Dear Julien, Dear Mar and people interested in DEXSeq , You recently reported some problems in DEXSeq that had to do with the way the HTSeq python scripts deal with the exons that overlap with more than one gene ID. The solution that we had taken so far was that the gene IDs sharing an exon were merged into an "aggregate gene" ID. From the input of some users and our own experience, we know that it was not the most appropriate solution: when the merged genes were differentially expressed, DEXSeq falsely calls differential usage in other exons of the aggregate genes. We have included a "-r" parameter in the script "prepare_annotation_dexseq.py", for the user to decide what to do with these exons: either to ignore the exons associated with more than one gene IDs and treat each gene separately, or to merge the genes and take these exons into account. Additionally, we have implemented the R/Bioconductor functions equivalent to the python scripts. These functions were implemented using code contributed by Mike Love. All these changes are available in the last svn version (1.5.9). Best regards, Alejandro Reyes Hi Alejandro, Just to let you know that adding the junctions to the test of differential expression of DEXSeq worked fine! The "hack" was actually straightforward, I just had to modify the counts files taken as input. On a different note, I noticed that many false positives were generated because of "aggregate" gene models that were composed on different overlapping genes. When these overlapping genes have different behavior in different conditions, this is interpreted as differential expression of some exons, while it is differential expression of genes... See the attached picture, this might turn out to be easier to understand Did you notice this behavior of DEXSeq, and do you have any comment on this? Thanks again for your work on DEXSeq Julien