Thanks a lot sudbery. So, now I will sum all the mature 3p and also sum the 5p and exclude precursor and stemloop for the analysis.
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
1 answer
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:
- 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.
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