I am not entirely sure about that but, since htseq-count allows to map the number of reads to each of the features described in the provided GTF we are faced with the same problem? I mean if I will use my predicted GTF rather than the reference GTF then again I will have the reported reads aligned to different XLOC_id attributes that may have assigned the same gene_name? Also, would you know whether the number of reads reported will be normalized for library size and gene length?
I'm going to examine the genes that had multiple XLOC attributed to them (as I said possibly due to different TSS) and if it turns out that there are only two XLOCs for all the genes considered and it looks like one is showing the predominant FPKM expression (other being negligible) then I think I can ignore the other. I just wasn't sure about the degree of FPKM variability and hence averaged the numbers but perhaps I should look more carefully at the data.
But I'm open to your suggestions.
Thank again
I could solve it by providing GTF file at the time of TopHat alignment. So Tophat alignment without GTF file produced two XLOC ids for the gene of my interest. On the other hand, the one with GTF file produced only one XLOC id.
GTF file was provided at the Cufflinks stage as well but providing it at TopHat stage made the difference.
If you are not willing to run the TopHat command again, you can look for the class_code of the XLOC ids and accept the XLOC ID with class_code such as "=" (and not "x"). That is select one instead of averaging out. Don't know how to filter XLOC ID by class_code.
Hope it helps.