--- a/blockclust.xml	Tue Jul 08 13:18:16 2014 -0400
+++ b/blockclust.xml	Wed Jul 09 08:38:01 2014 -0400
@@ -161,9 +161,12 @@
 fast graph-kernel techniques. BlockClust allows both clustering and 
 classification of small non-coding RNAs.
 
-BlockClust runs in three modes: 
+BlockClust runs in three operating modes: 
+
 1) Pre-processing - converts given mapped reads (BAM) into BED file of tags
-2) Clustering and classification - of given input block groups (from blockbuster tool) as explained in the original paper.
+
+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.
 
 For a thorough analysis of your data, we suggest you to use complete blockclust workflow, which contains all three modes of operation.
@@ -171,31 +174,33 @@
 **Inputs**
 
 BlockClust input files are dependent on the mode of operation:
-1) Pre-processing mode:
-Binary Sequence Alignment Map (BAM) file
+
+1. Pre-processing mode:
+    * Binary Sequence Alignment Map (BAM) file
 
-2) Clustering and classification:
-A blockgroups file generated by blockbuster tool
-Select reference genome
+2. Clustering and classification:
+    * A blockgroups file generated by blockbuster tool
+    * Select reference genome
 
-3) Post-processing:
-Output of cmsearch, searched clusters generated by BlockClust against Rfam
-BED file containing clusters generated by BlockClust
-Pairwise similarities of blockgroups generated by BlockClust
+3. Post-processing:
+    * Output of cmsearch, searched clusters generated by BlockClust against Rfam
+    * BED file containing clusters generated by BlockClust
+    * Pairwise similarities of blockgroups generated by BlockClust
 
-**Output**
-1) Pre-processing mode:
-BED file of tags with expressions
+**Outputs**
+
+1. Pre-processing mode:
+    * BED file of tags with expressions
 
-2) Clustering and classification:
-Hierarchical clustering plot of all input blockgroups by their similarity
-Pairwise similarities of all input blockgroups
-BED file containing predicted clusters
-BED file containing prediction of blockgroups by pre-compiled SVM binary classification model.
+2. Clustering and classification:
+    * Hierarchical clustering plot of all input blockgroups by their similarity
+    * Pairwise similarities of all input blockgroups
+    * BED file containing predicted clusters
+    * 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
-hierarchical clustering made out of centroids of each BlockClust predicted cluster
+3. Post-processing:
+    * Distribution of clusters with annotations searched against Rfam database
+    * Hierarchical clustering made out of centroids of each BlockClust predicted cluster
 
 ------