
I have downloaded the miRNA Isoform expression quantification data (`mirbase21.isoforms.quantification.txt`) from TCGA. The data looks like below:

    miRNA_ID	read_count	miRNA_region
    hsa-let-7b	     2	     precursor
    hsa-let-7b	     1	     precursor
    hsa-let-7b	     1       precursor
    hsa-let-7b	     58	    MIMAT0000063
    hsa-let-7b	    173	    MIMAT0000063
    hsa-let-7b	    5723	MIMAT0000063
    hsa-let-7b	   26947	MIMAT0000063
    hsa-let-7b	     1	      stemloop
    hsa-let-7b	     1	    MIMAT0004482
    hsa-let-7b	     2	    MIMAT0004482
    hsa-let-7b	    129	    MIMAT0004482
    hsa-let-7b	    401	    MIMAT0004482

And based on `miRBaseConverter` and information from `miRTarbase` I have the mature regions 3p and 5p information with Accession like below:

    miRNAName_v21	  Accession
    hsa-let-7b-5p	 MIMAT0000063
    hsa-let-7b-3p	 MIMAT0004482

So, based on above information I can sum the counts of mature 3p and 5p. And the main precursor also I can do, but what are stemloops? Do I need to include that with precursor or need to exclude that from analysis?

thanq
The the maturing of a miRNA is a multi-step process. The extra material at the 3' and 5' end is removed first, and then the loop, which acording to the TCGA pipeline description defined for the purposes of this pipeline as:

> 4. stemloop, from 1 to 6 bases outside the mature strand, between the mature and star strand

In the TCGA pipeline, reads are classified heirarchically.
Priority is given to reads mapping to mature miRNA, so if it aligns to something else as well as mature, it will be counted as mature (that actaully wouldn't be the way I would do, but never mind). Then to reads mapping to the pre-cursor, and then to the stemloop. 

This explains the high levels of counts in the mature, but not the pre-cursor, despite the mature being contained within the precursor. 

As you are probably intersted in the biologically active molecule, I would ignore everything other than the mature. 