I am working with a dataset containing 50 libraries of small RNAs. I am interested in all kinds of small RNAs (miRNA, tRNA fragments, piRNAs, etc.). I use an in-house script to obtain a matrix of counts: number of counts of each sequence for each sample. On this matrix I apply a low count filtering, so that I am left with a matrix of approximately 50K rows.
For me, the next logical step is to use this matrix as input to DESeq2 to obtain which sequences are differentially expressed between the experimental groups. However, I have some doubts about whether DESeq2 is suitable for this kind of data, as I don't know to what extent it is distributed in the same way as gene expression data. Could someone with experience with this kind of analysis tell me if this pipeline is correct? I have seen articles using this methodology, but I have not found direct answers from the package developer indicating that DESeq2 can be used with small-RNAseq data.