https://arn.ugr.es/mirnaqc/check/T227DTB9FS2ZBU9
2020-03-13 14:24:42.345 Process starts
2020-03-13 14:24:42.352 Total number of jobs: 4
2020-03-13 14:24:42.352 We estimate 5 min runtime per job!
2020-03-13 14:24:42.372 Launch SRR1563062
2020-03-13 14:24:42.373 Launch SRR1563057
2020-03-13 14:24:42.374 Launch SRR1563059
2020-03-13 14:24:42.374 Launch SRR1563060
2020-03-13 14:31:32.722 SRR1563062 Finished with success!
2020-03-13 14:31:32.723 Finished 1 out of 4 jobs!
2020-03-13 14:36:51.107 SRR1563057 Finished with success!
2020-03-13 14:36:51.107 Finished 2 out of 4 jobs!
2020-03-13 14:41:05.482 SRR1563059 Finished with success!
2020-03-13 14:41:05.483 Finished 3 out of 4 jobs!
2020-03-13 14:42:25.98 SRR1563060 Finished with success!
2020-03-13 14:42:25.98 Finished 4 out of 4 jobs!
2020-03-13 14:42:26.087 Found 4 finished jobs
2020-03-13 14:42:26.587 Analysis finished
miRNA-seq quality control is frequently restricted to calculating PhredScores and filtering of low complexity reads since miRNA specific quality measures cannot be easily assessed.
mirnaQC provides several quality features that can help researchers identify issues in their samples. These features are provided as absolute values and ranked with a percentile calculated from a corpus of more than 36,000 samples.
Please note that mirnaQC was developed and tested using Google Chrome and Mozilla Firefox. Using other (older) browsers will likely result in some features not working properly. Please contact us to try to solve them.
mirnaQC was developed by the Computational Epigenomics Group at University of Granada
If you have any issues or doubts using mirnaQC please contact hackenberg at go.ugr.es
If you use and like mirnaQC consider other tools from our lab:
For each of the quality parameters calculated by mirnaQC you can display the raw value or the percentile of this value in the whole of Sequence Reads Archive.
To calculate these distributions, we analysed more than 36,000 miRNA-seq samples from 25 different species.
A: For some attributes, low values are good (adapter dimers or percentage of ribosomal RNA fragments) while for others higher numbers are better (microRNA yield, percentage of reads in the analysis, Phred Scores). For each attribute and sample we provide the percentile the value has in the background distribution (the reference corpus). Therefore, for adapter-dimers, lower quartile values are good, while for microRNA yield upper quartile values would be desired. However, in order to keep the color code coherent the ‘good quartile’ will always be displayed in green while ‘the bad quartile’ will appear in red.
In order to generate a unique colour scheme in the heatmap, the percentiles need to be transformed so that the lower quartile indicates good performance while the upper quartile indicates bad results (see also What are the good percentiles? above for more information).
A: mirnaQC was conceptually designed for Illumina (nucleotide space) data. That implies that the pipeline can process SOLID data but many attributes will be meaningless. This is because to analyse SOLID data, colour space reads are mapped to the genome first in order to perform the conversion to nucleotide space. This means that the percentage of mapped reads or even adapter dimers cannot be assessed. Other attributes like the number of miRNAs, percentage of short reads, library complexity can be correctly analysed and compared.
This tool will display scatter-plot of a PCA calculated from the expression values of your data. You can then explore the different Principal Components using the selectors.
You can also change which quality parameter is used to color samples. This may help you spot variables that are driving the expression in your samples.
You can mouse over any point in the plot to see a sample percentile and value. Group information is also available if provided and each group will be represented using different symbols (square, circle, cross, etc).