@@ -1,6 +1,6 @@

 Package: CNEr 

-Version: 1.9.44

-Date: 2016-10-18

+Version: 1.11.2

+Date: 2016-10-19

 Title: CNE Detection and Visualization

 Description: Large-scale identification and advanced visualization 

              of sets of conserved noncoding elements.

@@ -94,9 +94,22 @@ makeGRBs <- function(x, winSize=NULL, genes=NULL, ratio=1,

   for(i in 1:length(xList)){

     hitsCNEs <- findOverlaps(xList[[i]], clusterRanges,

                              ignore.strand=TRUE, type="within")

+    

+    ## Add the number of CNEs

     cnes <- lengths(split(queryHits(hitsCNEs), subjectHits(hitsCNEs)))

     mcols(clusterRanges)[[names(xList)[i]]] <- 0L

     mcols(clusterRanges)[[names(xList)[i]]][as.integer(names(cnes))] <- cnes

+    

+    ## Add the CNE ranges

+    cnes <- split(xList[[i]][queryHits(hitsCNEs)], subjectHits(hitsCNEs))

+    missingCNEs <- setdiff(seq_len(length(clusterRanges)),

+                           subjectHits(hitsCNEs))

+    for(j in missingCNEs){

+      ## This is really a bad implementation. 

+      cnes[[as.character(j)]] <- GRanges()

+    }

+    mcols(clusterRanges)[[paste(names(xList)[i], "CNE")]] <- 

+      cnes[order(as.integer(names(cnes)))]

   }

   # Filter out the GRBs with few CNEs