--- a/blockclust.xml	Wed Jul 09 12:35:26 2014 -0400
+++ b/blockclust.xml	Wed Jul 09 13:01:56 2014 -0400
@@ -65,7 +65,7 @@
             cp #echo os.path.join($outputdir, 'hclust_tree.pdf')# $hclust_plot;
             cp #echo os.path.join($outputdir, 'discretized.gspan.tab')# $sim_tab_out
         #elif str($tool_mode.operation) == "post":
-            BlockClustPipeLine.pl -m POST -cbed $tool_mode.clusters_bed -cm $tool_mode.cmsearch_out -tab $tool_mode.sim_tab_in -o ./;
+            BlockClustPipeLine.pl -m POST -cbed $tool_mode.clusters_bed -cm $tool_mode.cmsearch_out -tab $tool_mode.sim_tab_in -rfam \$BLOCKCLUST_DATA_PATH/rfam_map.txt -o ./;
         #end if
     </command>
     <inputs>
@@ -167,7 +167,7 @@
 
 2) Clustering and classification - of given input blockgroups (output of blockbuster tool) as explained in the original paper.
 
-3) Post-processing - extracts distribution of clusters searched against Rfam database and plots hierarchical clustering made out of centroids of each BlockClust predicted cluster.
+3) Post-processing - plots for overview of predicted clusters.
 
 For a thorough analysis of your data, we suggest you to use complete blockclust workflow, which contains all three modes of operation.
 
@@ -199,7 +199,7 @@
     * BED file containing prediction of blockgroups by pre-compiled SVM binary classification model.
 
 3. Post-processing:
-    * Distribution of clusters with annotations searched against Rfam database
+    * Plot of distribution of ncRNA families per predicted cluster (overview of cluster precissions). The annotation of ncRNA families are retrieved by searching cluster instances against Rfam database.
     * Hierarchical clustering made out of centroids of each BlockClust predicted cluster
 
 ------