Hi, I have mirdeep2 output that looks like this.

```
#miRNA read_count precursor total seq seq(norm)
mmu-let-7a-5p 43271 mmu-let-7a-1 43271 43271 7658.26
mmu-let-7a-1-3p 784 mmu-let-7a-1 784 784 138.76
mmu-let-7a-5p 43224 mmu-let-7a-2 43224 43224 7649.94
```

I think using the seq(norm) would be appropriate, but I cannot find how the sequences have been normalised. I think it is something like TPM but am not sure. Does anyone know?

Best, Krutik

I would like to do a standard differential expression analysis.

Then I would simple make a count matrix, genes being rows, samples being columns, with the raw counts (read_count) the values, and feed it into e.g. DESeq2. This tool needs raw counts.

Hell ATpoint,

I found your replied is very helpful, thanks. I have another question, what's the criteria to select true-positive miRNAs from all predicted miRNAs? As I understand, it requests significant randfold p-value labeled as yes, high miRDEEP2 score. Do you know a more clear criteria? Thanks. Xu

Hi, unfortunately miRNAs are not my expertise, so I cannot comment here. I would suggest you open a new question with all the necessary details, so it gets more attention.

Thanks for your suggestion, just post one.