Error quantifier module of MirDeep2

Hi there,
I am trying to run the second step of quantification (i.e. quantifier) following the mapper module of mirDeep2. In the mapping step everything is fine and I get the collapsed reads to be given to the quantifier module. Then, when trying to run the quantifier module, providing mature, precursor and star sequences, the run ends with an error which apparently lacks any suggestions on where the error is.

Here the error:

Fatal error: Exit code 1 ()
Tool generated the following standard error:

getting samples and corresponding read numbers

Converting input files
building bowtie index
mapping mature sequences against index
mapping read sequences against index
Mapping statistics

#desc total mapped unmapped %mapped %unmapped
total: 12382657 343630 12039027 0.028 0.972
seq: 12382657 343630 12039027 0.028 0.972
mapping star sequences against index
analyzing data
Expressed miRNAs are written to expression_analyses/expression_analyses_galaxy/miRNA_expressed.csv
not expressed miRNAs are written to expression_analyses/expression_analyses_galaxy/miRNA_not_expressed.csv

Creating miRBase.mrd file -q expression_analyses/expression_analyses_galaxy/miRBase.mrd -k dataset_21574970.dat -y galaxy -o -i expression_analyses/expression_analyses_galaxy/dataset_21574970.dat_mapped.arf -j expression_analyses/expression_analyses_galaxy/dataset_21574968.dat_mapped.arf -l -M miRNAs_expressed_all_samples_galaxy.csv
miRNAs_expressed_all_samples_galaxy.csv file with miRNA expression values
parsing miRBase.mrd file finished
creating PDF files

Any clues on what’s going on here? I see a lot of unmapped sequences but this is somewhat expected from a biological point of view as the miRNA fraction is not usually the most abundant in the tissue I am investigating (zebrafish gonads)


Found out, there were mismatches between fasta identifiers in the provided miRNAs (precursors, star and mature)

1 Like

Thanks for posting back @luca and very glad you found the problem :slight_smile:

FAQ for others that may run into the same root issue (across tools). Mismatched Chromosome identifiers (and how to avoid them)

There are many ways to compare data to ensure consistent identifier naming across inputs, but some hints to get started often help. Tools can error in various ways with mismatched inputs – or sometimes not error at all and just produce odd results.