diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py
index c6ff5509..8a01a0ca 100644
--- a/PopPUNK/visualise.py
+++ b/PopPUNK/visualise.py
@@ -314,16 +314,17 @@ def generate_visualisations(query_db,
 
     #******************************#
     #*                            *#
-    #* Process dense or sparse    *#
-    #* distances                  *#
+    #* Determine type of distance *#
+    #* to use                     *#
     #*                            *#
     #******************************#
 
     # Determine whether to use sparse distances
     use_sparse = False
     use_dense = False
-
-    if (tree == "mst" or tree == "both") and rank_fit is not None:
+    if (tree == "nj" or tree == "both") or rank_fit == None:
+        use_dense = True
+    elif (tree == "mst" or tree == "both") and rank_fit is not None:
         # Set flag
         use_sparse = True
         # Read list of sequence names and sparse distance matrix
@@ -336,9 +337,91 @@ def generate_visualisations(query_db,
         elif previous_mst is not None:
             sys.stderr.write('The prefix of the distance files used to create the previous MST'
                              ' is needed to use the network')
-          
+
+    #**********************************#
+    #*                                *#
+    #* Process clustering information *#
+    #*                                *#
+    #**********************************#
+
+    # identify existing model and cluster files
+    if model_dir is not None:
+        model_prefix = model_dir
+    else:
+        model_prefix = ref_db
+    try:
+        model_file = os.path.join(model_prefix, os.path.basename(model_prefix))
+        model = loadClusterFit(model_file + '_fit.pkl',
+                               model_file + '_fit.npz')
+        model.set_threads(threads)
+    except FileNotFoundError:
+        sys.stderr.write('Unable to locate previous model fit in ' + model_prefix + '\n')
+        sys.exit(1)
+
+    # Either use strain definitions, lineage assignments or external clustering
+    isolateClustering = {}
+    # Use external clustering if specified
+    if external_clustering:
+        mode = 'external'
+        cluster_file = external_clustering
+        if cluster_file.endswith('_lineages.csv'):
+            suffix = "_lineages.csv"
+        else:
+            suffix = "_clusters.csv"
+    else:
+        # Load previous clusters
+        if previous_clustering is not None:
+            cluster_file = previous_clustering
+            mode = "clusters"
+            suffix = "_clusters.csv"
+            if cluster_file.endswith('_lineages.csv'):
+                mode = "lineages"
+                suffix = "_lineages.csv"
+        else:
+            # Identify type of clustering based on model
+            mode = "clusters"
+            suffix = "_clusters.csv"
+            if model.type == "lineage":
+                mode = "lineages"
+                suffix = "_lineages.csv"
+            cluster_file = os.path.join(model_prefix, os.path.basename(model_prefix) + suffix)
+
+    isolateClustering = readIsolateTypeFromCsv(cluster_file,
+                                               mode = mode,
+                                               return_dict = True)
+
+    # Add individual refinement clusters if they exist
+    if model.indiv_fitted:
+        for type, indiv_suffix in zip(['Core','Accessory'],['_core_clusters.csv','_accessory_clusters.csv']):
+            indiv_clustering = os.path.join(model_prefix, os.path.basename(model_prefix) + indiv_suffix)
+            if os.path.isfile(indiv_clustering):
+                indiv_isolateClustering = readIsolateTypeFromCsv(indiv_clustering,
+                                                                   mode = mode,
+                                                                   return_dict = True)
+                isolateClustering[type] = indiv_isolateClustering['Cluster']
+
+    # Join clusters with query clusters if required
+    if use_dense:
+        if query_db is not None:
+            if previous_query_clustering is not None:
+                prev_query_clustering = previous_query_clustering
+            else:
+                prev_query_clustering = os.path.join(query_db, os.path.basename(query_db) + suffix)
+
+            queryIsolateClustering = readIsolateTypeFromCsv(
+                    prev_query_clustering,
+                    mode = mode,
+                    return_dict = True)
+            isolateClustering = joinClusterDicts(isolateClustering, queryIsolateClustering)
+
+    #******************************#
+    #*                            *#
+    #* Process dense or sparse    *#
+    #* distances                  *#
+    #*                            *#
+    #******************************#
+
     if (tree == "nj" or tree == "both") or rank_fit == None:
-        use_dense = True
         
         # Either calculate or read distances
         if recalculate_distances:
@@ -456,81 +539,6 @@ def generate_visualisations(query_db,
                   core_distMat = core_distMat[np.ix_(row_slice, row_slice)]
                   acc_distMat = acc_distMat[np.ix_(row_slice, row_slice)]
 
-    #**********************************#
-    #*                                *#
-    #* Process clustering information *#
-    #*                                *#
-    #**********************************#
-
-    # identify existing model and cluster files
-    if model_dir is not None:
-        model_prefix = model_dir
-    else:
-        model_prefix = ref_db
-    try:
-        model_file = os.path.join(model_prefix, os.path.basename(model_prefix))
-        model = loadClusterFit(model_file + '_fit.pkl',
-                               model_file + '_fit.npz')
-        model.set_threads(threads)
-    except FileNotFoundError:
-        sys.stderr.write('Unable to locate previous model fit in ' + model_prefix + '\n')
-        sys.exit(1)
-
-    # Either use strain definitions, lineage assignments or external clustering
-    isolateClustering = {}
-    # Use external clustering if specified
-    if external_clustering:
-        mode = 'external'
-        cluster_file = external_clustering
-        if cluster_file.endswith('_lineages.csv'):
-            suffix = "_lineages.csv"
-        else:
-            suffix = "_clusters.csv"
-    else:
-        # Load previous clusters
-        if previous_clustering is not None:
-            cluster_file = previous_clustering
-            mode = "clusters"
-            suffix = "_clusters.csv"
-            if cluster_file.endswith('_lineages.csv'):
-                mode = "lineages"
-                suffix = "_lineages.csv"
-        else:
-            # Identify type of clustering based on model
-            mode = "clusters"
-            suffix = "_clusters.csv"
-            if model.type == "lineage":
-                mode = "lineages"
-                suffix = "_lineages.csv"
-            cluster_file = os.path.join(model_prefix, os.path.basename(model_prefix) + suffix)
-
-    isolateClustering = readIsolateTypeFromCsv(cluster_file,
-                                               mode = mode,
-                                               return_dict = True)
-
-    # Add individual refinement clusters if they exist
-    if model.indiv_fitted:
-        for type, indiv_suffix in zip(['Core','Accessory'],['_core_clusters.csv','_accessory_clusters.csv']):
-            indiv_clustering = os.path.join(model_prefix, os.path.basename(model_prefix) + indiv_suffix)
-            if os.path.isfile(indiv_clustering):
-                indiv_isolateClustering = readIsolateTypeFromCsv(indiv_clustering,
-                                                                   mode = mode,
-                                                                   return_dict = True)
-                isolateClustering[type] = indiv_isolateClustering['Cluster']
-
-    # Join clusters with query clusters if required
-    if use_dense:
-        if query_db is not None:
-            if previous_query_clustering is not None:
-                prev_query_clustering = previous_query_clustering
-            else:
-                prev_query_clustering = os.path.join(query_db, os.path.basename(query_db) + suffix)
-
-            queryIsolateClustering = readIsolateTypeFromCsv(
-                    prev_query_clustering,
-                    mode = mode,
-                    return_dict = True)
-            isolateClustering = joinClusterDicts(isolateClustering, queryIsolateClustering)
 
     #*******************#
     #*                 *#