which the layers must be created for each layer, which includes the identifiers and requires the annotations which is a required for the network generation.