I have answered similar question seqanswers: http://seqanswers.com/forums/showthread.php?t=28976&highlight=miRNA
Mirna Mirdeep2 : Mirnas_Expressed_All_Samples.Csv Or Result.Csv Which One To Consider?
I have a basic question: From the mirdeep2 outputs which one we need to consider for expression comparisions across groups (tumor vs. normal): (Or how researchers report?).
in the below example: mmu-let-7c-5p have different precursors...How to tackle this; I appreciate your inputs.
And also how does programs like "Deseq" handle differences in the initial number of reads for samples.
miRNAs_expressed_all_samples.csv
mmu-let-7c-5p 1452 mmu-let-7c-1 1452 1452 1452.00
mmu-let-7c-5p 1452 mmu-let-7c-2 1452 1452 1452.00
or result.csv.
chr16_6774 5.3 85 +/- 20% - 729 729 0 0 yes mmu-let-7c-5p aae-let-7 - - ugagguaguagguuguaugguu cuguacaaccuucuagcuuucc ugagguaguagguuguaugguuuagaguuacacccugggaguuaacuguacaaccuucuagcuuucc chr16:77599897..77600004:+
chr15_6481 0.3 12 +/- 12% - 733 733 0 0 no mmu-let-7c-5p aae-let-7 - - ugagguaguagguuguaugguu ccacaacauccagcucuacg ccacaacauccagcucuacgccaagacugacugacggccuuuggggugagguaguagguuguaugguu chr15:85536976..85537086:+
1 answer
I got the answer from the mirdeep2 Authors:
"I would use the counts from the file miRNAs_expressed_all_samles.csv. These counts are based on the annotated mature sequence plus some small flanking sequence while the result.csv uses the estimated mature sequence by miRDeep2.
Regarding the normalization I think taking RPKM is a good idea and something similar is done in the qunatifier output. Since I am not an expert in differential expression analysis I can only recommend to you to use some package that implements these tests for example the R package DeSeq."
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